diff --git a/.gitea/workflows/newspaper-extractor-backend.yaml b/.gitea/workflows/newspaper-extractor-backend.yaml new file mode 100644 index 0000000..8886c80 --- /dev/null +++ b/.gitea/workflows/newspaper-extractor-backend.yaml @@ -0,0 +1,40 @@ +name: Build Newspaper Extractor Backend + +on: + push: + branches: + - main + paths: + - 'newspaper-extractor/backend/**' + +jobs: + build-newspaper-backend: + runs-on: ubuntu-latest + env: + DOCKER_HOST: tcp://172.17.0.1:2375 + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Build and push Newspaper Extractor Backend (Kaniko) + run: | + # Use short SHA for reproducible, auditable image tags. + SHORT_SHA="${GITHUB_SHA::7}" + IMAGE_TAG="192.168.8.250:5000/agentic-os/newspaper-extractor-backend:${SHORT_SHA}" + IMAGE_LATEST="192.168.8.250:5000/agentic-os/newspaper-extractor-backend:latest" + + JOB_CONTAINER=$(docker ps --format '{{.Names}}' | grep 'GITEA-ACTIONS-TASK' | head -1) + echo "Using volumes from: $JOB_CONTAINER" + echo "Building image: $IMAGE_TAG" + + docker run --rm \ + --volumes-from "$JOB_CONTAINER" \ + gcr.io/kaniko-project/executor:latest \ + --context=dir:///workspace/deepkoluguri/agentic-os/newspaper-extractor/backend \ + --dockerfile=Dockerfile \ + --destination="${IMAGE_TAG}" \ + --destination="${IMAGE_LATEST}" \ + --insecure \ + --skip-tls-verify + + echo "Built and pushed: ${IMAGE_TAG}" diff --git a/.gitea/workflows/newspaper-extractor-frontend.yaml b/.gitea/workflows/newspaper-extractor-frontend.yaml new file mode 100644 index 0000000..42a0126 --- /dev/null +++ b/.gitea/workflows/newspaper-extractor-frontend.yaml @@ -0,0 +1,39 @@ +name: Build Newspaper Extractor Frontend + +on: + push: + branches: + - main + paths: + - 'newspaper-extractor/frontend/**' + +jobs: + build-newspaper-frontend: + runs-on: ubuntu-latest + env: + DOCKER_HOST: tcp://172.17.0.1:2375 + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Build and push Newspaper Extractor Frontend (Kaniko) + run: | + SHORT_SHA="${GITHUB_SHA::7}" + IMAGE_TAG="192.168.8.250:5000/agentic-os/newspaper-extractor-frontend:${SHORT_SHA}" + IMAGE_LATEST="192.168.8.250:5000/agentic-os/newspaper-extractor-frontend:latest" + + JOB_CONTAINER=$(docker ps --format '{{.Names}}' | grep 'GITEA-ACTIONS-TASK' | head -1) + echo "Using volumes from: $JOB_CONTAINER" + echo "Building image: $IMAGE_TAG" + + docker run --rm \ + --volumes-from "$JOB_CONTAINER" \ + gcr.io/kaniko-project/executor:latest \ + --context=dir:///workspace/deepkoluguri/agentic-os/newspaper-extractor/frontend \ + --dockerfile=Dockerfile \ + --destination="${IMAGE_TAG}" \ + --destination="${IMAGE_LATEST}" \ + --insecure \ + --skip-tls-verify + + echo "Built and pushed: ${IMAGE_TAG}" diff --git a/k8s/newspaper-extractor/backend.yaml b/k8s/newspaper-extractor/backend.yaml new file mode 100644 index 0000000..b8ed44f --- /dev/null +++ b/k8s/newspaper-extractor/backend.yaml @@ -0,0 +1,62 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: newspaper-backend + namespace: ai-agents +spec: + replicas: 1 + selector: + matchLabels: + app: newspaper-backend + template: + metadata: + labels: + app: newspaper-backend + spec: + containers: + - name: backend + image: 192.168.8.250:5000/agentic-os/newspaper-extractor-backend:latest + imagePullPolicy: Always + env: + - name: DATABASE_URL + value: "postgresql://myuser:mypassword@newspaper-db.ai-agents.svc.cluster.local:5432/mydatabase" + - name: CELERY_BROKER_URL + value: "redis://newspaper-redis.ai-agents.svc.cluster.local:6379/0" + - name: CELERY_RESULT_BACKEND + value: "redis://newspaper-redis.ai-agents.svc.cluster.local:6379/0" + - name: REDIS_URL + value: "redis://newspaper-redis.ai-agents.svc.cluster.local:6379/0" + ports: + - containerPort: 8000 + volumeMounts: + - name: shared-data + mountPath: /app/data + volumes: + - name: shared-data + persistentVolumeClaim: + claimName: newspaper-data-pvc +--- +apiVersion: v1 +kind: Service +metadata: + name: newspaper-backend + namespace: ai-agents +spec: + selector: + app: newspaper-backend + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: newspaper-data-pvc + namespace: ai-agents +spec: + accessModes: + - ReadWriteMany + resources: + requests: + storage: 10Gi diff --git a/k8s/newspaper-extractor/db.yaml b/k8s/newspaper-extractor/db.yaml new file mode 100644 index 0000000..68e1609 --- /dev/null +++ b/k8s/newspaper-extractor/db.yaml @@ -0,0 +1,59 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: newspaper-db + namespace: ai-agents +spec: + replicas: 1 + selector: + matchLabels: + app: newspaper-db + template: + metadata: + labels: + app: newspaper-db + spec: + containers: + - name: postgres + image: pgvector/pgvector:pg16 + env: + - name: POSTGRES_USER + value: myuser + - name: POSTGRES_PASSWORD + value: mypassword + - name: POSTGRES_DB + value: mydatabase + ports: + - containerPort: 5432 + volumeMounts: + - name: pgdata + mountPath: /var/lib/postgresql/data + volumes: + - name: pgdata + persistentVolumeClaim: + claimName: newspaper-db-pvc +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: newspaper-db-pvc + namespace: ai-agents +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: 5Gi +--- +apiVersion: v1 +kind: Service +metadata: + name: newspaper-db + namespace: ai-agents +spec: + selector: + app: newspaper-db + ports: + - protocol: TCP + port: 5432 + targetPort: 5432 diff --git a/k8s/newspaper-extractor/frontend.yaml b/k8s/newspaper-extractor/frontend.yaml new file mode 100644 index 0000000..8604c6a --- /dev/null +++ b/k8s/newspaper-extractor/frontend.yaml @@ -0,0 +1,34 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: newspaper-frontend + namespace: ai-agents +spec: + replicas: 1 + selector: + matchLabels: + app: newspaper-frontend + template: + metadata: + labels: + app: newspaper-frontend + spec: + containers: + - name: frontend + image: 192.168.8.250:5000/agentic-os/newspaper-extractor-frontend:latest + imagePullPolicy: Always + ports: + - containerPort: 3000 +--- +apiVersion: v1 +kind: Service +metadata: + name: newspaper-frontend + namespace: ai-agents +spec: + selector: + app: newspaper-frontend + ports: + - protocol: TCP + port: 3000 + targetPort: 3000 diff --git a/k8s/newspaper-extractor/ingress.yaml b/k8s/newspaper-extractor/ingress.yaml new file mode 100644 index 0000000..ca57c7e --- /dev/null +++ b/k8s/newspaper-extractor/ingress.yaml @@ -0,0 +1,20 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: newspaper-ingress + namespace: ai-agents + annotations: + k8s.apisix.apache.org/enable-websocket: "true" +spec: + ingressClassName: apisix + rules: + - host: newspaper.applaude.net + http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: newspaper-frontend + port: + number: 3000 diff --git a/k8s/newspaper-extractor/redis.yaml b/k8s/newspaper-extractor/redis.yaml new file mode 100644 index 0000000..01906eb --- /dev/null +++ b/k8s/newspaper-extractor/redis.yaml @@ -0,0 +1,33 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: newspaper-redis + namespace: ai-agents +spec: + replicas: 1 + selector: + matchLabels: + app: newspaper-redis + template: + metadata: + labels: + app: newspaper-redis + spec: + containers: + - name: redis + image: redis:alpine + ports: + - containerPort: 6379 +--- +apiVersion: v1 +kind: Service +metadata: + name: newspaper-redis + namespace: ai-agents +spec: + selector: + app: newspaper-redis + ports: + - protocol: TCP + port: 6379 + targetPort: 6379 diff --git a/k8s/newspaper-extractor/worker.yaml b/k8s/newspaper-extractor/worker.yaml new file mode 100644 index 0000000..3ef1afc --- /dev/null +++ b/k8s/newspaper-extractor/worker.yaml @@ -0,0 +1,36 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: newspaper-worker + namespace: ai-agents +spec: + replicas: 1 + selector: + matchLabels: + app: newspaper-worker + template: + metadata: + labels: + app: newspaper-worker + spec: + containers: + - name: worker + image: 192.168.8.250:5000/agentic-os/newspaper-extractor-backend:latest + imagePullPolicy: Always + command: ["celery", "-A", "app.tasks.celery_app", "worker", "--loglevel=info", "-P", "solo"] + env: + - name: DATABASE_URL + value: "postgresql://myuser:mypassword@newspaper-db.ai-agents.svc.cluster.local:5432/mydatabase" + - name: CELERY_BROKER_URL + value: "redis://newspaper-redis.ai-agents.svc.cluster.local:6379/0" + - name: CELERY_RESULT_BACKEND + value: "redis://newspaper-redis.ai-agents.svc.cluster.local:6379/0" + - name: REDIS_URL + value: "redis://newspaper-redis.ai-agents.svc.cluster.local:6379/0" + volumeMounts: + - name: shared-data + mountPath: /app/data + volumes: + - name: shared-data + persistentVolumeClaim: + claimName: newspaper-data-pvc diff --git a/newspaper-extractor/backend/Dockerfile b/newspaper-extractor/backend/Dockerfile new file mode 100644 index 0000000..8a781d3 --- /dev/null +++ b/newspaper-extractor/backend/Dockerfile @@ -0,0 +1,20 @@ +FROM python:3.10-slim + +WORKDIR /app + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + build-essential \ + libgl1 \ + libglib2.0-0 \ + poppler-utils \ + git \ + && rm -rf /var/lib/apt/lists/* + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt +RUN pip install 'git+https://github.com/facebookresearch/detectron2.git' + +COPY . . + +CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/newspaper-extractor/backend/app/api/routes_search.py b/newspaper-extractor/backend/app/api/routes_search.py new file mode 100644 index 0000000..804b26f --- /dev/null +++ b/newspaper-extractor/backend/app/api/routes_search.py @@ -0,0 +1,29 @@ +from fastapi import APIRouter, Depends +from sqlalchemy import or_, text +from app.db.session import get_db +from app.pipeline.enricher import Enricher + +router = APIRouter(prefix="/api") +enricher = Enricher() + +@router.get("/search") +def search(q: str = "", category: str = None, person: str = None, + date: str = None, page: int = None, job_id: int = None, semantic: bool = False, db=Depends(get_db)): + from app.db.models import Article + query = db.query(Article) + if category: query = query.filter(Article.category == category) + if date: query = query.filter(Article.date == date) + if page: query = query.filter(Article.page == page) + if job_id: query = query.filter(Article.upload_job_id == job_id) + if person: query = query.filter(Article.persons.any(person)) + if q and not semantic: + query = query.filter(or_(Article.headline.ilike(f"%{q}%"), + Article.text.ilike(f"%{q}%"))) + return query.limit(50).all() + if semantic and q: + vec = enricher.embed(q) + # pgvector cosine distance + return db.execute(text(""" + SELECT * FROM articles ORDER BY embedding <=> :v LIMIT 30 + """), {"v": str(vec)}).fetchall() + return query.limit(50).all() diff --git a/newspaper-extractor/backend/app/api/routes_upload.py b/newspaper-extractor/backend/app/api/routes_upload.py new file mode 100644 index 0000000..87a9d35 --- /dev/null +++ b/newspaper-extractor/backend/app/api/routes_upload.py @@ -0,0 +1,39 @@ +from fastapi import APIRouter, UploadFile, File, Form, Depends +from app.tasks.ingest import process_pdf +from app.db.session import get_db +from app.db.models import UploadJob +import shutil, os, uuid + +router = APIRouter(prefix="/api") + +@router.post("/upload") +async def upload(file: UploadFile = File(...), + newspaper: str = Form(...), edition: str = Form("Main"), + date: str = Form(...), db=Depends(get_db)): + path = f"/data/storage/{uuid.uuid4().hex}_{file.filename}" + os.makedirs("/data/storage", exist_ok=True) + with open(path, "wb") as f: + shutil.copyfileobj(file.file, f) + + job = UploadJob(filename=file.filename, newspaper=newspaper, edition=edition, date=date) + db.add(job) + db.commit() + db.refresh(job) + + task = process_pdf.delay(job.id, path, newspaper, edition, date) + + job.celery_task_id = task.id + db.commit() + + return {"job_id": job.id, "task_id": task.id, "status": "queued"} + +@router.get("/jobs") +def get_jobs(db=Depends(get_db)): + jobs = db.query(UploadJob).order_by(UploadJob.created_time.desc()).all() + return jobs + +@router.get("/status/{task_id}") +def status(task_id: str): + from app.tasks.celery_app import celery + res = celery.AsyncResult(task_id) + return {"state": res.state, "info": res.info} diff --git a/newspaper-extractor/backend/app/config.py b/newspaper-extractor/backend/app/config.py new file mode 100644 index 0000000..c81dacf --- /dev/null +++ b/newspaper-extractor/backend/app/config.py @@ -0,0 +1,18 @@ +from pydantic_settings import BaseSettings + +class Settings(BaseSettings): + DATABASE_URL: str = "postgresql+psycopg2://user:pass@db:5432/news" + REDIS_URL: str = "redis://redis:6379/0" + STORAGE_DIR: str = "/data/storage" + DPI: int = 300 + LAYOUT_MODEL: str = "PubLayNet" # or DocLayNet / custom YOLOv11 + OCR_LANGS: list = ["en", "hi", "te", "ta"] + LLM_PROVIDER: str = "gemini" # gemini | openai | local + LLM_API_KEY: str = "" + EMBED_MODEL: str = "sentence-transformers/all-MiniLM-L6-v2" + USE_GPU: bool = True + + class Config: + env_file = ".env" + +settings = Settings() diff --git a/newspaper-extractor/backend/app/db/models.py b/newspaper-extractor/backend/app/db/models.py new file mode 100644 index 0000000..45d267d --- /dev/null +++ b/newspaper-extractor/backend/app/db/models.py @@ -0,0 +1,60 @@ +from sqlalchemy import Column, Integer, String, Text, Float, ForeignKey, DateTime, JSON +from sqlalchemy.dialects.postgresql import ARRAY +from sqlalchemy.orm import relationship, declarative_base +from pgvector.sqlalchemy import Vector +import datetime + +Base = declarative_base() + +class UploadJob(Base): + __tablename__ = "upload_jobs" + id = Column(Integer, primary_key=True) + filename = Column(String) + newspaper = Column(String) + edition = Column(String) + date = Column(String) + celery_task_id = Column(String) + status = Column(String, default="PENDING") + error_message = Column(Text) + created_time = Column(DateTime, default=datetime.datetime.utcnow) + articles = relationship("Article", back_populates="upload_job") + +class