Update main.py

main.py

@@ -13,13 +13,13 @@ from io import BytesIO
 # -----------------------------------------------------------------------------
 # CONFIGURATION
 # -----------------------------------------------------------------------------
-HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")                # set in HF Space secrets or env
-PORT               = int(os.getenv("PORT", 7860))        # Spaces auto-set PORT; default 7860 locally
+HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")
+PORT = int(os.getenv("PORT", 7860))
 
 app = FastAPI(
-    title="AI-Powered Web-App API",
+    title="AI‑Powered Web‑App API",
     description="Backend for summarisation, captioning & QA",
-    version="1.2.1",
+    version="1.2.2",
 )
 
 app.add_middleware(
@@ -31,7 +31,7 @@ app.add_middleware(
 )
 
 # -----------------------------------------------------------------------------
-# OPTIONAL STATIC FILES (only if ./static exists)
+# OPTIONAL STATIC FILES
 # -----------------------------------------------------------------------------
 static_dir = Path("static")
 if static_dir.exists():
@@ -40,8 +40,8 @@ if static_dir.exists():
 # -----------------------------------------------------------------------------
 # HUGGING FACE INFERENCE CLIENTS
 # -----------------------------------------------------------------------------
-summary_client       = InferenceClient("facebook/bart-large-cnn",              token=HUGGINGFACE_TOKEN)
-qa_client            = InferenceClient("deepset/roberta-base-squad2",          token=HUGGINGFACE_TOKEN)
+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)
 
 # -----------------------------------------------------------------------------
@@ -57,7 +57,7 @@ def extract_text_from_docx(content: bytes) -> str:
     return "\n".join(p.text for p in doc.paragraphs).strip()
 
 def process_uploaded_file(file: UploadFile) -> str:
-    content   = file.file.read()
+    content = file.file.read()
     ext = file.filename.split(".")[-1].lower()
     if ext == "pdf":
         return extract_text_from_pdf(content)
@@ -73,36 +73,31 @@ def process_uploaded_file(file: UploadFile) -> str:
 
 @app.get("/", response_class=HTMLResponse)
 async def serve_index():
-    """Return the frontend HTML page."""
     return FileResponse("index.html")
 
 # -------------------- Summarisation ------------------------------------------
-
 @app.post("/api/summarize")
 async def summarize_document(file: UploadFile = File(...)):
     try:
         text = process_uploaded_file(file)
         if len(text) < 20:
             return {"result": "Document too short to summarise."}
-
         summary_raw = summary_client.summarization(text[:3000])
-        if isinstance(summary_raw, list):
-            summary_txt = summary_raw[0].get("summary_text", str(summary_raw))
-        elif isinstance(summary_raw, dict):
-            summary_txt = summary_raw.get("summary_text", str(summary_raw))
-        else:
-            summary_txt = str(summary_raw)
-
+        summary_txt = (
+            summary_raw[0].get("summary_text") if isinstance(summary_raw, list) else
+            summary_raw.get("summary_text")   if isinstance(summary_raw, dict) else
+            str(summary_raw)
+        )
         return {"result": summary_txt}
     except Exception as exc:
         return JSONResponse(status_code=500, content={"error": f"Summarisation failure: {exc}"})
 
 # -------------------- Image Caption -----------------------------------------
-
 @app.post("/api/caption")
-async def caption_image(file: UploadFile = File(...)):
+async def caption_image(image: UploadFile = File(...)):
+    """`image` field name matches frontend (was `file` before)."""
     try:
-        img_bytes = await file.read()
+        img_bytes = await image.read()
         img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
         img.thumbnail((1024, 1024))
         buf = BytesIO(); img.save(buf, format="JPEG")
@@ -118,29 +113,25 @@ async def caption_image(file: UploadFile = File(...)):
         return JSONResponse(status_code=500, content={"error": f"Caption failure: {exc}"})
 
 # -------------------- Question Answering ------------------------------------
-
 @app.post("/api/qa")
 async def question_answering(file: UploadFile = File(...), question: str = Form(...)):
     try:
         if file.content_type.startswith("image/"):
             img_bytes = await file.read()
             img = Image.open(io.BytesIO(img_bytes)).convert("RGB"); img.thumbnail((1024, 1024))
-            b = BytesIO(); img.save(b, format="JPEG")
-            res = image_caption_client.image_to_text(b.getvalue())
+            buf = BytesIO(); img.save(buf, format="JPEG")
+            res = image_caption_client.image_to_text(buf.getvalue())
             context = res.get("generated_text") if isinstance(res, dict) else str(res)
         else:
             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": answer.get("answer", "No answer found.")}
     except Exception as exc:
         return JSONResponse(status_code=500, content={"error": f"QA failure: {exc}"})
 
 # -------------------- Health -------------------------------------------------
-
 @app.get("/api/health")
 async def health():
     return {"status": "healthy", "hf_token_set": bool(HUGGINGFACE_TOKEN), "version": app.version}
@@ -148,7 +139,6 @@ async def health():
 # -----------------------------------------------------------------------------
 # ENTRYPOINT
 # -----------------------------------------------------------------------------
-
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=PORT)