Update main.py

main.py

@@ -1,22 +1,26 @@
-import os
+import os, io
 from fastapi import FastAPI, UploadFile, File, Form
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.responses import JSONResponse, HTMLResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
 from huggingface_hub import InferenceClient
 from PyPDF2 import PdfReader
 from docx import Document
 from PIL import Image
-import io
 from io import BytesIO
-import requests
 
-# Remplace ce token par le tien de manière sécurisée (variable d'environnement recommandée en production)
-HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")
+# -----------------------------------------------------------------------------
+# CONFIGURATION
+# -----------------------------------------------------------------------------
+HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")                # injected as a secret
+PORT               = int(os.getenv("PORT", 7860))        # HF Spaces provides it
 
-# Initialisation de l'app FastAPI
-app = FastAPI()
+app = FastAPI(
+    title="AI‑Powered Web‑App API",
+    description="Backend endpoints for summarisation, captioning and QA",
+    version="1.1.0",
+)
 
-# Autoriser les requêtes Cross-Origin
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -25,142 +29,111 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Initialisation des clients Hugging Face avec authentification
-summary_client = InferenceClient(model="facebook/bart-large-cnn", token=HUGGINGFACE_TOKEN)
-qa_client = InferenceClient(model="deepset/roberta-base-squad2", token=HUGGINGFACE_TOKEN)
-image_caption_client = InferenceClient(model="nlpconnect/vit-gpt2-image-captioning", token=HUGGINGFACE_TOKEN)
+# Optional: serve static assets from /static (images, css, js)
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# -----------------------------------------------------------------------------
+# MODEL CLIENTS (remote HuggingFace Inference API)
+# -----------------------------------------------------------------------------
+summary_client       = InferenceClient("facebook/bart-large-cnn",            token=HUGGINGFACE_TOKEN)
+qa_client            = InferenceClient("deepset/roberta-base-squad2",        token=HUGGINGFACE_TOKEN)
+image_caption_client = InferenceClient("nlpconnect/vit-gpt2-image-captioning", token=HUGGINGFACE_TOKEN)
+
+# -----------------------------------------------------------------------------
+# UTILITY FUNCTIONS
+# -----------------------------------------------------------------------------
 
-# Extraction du texte des fichiers
 def extract_text_from_pdf(content: bytes) -> str:
-    text = ""
     reader = PdfReader(io.BytesIO(content))
-    for page in reader.pages:
-        if page.extract_text():
-            text += page.extract_text() + "\n"
-    return text.strip()
+    return "\n".join(page.extract_text() or "" for page in reader.pages).strip()
 
 def extract_text_from_docx(content: bytes) -> str:
-    text = ""
     doc = Document(io.BytesIO(content))
-    for para in doc.paragraphs:
-        text += para.text + "\n"
-    return text.strip()
+    return "\n".join(p.text for p in doc.paragraphs).strip()
 
 def process_uploaded_file(file: UploadFile) -> str:
-    content = file.file.read()
-    extension = file.filename.split('.')[-1].lower()
-
+    content   = file.file.read()
+    extension = file.filename.split(".")[-1].lower()
     if extension == "pdf":
         return extract_text_from_pdf(content)
-    elif extension == "docx":
+    if extension == "docx":
         return extract_text_from_docx(content)
-    elif extension == "txt":
+    if extension == "txt":
         return content.decode("utf-8").strip()
-    else:
-        raise ValueError("Type de fichier non supporté")
+    raise ValueError("Unsupported file type")
+
+# -----------------------------------------------------------------------------
+# ROUTES
+# -----------------------------------------------------------------------------
 
-# Point d'entrée HTML
 @app.get("/", response_class=HTMLResponse)
-async def serve_homepage():
-    with open("index.html", "r", encoding="utf-8") as f:
-        return HTMLResponse(content=f.read(), status_code=200)
+async def serve_index():
+    """Send the frontend HTML."""
+    return FileResponse("index.html")
+
+# ---------- Summarisation -----------------------------------------------------
 
-# Résumé
-@app.post("/analyze")
-async def analyze_file(file: UploadFile = File(...)):
+@app.post("/api/summarize")
+async def summarize_document(file: UploadFile = File(...)):
     try:
         text = process_uploaded_file(file)
-
         if len(text) < 20:
-            return {"summary": "Document trop court pour être résumé."}
-
-        summary = summary_client.summarization(text[:3000])
-        return {"summary": summary}
+            return {"result": "Document too short to summarise."}
+        summary_text = summary_client.summarization(text[:3000])
+        return {"result": str(summary_text)}
+    except Exception as exc:
+        return JSONResponse(status_code=500, content={"error": f"Analyse failure: {exc}"})
 
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": f"Erreur lors de l'analyse: {str(e)}"})
+# ---------- Image Caption -----------------------------------------------------
 