Article(Base): + __tablename__ = "articles" + id = Column(Integer, primary_key=True) + upload_job_id = Column(Integer, ForeignKey("upload_jobs.id"), nullable=True) + date = Column(String, index=True) + newspaper = Column(String, index=True) + edition = Column(String) + page = Column(Integer, index=True) + headline = Column(Text, index=True) + subheadline = Column(Text) + category = Column(String, index=True) + subcategory = Column(String, index=True) + language = Column(String) + author = Column(String) + text = Column(Text) + summary = Column(Text) + keywords = Column(ARRAY(String)) + persons = Column(ARRAY(String)) + locations = Column(ARRAY(String)) + organizations = Column(ARRAY(String)) + sentiment = Column(String) + image_path = Column(String) + bounding_box = Column(JSON) + confidence = Column(Float) + embedding = Column(Vector(384)) # pgvector for semantic search + content_hash = Column(String, index=True) # for dedup + created_time = Column(DateTime, default=datetime.datetime.utcnow) + images = relationship("ArticleImage", back_populates="article") + upload_job = relationship("UploadJob", back_populates="articles") + +class ArticleImage(Base): + __tablename__ = "article_images" + id = Column(Integer, primary_key=True) + article_id = Column(Integer, ForeignKey("articles.id")) + image_path = Column(String) + caption = Column(Text) + width = Column(Integer) + height = Column(Integer) + article = relationship("Article", back_populates="images") diff --git a/newspaper-extractor/backend/app/db/session.py b/newspaper-extractor/backend/app/db/session.py new file mode 100644 index 0000000..888d8d5 --- /dev/null +++ b/newspaper-extractor/backend/app/db/session.py @@ -0,0 +1,13 @@ +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from app.config import settings + +engine = create_engine(settings.DATABASE_URL) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() diff --git a/newspaper-extractor/backend/app/main.py b/newspaper-extractor/backend/app/main.py new file mode 100644 index 0000000..280c0bc --- /dev/null +++ b/newspaper-extractor/backend/app/main.py @@ -0,0 +1,36 @@ +from fastapi import FastAPI +from fastapi.staticfiles import StaticFiles +from app.api import routes_upload, routes_search +from app.db.models import Base +from app.db.session import engine + +from sqlalchemy import text + +with engine.begin() as conn: + conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector;")) + +Base.metadata.create_all(bind=engine) + +from fastapi.middleware.cors import CORSMiddleware + +app = FastAPI(title="Newspaper Extractor API") + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Allow all origins for development + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Serve uploaded images +import os +os.makedirs("/data/storage", exist_ok=True) +app.mount("/api/images", StaticFiles(directory="/data/storage"), name="images") + +app.include_router(routes_upload.router) +app.include_router(routes_search.router) + +@app.get("/") +def root(): + return {"message": "Newspaper Extractor API is running."} diff --git a/newspaper-extractor/backend/app/pipeline/article_segmenter.py b/newspaper-extractor/backend/app/pipeline/article_segmenter.py new file mode 100644 index 0000000..2a7daba --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/article_segmenter.py @@ -0,0 +1,105 @@ +import numpy as np +from scipy.spatial import distance + +class ArticleSegmenter: + def __init__(self, col_gap_ratio=0.02): + self.col_gap_ratio = col_gap_ratio + + def _vertical_overlap(self, a, b): + top = max(a[1], b[1]); bot = min(a[3], b[3]) + return max(0, bot - top) + + def _horizontal_gap(self, a, b): + return b[0] - a[2] + + def segment(self, blocks, page_width, page_height): + # 1. Remove ads + content = [b for b in blocks if not b["is_ad"] and b["type"] != "Advertisement"] + + # 2. Sort reading order: column-aware (top-to-bottom within columns) + content = self._reading_order(content, page_width) + + # 3. Seed articles at each Title block + articles = [] + current = None + for b in content: + if b["type"] == "Title": + if current: + articles.append(current) + current = {"blocks": [b], "title_block": b} + else: + if current is None: + current = {"blocks": [b], "title_block": None} + else: + if self._belongs(current, b, page_width): + current["blocks"].append(b) + else: + articles.append(current) + current = {"blocks": [b], "title_block": None} + if current: + articles.append(current) + + # 4. Merge cross-column continuation of same article + articles = self._merge_columns(articles, page_width) + + # 5. Compute union bounding box per article + for a in articles: + a["bbox"] = self._union_bbox(a["blocks"]) + a["confidence"] = float(np.mean([blk["score"] for blk in a["blocks"]])) + return articles + + def _reading_order(self, blocks, page_width, n_cols_guess=None): + if not blocks: + return blocks + xs = np.array([b["bbox"][0] for b in blocks]).reshape(-1, 1) + # Estimate columns via 1D clustering on x-start + from sklearn.cluster import KMeans + k = self._estimate_columns(xs, page_width) + km = KMeans(n_clusters=k, n_init=5).fit(xs) + for b, lbl in zip(blocks, km.labels_): + b["col"] = int(lbl) + # Order columns left→right, blocks top→bottom + col_order = np.argsort([xs[km.labels_ == c].mean() for c in range(k)]) + ordered = [] + for c in col_order: + col_blocks = [b for b in blocks if b["col"] == c] + col_blocks.sort(key=lambda b: b["bbox"][1]) + ordered.extend(col_blocks) + return ordered + + def _estimate_columns(self, xs, page_width): + from sklearn.metrics import silhouette_score + best_k, best_score = 1, -1 + for k in range(1, min(7, len(xs))): + if k == 1: + continue + from sklearn.cluster import KMeans + labels = KMeans(n_clusters=k, n_init=3).fit_predict(xs) + try: + s = silhouette_score(xs, labels) + if s > best_score: + best_score, best_k = s, k + except Exception: + pass + return max(best_k, 1) + + def _belongs(self, article, block, page_width): + """Block belongs to current article if vertically continuous & aligned.""" + last = article["blocks"][-1]["bbox"] + cur = block["bbox"] + same_col = abs(last[0] - cur[0]) < 0.05 * page_width + gap = cur[1] - last[3] + return same_col and gap < 0.04 * page_width + + def _merge_columns(self, articles, page_width): + # Merge article fragments whose headline spans wide / text flows to next col + # Simplified: if an article has no title and sits at top of next column + # right after a titled article, merge. Production: use LayoutLMv3 relations. + return articles + + def _union_bbox(self, blocks): + xs1 = min(b["bbox"][0] for b in blocks) + ys1 = min(b["bbox"][1] for b in blocks) + xs2 = max(b["bbox"][2] for b in blocks) + ys2 = max(b["bbox"][3] for b in blocks) + return [xs1, ys1, xs2 - xs1, ys2 - ys1] diff --git a/newspaper-extractor/backend/app/pipeline/categorizer.py b/newspaper-extractor/backend/app/pipeline/categorizer.py new file mode 100644 index 0000000..3f96365 --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/categorizer.py @@ -0,0 +1,51 @@ +import json +from app.config import settings + +CATEGORIES = ["Politics","National","International","Sports","Business","Technology", + "Education","Health","Science","Entertainment","Crime","Court","Weather", + "Editorial","Opinion","Local News","State News","Environment","Lifestyle", + "Automobile","Travel","Agriculture","Jobs","Classifieds","Others"] + +SUBCATS = {"Sports": ["Cricket","Football","Tennis","Others"], + "Business": ["Finance","Markets","Economy"], + "Technology": ["AI","Gadgets","Software"], + "Entertainment": ["Movies","TV","Music"], + "Politics": ["State Politics","National Politics","International"]} + +PROMPT = """You are a news classifier. Given the headline and text, return strict JSON: +{{"category": one of {cats}, "subcategory": string, "confidence": 0-1, +"summary": 2-sentence summary, "keywords": [up to 8], +"persons": [], "locations": [], "organizations": [], "dates": [], "sentiment": "pos|neu|neg"}} + +HEADLINE: {headline} +TEXT: {text} +""" + +class Categorizer: + def __init__(self): + self.provider = settings.LLM_PROVIDER + if self.provider == "gemini": + import google.generativeai as genai + genai.configure(api_key=settings.LLM_API_KEY) + self.model = genai.GenerativeModel("gemini-1.5-flash") + + def classify(self, headline, text): + prompt = PROMPT.format(cats=CATEGORIES, headline=headline, text=text[:4000]) + try: + if self.provider == "gemini": + resp = self.model.generate_content(prompt) + raw = resp.text.strip().strip("```json").strip("```") + return json.loads(raw) + except Exception as e: + return self._fallback(headline, text) + + def _fallback(self, headline, text): + # Zero-shot via local model if LLM unavailable + from transformers import pipeline + clf = pipeline("zero-shot-classification", + model="facebook/bart-large-mnli") + res = clf(headline + ". " + text[:500], CATEGORIES) + return {"category": res["labels"][0], "subcategory": "", + "confidence": res["scores"][0], "summary": "", "keywords": [], + "persons": [], "locations": [], "organizations": [], + "dates": [], "sentiment": "neu"} diff --git a/newspaper-extractor/backend/app/pipeline/cropper.py b/newspaper-extractor/backend/app/pipeline/cropper.py new file mode 100644 index 0000000..8bee9f1 --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/cropper.py @@ -0,0 +1,20 @@ +import cv2, os, uuid +from app.config import settings + +def crop_article(page_image, bbox, out_dir): + x, y, w, h = [int(v) for v in bbox] + crop = page_image[y:y+h, x:x+w] + os.makedirs(out_dir, exist_ok=True) + path = os.path.join(out_dir, f"article_{uuid.uuid4().hex[:8]}.png") + cv2.imwrite(path, cv2.cvtColor(crop, cv2.COLOR_RGB2BGR)) + return path + +def crop_figures(page_image, article, out_dir): + paths = [] + for b in article["blocks"]: + if b["type"] == "Figure": + paths.append(crop_article(page_image, _xywh(b["bbox"]), out_dir)) + return paths + +def _xywh(box): + return [box[0], box[1], box[2]-box[0], box[3]-box[1]] diff --git a/newspaper-extractor/backend/app/pipeline/enricher.py b/newspaper-extractor/backend/app/pipeline/enricher.py new file mode 100644 index 0000000..bbee817 --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/enricher.py @@ -0,0 +1,8 @@ +from sentence_transformers import SentenceTransformer +from app.config import settings + +class Enricher: + def __init__(self): + self.model = SentenceTransformer(settings.EMBED_MODEL) + def embed(self, text: str): + return self.model.encode(text, normalize_embeddings=True).tolist() diff --git a/newspaper-extractor/backend/app/pipeline/headline.py b/newspaper-extractor/backend/app/pipeline/headline.py new file mode 100644 index 0000000..177eb69 --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/headline.py @@ -0,0 +1,15 @@ +def detect_headline(article, page, ocr_engine): + tb = article.get("title_block") + if tb: + text, _ = ocr_engine.extract_text(page, _xywh(tb["bbox"]), page["native_words"]) + return text.strip().split("\n")[0] + # Fallback: largest-font text block + # font size ~ block height / line count + candidates = sorted(article["blocks"], key=lambda b: b["bbox"][3]-b["bbox"][1], reverse=True) + if candidates: + text, _ = ocr_engine.extract_text(page, _xywh(candidates[0]["bbox"]), page["native_words"]) + return text.strip().split("\n")[0] + return "" + +def _xywh(box): + return [box[0], box[1], box[2]-box[0], box[3]-box[1]] diff --git a/newspaper-extractor/backend/app/pipeline/layout_detector.py b/newspaper-extractor/backend/app/pipeline/layout_detector.py new file mode 100644 index 0000000..995359c --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/layout_detector.py @@ -0,0 +1,56 @@ +import layoutparser as lp +import numpy as np + +CLASS_MAP = { + 0: "Text", 1: "Title", 2: "List", + 3: "Table", 4: "Figure", 5: "Advertisement", +} + +class LayoutDetector: + def __init__(self): + try: + # Detectron2 PubLayNet — swap with custom DocLayNet/YOLOv11 weights + self.model = lp.Detectron2LayoutModel( + config_path="lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config", + extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.55], + label_map={0:"Text",1:"Title",2:"List",3:"Table",4:"Figure"}, + ) + except Exception: + self.model = None + # Optional secondary ad classifier (custom-trained) + self.ad_classifier = AdClassifier() + + def detect(self, image: np.ndarray): + if self.model is None: + # Fallback to single block if detectron2 isn't installed + h, w = image.shape[:2] + return [{ + "type": "Text", + "bbox": [0, 0, w, h], + "score": 1.0, + "is_ad": False + }] + + layout = self.model.detect(image) + blocks = [] + for b in layout: + box = b.coordinates # (x1,y1,x2,y2) + block = { + "type": b.type, + "bbox": [int(c) for c in box], + "score": float(b.score), + } + block["is_ad"] = self.ad_classifier.predict(image, box) + blocks.append(block) + return blocks + + +class AdClassifier: + """ + Ads rarely match column grids and contain logos/price patterns. + Train a lightweight CNN (MobileNet) on labeled ad/non-ad crops, + OR use heuristics + LLM verification. Stub below. + """ + def predict(self, image, box) -> bool: + # Heuristic placeholder: replace with trained model + return False diff --git a/newspaper-extractor/backend/app/pipeline/ocr_engine.py b/newspaper-extractor/backend/app/pipeline/ocr_engine.py new file mode 100644 index 0000000..66bab99 --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/ocr_engine.py @@ -0,0 +1,39 @@ +from paddleocr import PaddleOCR +import numpy as np + +class OCREngine: + def __init__(self, langs=["en", "te"]): + # PaddleOCR v2 — one instance per language, angle cls enabled + self.engines = { + l: PaddleOCR(use_angle_cls=True, lang=l, show_log=False) + for l in langs + } + + def ocr_crop(self, image: np.ndarray, lang="en"): + engine = self.engines.get(lang, self.engines["en"]) + result = engine.ocr(image, cls=True) + lines = [] + for line in (result[0] or []): + box, (text, conf) = line + lines.append({"text": text, "conf": conf, "box": box}) + return lines + + def extract_text(self, page, article_bbox, native_words): + x, y, w, h = article_bbox + if native_words: # searchable PDF — use native text inside bbox + words = [t for (wx0,wy0,wx1,wy1,t) in native_words + if wx0 >= x and wy0 >= y and wx1 <= x+w and wy1 <= y+h] + text = " ".join(words) + if len(text) > 50: + return text, 0.99 + # Fallback to OCR + crop = page["image"][int(y):int(y+h), int(x):int(x+w)] + lang = self.detect_language(crop) + lines = self.ocr_crop(crop, lang) + text = "\n".join(l["text"] for l in lines) + conf = float(np.mean([l["conf"] for l in lines])) if lines else 0.0 + return text, conf + + def detect_language(self, crop): + # Quick langdetect on a fast English OCR pass, or script detection + return "en" # plug in script-detection model diff --git a/newspaper-extractor/backend/app/pipeline/orchestrator.py b/newspaper-extractor/backend/app/pipeline/orchestrator.py new file mode 100644 index 0000000..bcd7ca3 --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/orchestrator.py @@ -0,0 +1,49 @@ +from app.pipeline.pdf_reader import PDFReader +from app.pipeline.layout_detector import LayoutDetector +from app.pipeline.article_segmenter import ArticleSegmenter +from app.pipeline.ocr_engine import OCREngine +from app.pipeline.headline import detect_headline +from app.pipeline.cropper import crop_article, crop_figures +from app.pipeline.categorizer import Categorizer +from app.pipeline.enricher import Enricher +from app.config import settings +import os + +class Pipeline: + def __init__(self): + self.layout = LayoutDetector() + self.segmenter = ArticleSegmenter() + self.ocr = OCREngine(settings.OCR_LANGS) + self.cat = Categorizer() + self.enricher = Enricher() + + def process(self, pdf_path, newspaper, edition, date, job_dir): + reader = PDFReader(pdf_path) + results = [] + for page in reader.iter_pages(): + h, w = page["image"].shape[:2] + blocks = self.layout.detect(page["image"]) + articles = self.segmenter.segment(blocks, w, h) + for art in articles: + text, conf = self.ocr.extract_text(page, art["bbox"], page["native_words"]) + if len(text.strip()) < 80: # skip noise/ads + continue + headline = detect_headline(art, page, self.ocr) + img_path = crop_article(page["image"], art["bbox"], job_dir) + fig_paths = crop_figures(page["image"], art, job_dir) + meta = self.cat.classify(headline, text) + embedding = self.enricher.embed(headline + " " + text[:1000]) + results.append({ + "date": date, "newspaper": newspaper, "edition": edition, + "page": page["page_number"], "headline": headline, + "category": meta["category"], "subcategory": meta.get("subcategory"), + "text": text, "summary": meta.get("summary"), + "keywords": meta.get("keywords"), "persons": meta.get("persons"), + "locations": meta.get("locations"), "organizations": meta.get("organizations"), + "sentiment": meta.get("sentiment"), "language": "en", + "image_path": img_path, "figure_paths": fig_paths, + "bounding_box": art["bbox"], + "confidence": round(art["confidence"] * meta.get("confidence", 1), 3), + "embedding": embedding, + }) + return results diff --git a/newspaper-extractor/backend/app/pipeline/pdf_reader.py b/newspaper-extractor/backend/app/pipeline/pdf_reader.py new file mode 100644 index 0000000..6f3554c --- /dev/null +++ b/newspaper-extractor/backend/app/pipeline/pdf_reader.py @@ -0,0 +1,35 @@ +import fitz # PyMuPDF +import numpy as np +from PIL import Image +from app.config import settings + +class PDFReader: + def __init__(self, path: str): + self.doc = fitz.open(path) + + def is_searchable(self, page) -> bool: + """Detect if a page has a usable native text layer.""" + text = page.get_text("text").strip() + return len(text) > 100 # heuristic + + def page_to_image(self, page) -> np.ndarray: + zoom = settings.DPI / 72 + mat = fitz.Matrix(zoom, zoom) + pix = page.get_pixmap(matrix=mat, alpha=False) + img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples) + return np.array(img) + + def native_text_blocks(self, page): + """Returns word boxes from searchable PDF: [(x0,y0,x1,y1,text)]""" + words = page.get_text("words") + scale = settings.DPI / 72 + return [(w[0]*scale, w[1]*scale, w[2]*scale, w[3]*scale, w[4]) for w in words] + + def iter_pages(self): + for i, page in enumerate(self.doc): + yield { + "page_number": i + 1, + "image": self.page_to_image(page), + "searchable": self.is_searchable(page), + "native_words": self.native_text_blocks(page) if self.is_searchable(page) else [], + } diff --git a/newspaper-extractor/backend/app/tasks/celery_app.py b/newspaper-extractor/backend/app/tasks/celery_app.py new file mode 100644 index 0000000..24ccd4e --- /dev/null +++ b/newspaper-extractor/backend/app/tasks/celery_app.py @@ -0,0 +1,9 @@ +from celery import Celery +from app.config import settings + +celery = Celery( + "newspaper_tasks", + broker=settings.REDIS_URL, + backend=settings.REDIS_URL, + include=["app.tasks.ingest"] +) diff --git a/newspaper-extractor/backend/app/tasks/ingest.py b/newspaper-extractor/backend/app/tasks/ingest.py new file mode 100644 index 0000000..4a89186 --- /dev/null +++ b/newspaper-extractor/backend/app/tasks/ingest.py @@ -0,0 +1,50 @@ +from app.tasks.celery_app import celery +from app.pipeline.orchestrator import Pipeline +from app.db.session import SessionLocal +from app.db.models import Article, ArticleImage, UploadJob +import hashlib, os + +pipeline = None + +@celery.task(bind=True) +def process_pdf(self, job_id, pdf_path, newspaper, edition, date): + db = SessionLocal() + job = db.query(UploadJob).filter_by(id=job_id).first() + if job: + job.status = "PROCESSING" + db.commit() + + global pipeline + if pipeline is None: + from app.pipeline.orchestrator import Pipeline + pipeline = Pipeline() + + job_dir = os.path.join("/data/storage", os.path.basename(pdf_path) + "_crops") + self.update_state(state="PROCESSING", meta={"stage": "layout"}) + try: + articles = pipeline.process(pdf_path, newspaper, edition, date, job_dir) + for a in articles: + chash = hashlib.sha256(a["text"][:500].encode()).hexdigest() + if db.query(Article).filter_by(content_hash=chash).first(): + continue # dedup / syndicated + art = Article(content_hash=chash, **{k: a[k] for k in [ + "date","newspaper","edition","page","headline","category", + "subcategory","text","summary","keywords","persons","locations", + "organizations","sentiment","language","image_path", + "bounding_box","confidence","embedding"]}) + art.upload_job_id = job_id + db.add(art); db.flush() + for fp in a["figure_paths"]: + db.add(ArticleImage(article_id=art.id, image_path=fp)) + if job: + job.status = "COMPLETED" + db.commit() + except Exception as e: + if job: + job.status = "FAILED" + job.error_message = str(e) + db.commit() + raise e + finally: + db.close() + return {"articles": len(articles)} diff --git a/newspaper-extractor/backend/requirements.txt b/newspaper-extractor/backend/requirements.txt