Update main.py

main.py

@@ -1,39 +1,22 @@
 import os
-import io
-from io import BytesIO
 from fastapi import FastAPI, UploadFile, File, Form
-from fastapi.responses import JSONResponse, HTMLResponse
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
-from huggingface_hub import InferenceClient, login
+from fastapi.responses import JSONResponse, HTMLResponse
+from huggingface_hub import InferenceClient
 from PyPDF2 import PdfReader
 from docx import Document
 from PIL import Image
-from routers import ai   # conservez vos routes annexes
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 1) Authentification Hugging Face
-# ──────────────────────────────────────────────────────────────────────────────
-HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
-if not HF_TOKEN:
-    raise RuntimeError(
-        "Variable d'environnement HF_TOKEN absente ; créez un jeton « Read » "
-        "sur https://huggingface.co/settings/tokens et exportez-le (voir .env)."
-    )
-
-login(token=HF_TOKEN)           # Authentifie tout le process
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 2) Configuration FastAPI
-# ──────────────────────────────────────────────────────────────────────────────
-PORT = int(os.getenv("PORT", 7860))
-
-app = FastAPI(
-    title="AI Web App API",
-    description="Backend API for AI-powered web application",
-    version="1.0.0"
-)
+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")
+
+# Initialisation de l'app FastAPI
+app = FastAPI()
+
+# Autoriser les requêtes Cross-Origin
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -42,42 +25,142 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-app.mount("/", StaticFiles(directory=".", html=True), name="static")
-app.include_router(ai.router)
+# 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)
 
-# Clients HF (token passé implicitement)
-summary_client       = InferenceClient("facebook/bart-large-cnn")
-qa_client            = InferenceClient("deepset/roberta-base-squad2")
-image_caption_client = InferenceClient("nlpconnect/vit-gpt2-image-captioning")
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 3) Utils : extraction texte, routes API (inchangés ou presque)
-# ──────────────────────────────────────────────────────────────────────────────
+# Extraction du texte des fichiers
 def extract_text_from_pdf(content: bytes) -> str:
+    text = ""
     reader = PdfReader(io.BytesIO(content))
-    return "\n".join(p.extract_text() or "" for p in reader.pages).strip()
+    for page in reader.pages:
+        if page.extract_text():
+            text += page.extract_text() + "\n"
+    return text.strip()
 
 def extract_text_from_docx(content: bytes) -> str:
+    text = ""
     doc = Document(io.BytesIO(content))
-    return "\n".join(p.text for p in doc.paragraphs).strip()
+    for para in doc.paragraphs:
+        text += para.text + "\n"
+    return text.strip()
 
 def process_uploaded_file(file: UploadFile) -> str:
     content = file.file.read()
-    ext = file.filename.rsplit(".", 1)[-1].lower()
-    if ext == "pdf":
+    extension = file.filename.split('.')[-1].lower()
+
+    if extension == "pdf":
         return extract_text_from_pdf(content)
-    if ext == "docx":
+    elif extension == "docx":
         return extract_text_from_docx(content)
-    if ext == "txt":
+    elif extension == "txt":
         return content.decode("utf-8").strip()
-    raise ValueError("Type de fichier non supporté")
+    else:
+        raise ValueError("Type de fichier non supporté")
+
+# 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)
+
+# Résumé
+@app.post("/analyze")
+async def analyze_file(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}
+
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": f"Erreur lors de l'analyse: {str(e)}"})
+
+# Question-Réponse
+@app.post("/ask")
+async def ask_question(file: UploadFile = File(...), question: str = Form(...)):
+    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 = ""
+
+        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]
+
+        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.")}
+
+    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(...)):
+    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
+        else:
+            description = "Description non trouvée."
 
-# … (gardez vos trois routes /analyze, /ask, /interpret_image identiques)
+        return {"description": description}
 
-@app.get("/api/health")
-async def health_check():
-    return {"status": "healthy", "version": "1.0.0", "hf_token_set": True}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": f"Erreur lors de l'interprétation de l'image: {str(e)}"})
 
+# Démarrage local
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=PORT)
+    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)