-# Question-Réponse
-@app.post("/ask")
-async def ask_question(file: UploadFile = File(...), question: str = Form(...)):
+@app.post("/api/caption")
+async def caption_image(file: UploadFile = File(...)):
     try:
-        # Determine if the file is an image
-        content_type = file.content_type
-        if content_type.startswith("image/"):
-            image_bytes = await file.read()
-            image_pil = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-            image_pil.thumbnail((1024, 1024))
-
-            img_byte_arr = BytesIO()
-            image_pil.save(img_byte_arr, format='JPEG')
-            img_byte_arr = img_byte_arr.getvalue()
-
-            # Generate image description
-            result = image_caption_client.image_to_text(img_byte_arr)
-            if isinstance(result, dict):
-                context = result.get("generated_text") or result.get("caption") or ""
-            elif isinstance(result, list) and len(result) > 0:
-                context = result[0].get("generated_text", "")
-            elif isinstance(result, str):
-                context = result
-            else:
-                context = ""
-
+        image_bytes = await file.read()
+        image_pil   = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        image_pil.thumbnail((1024, 1024))
+        buf = BytesIO(); image_pil.save(buf, format="JPEG"); img = buf.getvalue()
+        result = image_caption_client.image_to_text(img)
+        if isinstance(result, dict):
+            caption = result.get("generated_text") or result.get("caption") or "No caption found."
+        elif isinstance(result, list):
+            caption = result[0].get("generated_text", "No caption found.")
         else:
-            # Not an image, process as document
-            text = process_uploaded_file(file)
-            if len(text) < 20:
-                return {"answer": "Document trop court pour répondre à la question."}
-            context = text[:3000]
+            caption = str(result)
+        return {"result": str(caption)}
+    except Exception as exc:
+        return JSONResponse(status_code=500, content={"error": f"Caption failure: {exc}"})
 
-        if not context:
-            return {"answer": "Aucune information disponible pour répondre à la question."}
-
-        result = qa_client.question_answering(question=question, context=context)
-        return {"answer": result.get("answer", "Aucune réponse trouvée.")}
+# ---------- Question Answering ----------------------------------------------
 
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": f"Erreur lors de la recherche de réponse: {str(e)}"})
-        
-# Interprétation d'Image
-@app.post("/interpret_image")
-async def interpret_image(image: UploadFile = File(...)):
+@app.post("/api/qa")
+async def question_answering(file: UploadFile = File(...), question: str = Form(...)):
     try:
-        # Lire l'image
-        image_bytes = await image.read()
-
-        # Ouvrir l'image avec PIL
-        image_pil = Image.open(io.BytesIO(image_bytes))
-        image_pil = image_pil.convert("RGB")
-        image_pil.thumbnail((1024, 1024))
-
-        # Convertir en bytes (JPEG)
-        img_byte_arr = BytesIO()
-        image_pil.save(img_byte_arr, format='JPEG')
-        img_byte_arr = img_byte_arr.getvalue()
-
-        # Appeler le modèle
-        result = image_caption_client.image_to_text(img_byte_arr)
-
-        # 🔍 Affichage du résultat brut pour débogage
-        print("Résultat brut du modèle image-to-text:", result)
-
-        # Extraire la description si disponible
-        if isinstance(result, dict):
-            description = result.get("generated_text") or result.get("caption") or "Description non trouvée."
-        elif isinstance(result, list) and len(result) > 0:
-            description = result[0].get("generated_text", "Description non trouvée.")
-        elif isinstance(result, str):
-            description = result
+        if file.content_type.startswith("image/"):
+            image_bytes = await file.read()
+            pil        = Image.open(io.BytesIO(image_bytes)).convert("RGB"); pil.thumbnail((1024, 1024))
+            buf = BytesIO(); pil.save(buf, format="JPEG"); img = buf.getvalue()
+            res = image_caption_client.image_to_text(img)
+            context = res.get("generated_text") if isinstance(res, dict) else str(res)
         else:
-            description = "Description non trouvée."
+            context = process_uploaded_file(file)[:3000]
+        if not context:
+            return {"result": "No context – cannot answer."}
+        answer = qa_client.question_answering(question=question, context=context)
+        return {"result": str(answer.get("answer", "No answer found."))}
+    except Exception as exc:
+        return JSONResponse(status_code=500, content={"error": f"QA failure: {exc}"})
+
+# ---------- Health check ------------------------------------------------------
 
-        return {"description": description}
+@app.get("/api/health")
+async def health():
+    return {"status": "healthy", "hf_token_set": bool(HUGGINGFACE_TOKEN)}
 
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": f"Erreur lors de l'interprétation de l'image: {str(e)}"})
+# -----------------------------------------------------------------------------
+# ENTRYPOINT
+# -----------------------------------------------------------------------------
 
-# Démarrage local
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
+    uvicorn.run(app, host="0.0.0.0", port=PORT)