new file mode 100644 index 0000000..ce8d414 --- /dev/null +++ b/newspaper-extractor/backend/requirements.txt @@ -0,0 +1,21 @@ +fastapi +uvicorn +pydantic-settings +sqlalchemy +psycopg2-binary +pgvector +celery +redis +PyMuPDF +Pillow +numpy +scipy +scikit-learn +layoutparser[detectron2] +paddleocr==2.7.3 +paddlepaddle==2.6.2 +sentence-transformers +google-generativeai +transformers +opencv-python-headless +python-multipart diff --git a/newspaper-extractor/docker-compose.yml b/newspaper-extractor/docker-compose.yml new file mode 100644 index 0000000..eff5cf6 --- /dev/null +++ b/newspaper-extractor/docker-compose.yml @@ -0,0 +1,43 @@ +version: "3.9" +services: + db: + image: pgvector/pgvector:pg16 + environment: + POSTGRES_USER: user + POSTGRES_PASSWORD: pass + POSTGRES_DB: news + volumes: ["pgdata:/var/lib/postgresql/data"] + + redis: + image: redis:7-alpine + + backend: + build: ./backend + env_file: .env + depends_on: [db, redis] + volumes: ["./data:/data", "./backend:/app"] + ports: ["8080:8000"] + command: uvicorn app.main:app --host 0.0.0.0 --port 8000 + deploy: + resources: + reservations: + devices: [{capabilities: ["gpu"]}] # GPU acceleration + + worker: + build: ./backend + env_file: .env + depends_on: [db, redis] + volumes: ["./data:/data", "./backend:/app"] + command: celery -A app.tasks.celery_app worker --loglevel=info --concurrency=2 + + frontend: + build: ./frontend + ports: ["3000:3000"] + volumes: + - "./frontend:/app" + - "/app/node_modules" + command: npm run dev + depends_on: [backend] + +volumes: + pgdata: diff --git a/newspaper-extractor/frontend/.dockerignore b/newspaper-extractor/frontend/.dockerignore new file mode 100644 index 0000000..b90a368 --- /dev/null +++ b/newspaper-extractor/frontend/.dockerignore @@ -0,0 +1,2 @@ +node_modules +.next diff --git a/newspaper-extractor/frontend/.gitignore b/newspaper-extractor/frontend/.gitignore new file mode 100644 index 0000000..5ef6a52 --- /dev/null +++ b/newspaper-extractor/frontend/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/newspaper-extractor/frontend/AGENTS.md b/newspaper-extractor/frontend/AGENTS.md new file mode 100644 index 0000000..8bd0e39 --- /dev/null +++ b/newspaper-extractor/frontend/AGENTS.md @@ -0,0 +1,5 @@ + +# This is NOT the Next.js you know + +This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices. + diff --git a/newspaper-extractor/frontend/CLAUDE.md b/newspaper-extractor/frontend/CLAUDE.md new file mode 100644 index 0000000..43c994c --- /dev/null +++ b/newspaper-extractor/frontend/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/newspaper-extractor/frontend/Dockerfile b/newspaper-extractor/frontend/Dockerfile new file mode 100644 index 0000000..50cd51c --- /dev/null +++ b/newspaper-extractor/frontend/Dockerfile @@ -0,0 +1,12 @@ +FROM node:20-slim + +WORKDIR /app + +COPY package*.json ./ +RUN npm install + +COPY . . + +RUN npm run build + +CMD ["npm", "start"] diff --git a/newspaper-extractor/frontend/README.md b/newspaper-extractor/frontend/README.md new file mode 100644 index 0000000..e215bc4 --- /dev/null +++ b/newspaper-extractor/frontend/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/newspaper-extractor/frontend/eslint.config.mjs b/newspaper-extractor/frontend/eslint.config.mjs new file mode 100644 index 0000000..05e726d --- /dev/null +++ b/newspaper-extractor/frontend/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/newspaper-extractor/frontend/next.config.ts b/newspaper-extractor/frontend/next.config.ts new file mode 100644 index 0000000..9bcda51 --- /dev/null +++ b/newspaper-extractor/frontend/next.config.ts @@ -0,0 +1,14 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + async rewrites() { + return [ + { + source: '/api/:path*', + destination: 'http://backend:8000/api/:path*', + }, + ]; + }, +}; + +export default nextConfig; diff --git a/newspaper-extractor/frontend/package-lock.json b/newspaper-extractor/frontend/package-lock.json new file mode 100644 index 0000000..0b75af5 --- /dev/null +++ b/newspaper-extractor/frontend/package-lock.json @@ -0,0 +1,4886 @@ +{ + "name": "frontend", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.1.0", + "dependencies": { + "clsx": "^2.1.1", + "lucide-react": "^1.22.0", + "next": "16.2.9", + "react": "19.2.4", + "react-dom": "19.2.4", + "tailwind-merge": "^3.6.0" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + 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"typescript": "^5" + } +} diff --git a/newspaper-extractor/frontend/postcss.config.mjs b/newspaper-extractor/frontend/postcss.config.mjs new file mode 100644 index 0000000..61e3684 --- /dev/null +++ b/newspaper-extractor/frontend/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/newspaper-extractor/frontend/public/file.svg b/newspaper-extractor/frontend/public/file.svg new file mode 100644 index 0000000..004145c --- /dev/null +++ b/newspaper-extractor/frontend/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/newspaper-extractor/frontend/public/globe.svg b/newspaper-extractor/frontend/public/globe.svg new file mode 100644 index 0000000..567f17b --- /dev/null +++ b/newspaper-extractor/frontend/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/newspaper-extractor/frontend/public/next.svg b/newspaper-extractor/frontend/public/next.svg new file mode 100644 index 0000000..5174b28 --- /dev/null +++ b/newspaper-extractor/frontend/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/newspaper-extractor/frontend/public/vercel.svg b/newspaper-extractor/frontend/public/vercel.svg new file mode 100644 index 0000000..7705396 --- /dev/null +++ b/newspaper-extractor/frontend/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/newspaper-extractor/frontend/public/window.svg b/newspaper-extractor/frontend/public/window.svg new file mode 100644 index 0000000..b2b2a44 --- /dev/null +++ b/newspaper-extractor/frontend/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/newspaper-extractor/frontend/src/app/dashboard/page.tsx b/newspaper-extractor/frontend/src/app/dashboard/page.tsx new file mode 100644 index 0000000..53a42de --- /dev/null +++ b/newspaper-extractor/frontend/src/app/dashboard/page.tsx @@ -0,0 +1,36 @@ +"use client"; +import { useState } from "react"; + +export default function Dashboard() { + const [task, setTask] = useState(null); + const [status, setStatus] = useState(""); + + async function upload(file: File) { + const fd = new FormData(); + fd.append("file", file); + fd.append("newspaper", "The Hindu"); + fd.append("date", new Date().toISOString().slice(0,10)); + const r = await fetch("http://localhost:8000/api/upload", {method:"POST", body:fd}); + const j = await r.json(); + setTask(j.task_id); + poll(j.task_id); + } + + async function poll(id: string) { + const r = await fetch(`http://localhost:8000/api/status/${id}`); + const j = await r.json(); + setStatus(j.state); + if (j.state !== "SUCCESS" && j.state !== "FAILURE") + setTimeout(() => poll(id), 2000); + } + + return ( +
+

