Update app.py

app.py

@@ -5,243 +5,134 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
 from langchain_community.vectorstores import FAISS
 from huggingface_hub import InferenceClient
+from langchain_core.prompts import ChatPromptTemplate
 
-# --- 1. Configuration & Setup ---
+# --- 1. Model Setup using HF Inference Client ---
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
 
-# Use InferenceClient
+if not HF_TOKEN:
+    print("⚠️ Warning: HF_TOKEN not set. The app may not work properly.")
+
+# Use InferenceClient directly instead of LangChain wrapper
 client = InferenceClient(token=HF_TOKEN)
 
-# --- 2. Logic: Paper Generation ---
-def generate_question_paper(pdf_file, difficulty, num_questions, progress=gr.Progress()):
+# --- 2. The Core Logic ---
+def generate_question_paper(pdf_file, difficulty, num_questions):
     if not pdf_file:
-        raise gr.Error("Please upload a PDF file first.")
+        return "❌ Please upload a PDF file first."
     
     if not HF_TOKEN:
-        raise gr.Error("HF_TOKEN is missing. Please check your Space settings.")
-
+        return "❌ Error: HF_TOKEN not configured. Please add your Hugging Face token in Space Settings > Repository secrets."
+    
     try:
-        # Step 1: Loading
-        progress(0.1, desc="Reading PDF...")
+        # A. Load PDF
         loader = PyPDFLoader(pdf_file.name)
         pages = loader.load()
         
         if not pages:
-            raise gr.Error("The PDF appears to be empty or unreadable.")
-
-        # Step 2: Splitting
-        progress(0.3, desc="Analyzing text structure...")
+            return "❌ Error: Could not extract text from PDF. Please ensure it's a valid PDF with text content."
+        
+        # B. Split Text
         text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1000,
             chunk_overlap=100
         )
         chunks = text_splitter.split_documents(pages)
-
-        # Step 3: Embedding
-        progress(0.5, desc="Building semantic index...")
+        
+        # C. Vector Store (FAISS)
         embeddings = FastEmbedEmbeddings()
         vector_store = FAISS.from_documents(chunks, embeddings)
-
-        # Step 4: Retrieval
-        progress(0.7, desc="Extracting key concepts...")
+        
+        # D. Retrieve Context
         retriever = vector_store.as_retriever(search_kwargs={"k": 7})
-        # We broaden the search query to get a general overview
-        context_docs = retriever.invoke("Chapter summary, definitions, and key concepts")
+        context_docs = retriever.invoke("Key concepts and definitions")
         context_text = "\n\n".join([doc.page_content for doc in context_docs])
-
-        # Step 5: Generation
-        progress(0.8, desc="Drafting questions...")
         
-        prompt = f"""
-        Role: Academic Examiner.
-        Task: Create a structured examination paper.
-        Source Material: Use the context below.
+        # E. Create Prompt
+        prompt = f"""You are an expert academic examiner. Create a formal Question Paper based ONLY on the context provided below.
+
+CONTEXT:
+{context_text}
+
+INSTRUCTIONS:
+- Difficulty: {difficulty}
+- Total Questions: {num_questions}
+- Format:
+  Section A: Multiple Choice Questions (MCQs)
+  Section B: Short Answer Questions
+  Section C: Long Answer/Essay Questions
+- Provide the Answer Key for MCQs at the very end.
+
+Do not output conversational text. Output ONLY the exam paper in a well-formatted structure."""
         
-        CONTEXT:
-        {context_text}
-
-        REQUIREMENTS:
-        - Difficulty: {difficulty}
-        - Total Questions: {num_questions}
-        - Structure:
-          1. HEADER (University/Course Name Placeholder, Duration: 1 Hour)
-          2. SECTION A: Multiple Choice ({int(num_questions * 0.4)} questions)
-          3. SECTION B: Short Answer ({int(num_questions * 0.4)} questions)
-          4. SECTION C: Essay/Long Answer ({int(num_questions * 0.2)} questions)
-          5. ANSWER KEY (at the very bottom)
-
-        OUTPUT FORMAT:
-        Return ONLY the exam paper in clean Markdown. Use bold headers.
-        """
-
+        # F. Generate using chat completion with a supported model
         messages = [{"role": "user", "content": prompt}]
         
