Update app.py

app.py

@@ -17,7 +17,7 @@ if not HF_TOKEN:
 client = InferenceClient(token=HF_TOKEN)
 
 # --- 2. The Core Logic ---
-def generate_question_paper(pdf_file, difficulty, num_questions):
+def generate_question_paper(pdf_file, difficulty, num_questions, progress=gr.Progress()):
     if not pdf_file:
         return "❌ Please upload a PDF file first."
     
@@ -26,27 +26,34 @@ def generate_question_paper(pdf_file, difficulty, num_questions):
     
     try:
         # A. Load PDF
+        progress(0, desc="📄 PDF file uploaded, accessing file...")
         loader = PyPDFLoader(pdf_file.name)
         pages = loader.load()
         
         if not pages:
             return "❌ Error: Could not extract text from PDF. Please ensure it's a valid PDF with text content."
         
+        progress(0.15, desc="✅ PDF loaded successfully, extracting text...")
+        
         # B. Split Text
         text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1000,
             chunk_overlap=100
         )
         chunks = text_splitter.split_documents(pages)
+        progress(0.3, desc="📝 Text extracted, preparing embeddings...")
         
         # C. Vector Store (FAISS)
         embeddings = FastEmbedEmbeddings()
+        progress(0.4, desc="🧠 Creating knowledge base...")
         vector_store = FAISS.from_documents(chunks, embeddings)
+        progress(0.5, desc="✅ Knowledge base ready, analyzing content...")
         
         # D. Retrieve Context
         retriever = vector_store.as_retriever(search_kwargs={"k": 7})
         context_docs = retriever.invoke("Key concepts and definitions")
         context_text = "\n\n".join([doc.page_content for doc in context_docs])
+        progress(0.6, desc="🎯 Key concepts identified, activating AI model...")
         
         # E. Create Prompt
         prompt = f"""You are an expert academic examiner. Create a formal Question Paper based ONLY on the context provided below.
@@ -66,9 +73,11 @@ INSTRUCTIONS:
 Do not output conversational text. Output ONLY the exam paper in a well-formatted structure."""
         
         # F. Generate using chat completion with a supported model
+        progress(0.7, desc="🤖 AI model activated, generating questions...")
         messages = [{"role": "user", "content": prompt}]
         
         response = ""
+        token_count = 0
         for message in client.chat_completion(
             messages=messages,
             model="meta-llama/Llama-3.2-3B-Instruct",
@@ -79,16 +88,21 @@ Do not output conversational text. Output ONLY the exam paper in a well-formatte
             if hasattr(message, 'choices') and len(message.choices) > 0:
                 if hasattr(message.choices[0], 'delta') and hasattr(message.choices[0].delta, 'content'):
                     response += message.choices[0].delta.content or ""
+                    token_count += 1
+                    # Update progress from 70% to 95% during generation
+                    progress_val = min(0.7 + (token_count / 2000) * 0.25, 0.95)
+                    progress(progress_val, desc=f"✍️ Generating question paper... {int((progress_val - 0.7) / 0.25 * 100)}%")
         
+        progress(1.0, desc="✅ Question paper generated successfully!")
         return response
 
     except Exception as e:
         return f"❌ Error: {str(e)}\n\nPlease check:\n1. PDF is valid and contains text\n2. HF_TOKEN is correctly set in Space secrets\n3. Try again or contact support"
 
 # --- 3. The UI ---
-with gr.Blocks(title="AI Question Paper Generator") as demo:
+with gr.Blocks(title="AI Question Paper Generator", theme=gr.themes.Soft(primary_hue="blue")) as demo:
     gr.Markdown("# 📄 AI Question Paper Generator")
-    gr.Markdown("Powered by **Fine Tuned Llama 3.2 3B**")
+    gr.Markdown("Powered by **Llama 3.2 3B** via Hugging Face Inference API")
     gr.Markdown("⚡ Fast • 🎯 Accurate • 📚 Context-Aware")
     
     with gr.Row():
@@ -128,5 +142,11 @@ with gr.Blocks(title="AI Question Paper Generator") as demo:
         outputs=output
     )
     
+    gr.Markdown("""
+    ---
+    **Note:** Set `HF_TOKEN` in your Space's Settings → Repository secrets.
+    Get your token from https://huggingface.co/settings/tokens
+    """)
+
 if __name__ == "__main__":
     demo.launch()