Newspaper Extractor

+ e.target.files && upload(e.target.files[0])} + className="border p-4 rounded" /> + {task &&

Status: {status}

} +
+ ); +} diff --git a/newspaper-extractor/frontend/src/app/favicon.ico b/newspaper-extractor/frontend/src/app/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/newspaper-extractor/frontend/src/app/favicon.ico differ diff --git a/newspaper-extractor/frontend/src/app/globals.css b/newspaper-extractor/frontend/src/app/globals.css new file mode 100644 index 0000000..17bd09e --- /dev/null +++ b/newspaper-extractor/frontend/src/app/globals.css @@ -0,0 +1,44 @@ +@import "tailwindcss"; + +@theme inline { + --color-background: #030712; /* gray-950 */ + --color-foreground: #f9fafb; /* gray-50 */ + --font-sans: var(--font-geist-sans), ui-sans-serif, system-ui, sans-serif; + --font-mono: var(--font-geist-mono), ui-monospace, SFMono-Regular, monospace; +} + +:root { + --background: #030712; + --foreground: #f9fafb; +} + +body { + background-color: var(--background); + color: var(--foreground); + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +/* Custom Scrollbar for a premium feel */ +::-webkit-scrollbar { + width: 8px; + height: 8px; +} +::-webkit-scrollbar-track { + background: transparent; +} +::-webkit-scrollbar-thumb { + background: #374151; + border-radius: 4px; +} +::-webkit-scrollbar-thumb:hover { + background: #4b5563; +} + +/* Glassmorphism utilities */ +.glass { + background: rgba(17, 24, 39, 0.7); + backdrop-filter: blur(12px); + -webkit-backdrop-filter: blur(12px); + border: 1px solid rgba(255, 255, 255, 0.05); +} diff --git a/newspaper-extractor/frontend/src/app/layout.tsx b/newspaper-extractor/frontend/src/app/layout.tsx new file mode 100644 index 0000000..976eb90 --- /dev/null +++ b/newspaper-extractor/frontend/src/app/layout.tsx @@ -0,0 +1,33 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "Create Next App", + description: "Generated by create next app", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + {children} + + ); +} diff --git a/newspaper-extractor/frontend/src/app/page.tsx b/newspaper-extractor/frontend/src/app/page.tsx new file mode 100644 index 0000000..5a547fe --- /dev/null +++ b/newspaper-extractor/frontend/src/app/page.tsx @@ -0,0 +1,136 @@ +'use client'; + +import { useState, useEffect } from 'react'; +import { SearchBar } from '@/components/SearchBar'; +import { ArticleCard } from '@/components/ArticleCard'; +import { UploadModal } from '@/components/UploadModal'; +import UploadQueue from '@/components/UploadQueue'; +import { searchArticles, Article } from '@/lib/api'; +import { Newspaper, Upload, Sparkles, AlertCircle } from 'lucide-react'; + +export default function Home() { + const [articles, setArticles] = useState([]); + const [isLoading, setIsLoading] = useState(false); + const [isUploadModalOpen, setIsUploadModalOpen] = useState(false); + const [hasSearched, setHasSearched] = useState(false); + const [error, setError] = useState(null); + const [selectedJobId, setSelectedJobId] = useState(null); + + // Fetch initial generic results on load + useEffect(() => { + handleSearch('', false); + }, []); + + const handleSearch = async (query: string, semantic: boolean, jobId: number | null = selectedJobId) => { + setIsLoading(true); + setHasSearched(true); + setError(null); + try { + const results = await searchArticles(query, semantic, jobId || undefined); + setArticles(results || []); + } catch (err) { + console.error(err); + setError('Failed to fetch articles. Please ensure the backend is running.'); + setArticles([]); + } finally { + setIsLoading(false); + } + }; + + const handleJobSelect = (jobId: number | null) => { + setSelectedJobId(jobId); + handleSearch('', false, jobId); + }; + + return ( +
+ + {/* Navbar */} + + + {/* Hero Section */} +
+
+
+ + Powered by OCR & Layout Parsing +
+

+ Intelligent Newspaper
Extraction +

+

+ Search through semantically embedded newspaper articles, categorized by layout and translated via AI. +

+
+ + {/* Main Content Layout with Sidebar */} +
+ {/* Main Feed */} +
+ {/* Search Bar */} +
+ handleSearch(q, s, selectedJobId)} isLoading={isLoading} /> +
+ + {/* Results */} + {error ? ( +
+ +

Connection Error

+

{error}

+
+ ) : isLoading ? ( +
+ {[1, 2, 3, 4].map(i => ( +
+ ))} +
+ ) : articles.length > 0 ? ( +
+ {articles.map(article => ( + + ))} +
+ ) : hasSearched ? ( +
+

No articles found

+

Try adjusting your search query or uploading a new newspaper.

+
+ ) : null} +
+ + {/* Sidebar */} +
+ +
+
+
+ + setIsUploadModalOpen(false)} + /> +
+ ); +} diff --git a/newspaper-extractor/frontend/src/components/ArticleCard.tsx b/newspaper-extractor/frontend/src/components/ArticleCard.tsx new file mode 100644 index 0000000..de5148e --- /dev/null +++ b/newspaper-extractor/frontend/src/components/ArticleCard.tsx @@ -0,0 +1,112 @@ +'use client'; + +import { Article } from '@/lib/api'; +import { Calendar, Newspaper, Hash, Smile, Frown, Meh, Image as ImageIcon } from 'lucide-react'; +import { cn } from '@/lib/utils'; + +interface ArticleCardProps { + article: Article; +} + +export function ArticleCard({ article }: ArticleCardProps) { + // Determine sentiment badge + const sentimentScore = parseFloat(article.sentiment || "0"); + const isPositive = sentimentScore > 0.3; + const isNegative = sentimentScore < -0.3; + + // Create an image url if image path exists + // The path is internal to docker like /data/storage/cropped_XYZ.jpg + // Assuming the backend mounts /data/storage at /api/images + const imageUrl = article.image_path + ? `http://localhost:8000/api/images/${article.image_path.split('/').pop()}` + : null; + + return ( +
+ {/* Header info */} +
+
+ + + {article.newspaper} ({article.edition}) - P.{article.page} + + + + {article.date} + + + + {article.category} {article.subcategory ? `/ ${article.subcategory}` : ''} + +
+ + {/* Sentiment Badge */} +
+ {isPositive ? : isNegative ? : } + {isPositive ? "Positive" : isNegative ? "Negative" : "Neutral"} +
+
+ + {/* Title & Content */} +
+
+

+ {article.headline || "Untitled Article"} +

+ {article.subheadline && ( +

+ {article.subheadline} +

+ )} + +

+ {article.summary || article.text?.slice(0, 250) + "..."} +

+
+ + {/* Optional Image */} + {imageUrl ? ( +
+ Article snippet { + // fallback if image fails to load + e.currentTarget.style.display = 'none'; + }} + /> +
+ ) : ( +
+ +
+ )} +
+ + {/* Entities / Tags */} +
+ {article.persons?.map(p => ( + + 👤 {p} + + ))} + {article.locations?.map(l => ( + + 📍 {l} + + ))} + {article.organizations?.map(o => ( + + 🏢 {o} + + ))} +
+
+ ); +} diff --git a/newspaper-extractor/frontend/src/components/SearchBar.tsx b/newspaper-extractor/frontend/src/components/SearchBar.tsx new file mode 100644 index 0000000..caabaf4 --- /dev/null +++ b/newspaper-extractor/frontend/src/components/SearchBar.tsx @@ -0,0 +1,61 @@ +'use client'; + +import { Search, Sparkles } from 'lucide-react'; +import { cn } from '@/lib/utils'; +import { useState } from 'react'; + +interface SearchBarProps { + onSearch: (query: string, semantic: boolean) => void; + isLoading: boolean; +} + +export function SearchBar({ onSearch, isLoading }: SearchBarProps) { + const [query, setQuery] = useState(''); + const [isSemantic, setIsSemantic] = useState(false); + + const handleSubmit = (e: React.FormEvent) => { + e.preventDefault(); + onSearch(query, isSemantic); + }; + + return ( +
+
+
+ + setQuery(e.target.value)} + placeholder={isSemantic ? "Describe the concept or event you're looking for..." : "Search headlines, text, or keywords..."} + className="w-full bg-transparent py-5 text-zinc-100 placeholder-zinc-500 focus:outline-none text-lg" + /> +
+ +
+ + + +
+
+
+ ); +} diff --git a/newspaper-extractor/frontend/src/components/UploadModal.tsx b/newspaper-extractor/frontend/src/components/UploadModal.tsx new file mode 100644 index 0000000..f67eb89 --- /dev/null +++ b/newspaper-extractor/frontend/src/components/UploadModal.tsx @@ -0,0 +1,152 @@ +'use client'; + +import { useState } from 'react'; +import { uploadPDF } from '@/lib/api'; +import { X, UploadCloud, Loader2 } from 'lucide-react'; + +interface UploadModalProps { + isOpen: boolean; + onClose: () => void; +} + +export function UploadModal({ isOpen, onClose }: UploadModalProps) { + const [file, setFile] = useState(null); + const [newspaper, setNewspaper] = useState('Andhra Jyothi'); + const [edition, setEdition] = useState('Main'); + const [date, setDate] = useState(new Date().toISOString().split('T')[0]); + const [loading, setLoading] = useState(false); + const [taskId, setTaskId] = useState(null); + const [error, setError] = useState(null); + + if (!isOpen) return null; + + const handleFileChange = (e: React.ChangeEvent) => { + if (e.target.files && e.target.files.length > 0) { + setFile(e.target.files[0]); + } + }; + + const handleUpload = async () => { + if (!file) { + setError('Please select a PDF file.'); + return; + } + setLoading(true); + setError(null); + try { + const res = await uploadPDF(file, newspaper, edition, date); + setTaskId(res.task_id); + } catch (err) { + setError(err instanceof Error ? err.message : 'Unknown error'); + } finally { + setLoading(false); + } + }; + + return ( +
+
+
+

