Update main.py

main.py

@@ -1,4 +1,5 @@
 import os, io
+from pathlib import Path
 from fastapi import FastAPI, UploadFile, File, Form
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, HTMLResponse, FileResponse
@@ -12,13 +13,13 @@ from io import BytesIO
 # -----------------------------------------------------------------------------
 # CONFIGURATION
 # -----------------------------------------------------------------------------
-HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")                # injected as a secret in HF Spaces
-PORT               = int(os.getenv("PORT", 7860))        # default for local, HF Spaces overrides
+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
 
 app = FastAPI(
-    title="AI‑Powered Web‑App API",
-    description="Backend endpoints for summarisation, captioning and QA",
-    version="1.2.0",
+    title="AI-Powered Web-App API",
+    description="Backend for summarisation, captioning & QA",
+    version="1.2.1",
 )
 
 app.add_middleware(
@@ -29,21 +30,22 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Serve optional static assets **only if the folder exists**
-from pathlib import Path
+# -----------------------------------------------------------------------------
+# OPTIONAL STATIC FILES (only if ./static exists)
+# -----------------------------------------------------------------------------
 static_dir = Path("static")
 if static_dir.exists():
-    app.mount("/static", StaticFiles(directory="static"), name="static"), name="static")
+    app.mount("/static", StaticFiles(directory=str(static_dir)), name="static")
 
 # -----------------------------------------------------------------------------
-# MODEL CLIENTS (remote Hugging Face Inference API)
+# HUGGING FACE INFERENCE CLIENTS
 # -----------------------------------------------------------------------------
 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
+# UTILITIES
 # -----------------------------------------------------------------------------
 
 def extract_text_from_pdf(content: bytes) -> str:
@@ -56,12 +58,12 @@ def extract_text_from_docx(content: bytes) -> str:
 
 def process_uploaded_file(file: UploadFile) -> str:
     content   = file.file.read()
-    extension = file.filename.split(".")[-1].lower()
-    if extension == "pdf":
+    ext = file.filename.split(".")[-1].lower()
+    if ext == "pdf":
         return extract_text_from_pdf(content)
-    if extension == "docx":
+    if ext == "docx":
         return extract_text_from_docx(content)
-    if extension == "txt":
+    if ext == "txt":
         return content.decode("utf-8").strip()
     raise ValueError("Unsupported file type")
 
@@ -71,10 +73,10 @@ def process_uploaded_file(file: UploadFile) -> str:
 
 @app.get("/", response_class=HTMLResponse)
 async def serve_index():
-    """Serve the frontend HTML file."""
+    """Return the frontend HTML page."""
     return FileResponse("index.html")
 
-# ---------- Summarisation -----------------------------------------------------
+# -------------------- Summarisation ------------------------------------------
 
 @app.post("/api/summarize")
 async def summarize_document(file: UploadFile = File(...)):
@@ -84,7 +86,6 @@ async def summarize_document(file: UploadFile = File(...)):
             return {"result": "Document too short to summarise."}
 
         summary_raw = summary_client.summarization(text[:3000])
-        # Normalise to plain string
         if isinstance(summary_raw, list):
             summary_txt = summary_raw[0].get("summary_text", str(summary_raw))
         elif isinstance(summary_raw, dict):
@@ -96,39 +97,36 @@ async def summarize_document(file: UploadFile = File(...)):
     except Exception as exc:
         return JSONResponse(status_code=500, content={"error": f"Summarisation failure: {exc}"})
 
-# ---------- Image Caption -----------------------------------------------------
+# -------------------- Image Caption -----------------------------------------
 
 @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))
-        buf = BytesIO(); image_pil.save(buf, format="JPEG"); img = buf.getvalue()
-
-        result = image_caption_client.image_to_text(img)
+        img_bytes = await file.read()
+        img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+        img.thumbnail((1024, 1024))
+        buf = BytesIO(); img.save(buf, format="JPEG")
+        result = image_caption_client.image_to_text(buf.getvalue())
         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:
             caption = str(result)
-
         return {"result": caption}
     except Exception as exc:
         return JSONResponse(status_code=500, content={"error": f"Caption failure: {exc}"})
 
-# ---------- Question Answering ----------------------------------------------
+# -------------------- Question Answering ------------------------------------
 
 @app.post("/api/qa")
 async def question_answering(file: UploadFile = File(...), question: str = Form(...)):
     try:
-        # If it's an image, first caption it to build context
         if file.content_type.startswith("image/"):
-            image_bytes = await file.read()
-            pil = Image.open(io.BytesIO(image_bytes)).convert("RGB"); pil.thumbnail((1024, 1024))
-            b = BytesIO(); pil.save(b, format="JPEG"); img = b.getvalue()
-            res = image_caption_client.image_to_text(img)
+            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())
             context = res.get("generated_text") if isinstance(res, dict) else str(res)
         else:
             context = process_uploaded_file(file)[:3000]
@@ -141,15 +139,11 @@ async def question_answering(file: UploadFile = File(...), question: str = Form(
     except Exception as exc:
         return JSONResponse(status_code=500, content={"error": f"QA failure: {exc}"})
 
-# ---------- Health check ------------------------------------------------------
+# -------------------- Health -------------------------------------------------
 
 @app.get("/api/health")
 async def health():
-    return {
-        "status": "healthy",
-        "hf_token_set": bool(HUGGINGFACE_TOKEN),
-        "version": app.version,
-    }
+    return {"status": "healthy", "hf_token_set": bool(HUGGINGFACE_TOKEN), "version": app.version}
 
 # -----------------------------------------------------------------------------
 # ENTRYPOINT