Update main.py

main.py

@@ -1,5 +1,4 @@
 import os
-import io
 from fastapi import FastAPI, UploadFile, File, Form
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, HTMLResponse
@@ -7,13 +6,17 @@ 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
 
-# Load Hugging Face Token securely
+# 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=["*"],
@@ -22,22 +25,31 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Initialize Hugging Face clients
+# 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)
 
+# Extraction du texte des fichiers
 def extract_text_from_pdf(content: bytes) -> str:
+    text = ""
     reader = PdfReader(io.BytesIO(content))
-    return "\n".join(page.extract_text() or "" for page 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(para.text for para 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()
     extension = file.filename.split('.')[-1].lower()
+
     if extension == "pdf":
         return extract_text_from_pdf(content)
     elif extension == "docx":
@@ -45,76 +57,110 @@ def process_uploaded_file(file: UploadFile) -> str:
     elif extension == "txt":
         return content.decode("utf-8").strip()
     else:
-        raise ValueError("Unsupported file type.")
+        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)
 
-@app.post("/api/summarize")
-async def summarize_document(file: UploadFile = File(...)):
+# Résumé
+@app.post("/analyze")
+async def analyze_file(file: UploadFile = File(...)):
     try:
         text = process_uploaded_file(file)
-        if len(text) < 20:
-            return {"result": "Document too short to summarize."}
-        summary = summary_client.summarization(text[:3000])
-        return {"result": summary}
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
 
-@app.post("/api/caption")
-async def caption_image(file: UploadFile = File(...)):
-    try:
-        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()
-        result = image_caption_client.image_to_text(img_byte_arr)
+        if len(text) < 20:
+            return {"summary": "Document trop court pour être résumé."}
 
-        if isinstance(result, dict):
-            caption = result.get("generated_text") or result.get("caption") or "No caption found."
-        elif isinstance(result, list) and result:
-            caption = result[0].get("generated_text", "No caption found.")
-        elif isinstance(result, str):
-            caption = result
-        else:
-            caption = "No caption found."
+        summary = summary_client.summarization(text[:3000])
+        return {"summary": summary}
 
-        return {"result": caption}
     except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
+        return JSONResponse(status_code=500, content={"error": f"Erreur lors de l'analyse: {str(e)}"})
 
-@app.post("/api/qa")
-async def question_answering(file: UploadFile = File(...), question: str = Form(...)):
+# 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)
-            context = result.get("generated_text") if isinstance(result, dict) else result
+            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 {"result": "Document too short to answer questions."}
+                return {"answer": "Document trop court pour répondre à la question."}
             context = text[:3000]
 
         if not context:
-            return {"result": "No context available to answer."}
+            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."
 
-        answer = qa_client.question_answering(question=question, context=context)
-        return {"result": answer.get("answer", "No answer found.")}
+        return {"description": description}
 
     except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(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("main:app", host="0.0.0.0", port=8000, reload=True)