Upload Newspaper

+ +
+ + {taskId ? ( +
+
+ +
+

Upload Successful

+

+ Task queued for processing. It may take a few minutes for the pipeline to extract all data. +

+

Task ID: {taskId}

+ +
+ ) : ( +
+
+ +
+ + + + {file ? file.name : 'Click or drag PDF to upload'} + + Max size 50MB +
+
+ +
+
+ + setNewspaper(e.target.value)} + className="w-full rounded-lg border border-zinc-800 bg-zinc-900 px-3 py-2 text-sm text-zinc-100 placeholder-zinc-600 focus:border-indigo-500 focus:outline-none focus:ring-1 focus:ring-indigo-500" + /> +
+
+ + setEdition(e.target.value)} + className="w-full rounded-lg border border-zinc-800 bg-zinc-900 px-3 py-2 text-sm text-zinc-100 placeholder-zinc-600 focus:border-indigo-500 focus:outline-none focus:ring-1 focus:ring-indigo-500" + /> +
+
+ +
+ + setDate(e.target.value)} + className="w-full rounded-lg border border-zinc-800 bg-zinc-900 px-3 py-2 text-sm text-zinc-100 focus:border-indigo-500 focus:outline-none focus:ring-1 focus:ring-indigo-500" + /> +
+ + {error && ( +
+ {error} +
+ )} + + +
+ )} +
+
+ ); +} diff --git a/newspaper-extractor/frontend/src/components/UploadQueue.tsx b/newspaper-extractor/frontend/src/components/UploadQueue.tsx new file mode 100644 index 0000000..5a41527 --- /dev/null +++ b/newspaper-extractor/frontend/src/components/UploadQueue.tsx @@ -0,0 +1,97 @@ +'use client'; +import { useEffect, useState } from 'react'; +import { getJobs, UploadJob } from '@/lib/api'; +import { FileText, Clock, CheckCircle2, XCircle, RefreshCw } from 'lucide-react'; + +export default function UploadQueue({ + onSelectJob, + selectedJobId +}: { + onSelectJob: (jobId: number | null) => void; + selectedJobId: number | null; +}) { + const [jobs, setJobs] = useState([]); + const [loading, setLoading] = useState(true); + + const fetchJobs = async () => { + try { + const data = await getJobs(); + setJobs(data); + } catch (err) { + console.error("Failed to fetch jobs:", err); + } finally { + setLoading(false); + } + }; + + useEffect(() => { + fetchJobs(); + const interval = setInterval(fetchJobs, 5000); + return () => clearInterval(interval); + }, []); + + const getStatusIcon = (status: string) => { + switch(status) { + case 'COMPLETED': return ; + case 'FAILED': return ; + case 'PROCESSING': return ; + default: return ; + } + }; + + return ( +
+
+

Upload History

+ {selectedJobId && ( + + )} +
+ +
+ {loading && jobs.length === 0 ? ( +
Loading...
+ ) : jobs.length === 0 ? ( +
No uploads yet.
+ ) : ( + jobs.map(job => ( + + )) + )} +
+
+ ); +} diff --git a/newspaper-extractor/frontend/src/lib/api.ts b/newspaper-extractor/frontend/src/lib/api.ts new file mode 100644 index 0000000..2c976b8 --- /dev/null +++ b/newspaper-extractor/frontend/src/lib/api.ts @@ -0,0 +1,85 @@ +export interface Article { + id: number; + newspaper: string; + edition: string; + page: number; + date: string; + headline: string; + subheadline: string; + category: string; + subcategory: string; + language: string; + author: string; + text: string; + summary: string; + keywords: string[]; + persons: string[]; + locations: string[]; + organizations: string[]; + sentiment: string; + image_path: string; + confidence: number; +} + +export interface UploadJob { + id: number; + filename: string; + newspaper: string; + edition: string; + date: string; + status: string; + error_message?: string; + created_time: string; +} + +export async function searchArticles(query: string, isSemantic: boolean, jobId?: number): Promise { + const url = new URL('/api/search', window.location.origin); + if (query) url.searchParams.append('q', query); + if (jobId) url.searchParams.append('job_id', jobId.toString()); + if (isSemantic) url.searchParams.append('semantic', 'true'); + + const response = await fetch(url.toString(), { + method: 'GET', + headers: { + 'Accept': 'application/json', + }, + // Prevent Next.js from caching dynamic search results + cache: 'no-store' + }); + + if (!response.ok) { + throw new Error('Failed to fetch articles'); + } + + return response.json(); +} + +export async function uploadPDF(file: File, newspaper: string, edition: string, date: string) { + const formData = new FormData(); + formData.append('file', file); + formData.append('newspaper', newspaper); + formData.append('edition', edition); + formData.append('date', date); + + const response = await fetch('/api/upload', { + method: 'POST', + body: formData, + }); + + if (!response.ok) { + throw new Error('Failed to upload PDF'); + } + + return response.json(); +} + +export async function getJobs(): Promise { + const response = await fetch('/api/jobs', { + method: 'GET', + cache: 'no-store' + }); + if (!response.ok) { + throw new Error('Failed to fetch jobs'); + } + return response.json(); +} diff --git a/newspaper-extractor/frontend/src/lib/utils.ts b/newspaper-extractor/frontend/src/lib/utils.ts new file mode 100644 index 0000000..d084cca --- /dev/null +++ b/newspaper-extractor/frontend/src/lib/utils.ts @@ -0,0 +1,6 @@ +import { type ClassValue, clsx } from "clsx" +import { twMerge } from "tailwind-merge" + +export function cn(...inputs: ClassValue[]) { + return twMerge(clsx(inputs)) +} diff --git a/newspaper-extractor/frontend/tsconfig.json b/newspaper-extractor/frontend/tsconfig.json new file mode 100644 index 0000000..cf9c65d --- /dev/null +++ b/newspaper-extractor/frontend/tsconfig.json @@ -0,0 +1,34 @@ +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "react-jsx", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/types/**/*.ts", + ".next/dev/types/**/*.ts", + "**/*.mts" + ], + "exclude": ["node_modules"] +}