-        partial_response = ""
+        response = ""
         for message in client.chat_completion(
             messages=messages,
             model="meta-llama/Llama-3.2-3B-Instruct",
-            max_tokens=2500,
-            temperature=0.6,
+            max_tokens=2000,
+            temperature=0.7,
             stream=True,
         ):
-            if hasattr(message.choices[0].delta, 'content'):
-                content = message.choices[0].delta.content
-                if content:
-                    partial_response += content
-                    yield partial_response
-
-        progress(1.0, desc="Finalizing formatting...")
-        return partial_response
+            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 ""
+        
+        return response
 
     except Exception as e:
-        raise gr.Error(f"Generation failed: {str(e)}")
-
-# --- 3. UI Layout (MVP Styling) ---
-
-# CSS Injection for SaaS/MVP Look
-css = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;800&display=swap');
-
-.gradio-container {
-    font-family: 'Inter', sans-serif !important;
-    background-color: #f3f4f6 !important;
-}
-
-.header-container {
-    text-align: center;
-    padding: 3rem 1rem;
-    background: white;
-    border-bottom: 1px solid #e5e7eb;
-    margin-bottom: 2rem;
-}
-
-.logo-text {
-    font-size: 2rem;
-    font-weight: 800;
-    background: linear-gradient(135deg, #4f46e5 0%, #ec4899 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-}
-
-.subtitle {
-    color: #6b7280;
-    font-size: 1.1rem;
-    margin-top: 0.5rem;
-}
-
-/* Custom Panel Styling */
-.control-panel {
-    background: white !important;
-    border: 1px solid #e5e7eb !important;
-    border-radius: 12px !important;
-    padding: 1.5rem !important;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1) !important;
-}
-
-/* Paper Output Styling */
-.paper-preview {
-    background: white !important;
-    border: 1px solid #e5e7eb !important;
-    min-height: 700px !important;
-    padding: 3rem !important;
-    border-radius: 2px !important;
-    box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04) !important;
-    font-family: 'Times New Roman', serif !important; /* Academic look */
-}
+        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"
 
-/* Primary Button Styling */
-#gen-btn {
-    background: linear-gradient(135deg, #4f46e5 0%, #4338ca 100%) !important;
-    border: none !important;
-    color: white !important;
-    font-weight: 600 !important;
-    padding: 12px !important;
-    border-radius: 8px !important;
-    transition: transform 0.2s, box-shadow 0.2s !important;
-}
-
-#gen-btn:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 10px 15px -3px rgba(79, 70, 229, 0.4) !important;
-}
-"""
-
-with gr.Blocks(title="ExamGen AI", css=css) as demo:
+# --- 3. The UI ---
+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 **Llama 3.2 3B** via Hugging Face Inference API")
+    gr.Markdown("⚡ Fast • 🎯 Accurate • 📚 Context-Aware")
     
-    # Header
-    gr.HTML("""
-        <div class="header-container">
-            <div class="logo-text">ExamGen AI</div>
-            <div class="subtitle">Enterprise-grade Question Paper Generator</div>
-        </div>
-    """)
-
     with gr.Row():
-        
-        # --- Left Sidebar: Controls ---
-        with gr.Column(scale=1, elem_classes="control-panel"):
-            gr.Markdown("### ⚙️ Configuration")
-            
-            gr.Markdown("**1. Upload Material**")
+        with gr.Column(scale=1):
             pdf_input = gr.File(
-                label="",
-                file_types=[".pdf"],
-                file_count="single"
+                label="📄 Upload Study Material (PDF)",
+                file_types=[".pdf"]
             )
             
-            gr.HTML("<hr style='margin: 1.5rem 0; border-color: #f3f4f6;'>")
-            
-            gr.Markdown("**2. Exam Settings**")
-            difficulty = gr.Radio(
-                choices=["Easy", "Medium", "Hard"],
-                value="Medium",
-                label="Difficulty Level",
-                info="Determines concept depth"
-            )
-            
-            num_questions = gr.Slider(
-                minimum=5, 
-                maximum=30, 
-                value=10, 
-                step=1,
-                label="Total Questions",
-                info="Mix of MCQ, Short & Long answers"
-            )
+            with gr.Group():
+                difficulty = gr.Radio(
+                    ["Easy", "Medium", "Hard"], 
+                    label="🎚️ Difficulty Level", 
+                    value="Medium"
+                )
+                num_questions = gr.Slider(
+                    5, 20, value=10, step=1, 
+                    label="📊 Total Questions"
+                )
             
-            gr.HTML("<div style='margin-top: 1.5rem;'></div>")
+            btn = gr.Button("✨ Generate Question Paper", variant="primary", size="lg")
             
-            btn_generate = gr.Button(
-                "✨ Generate Question Paper",
-                elem_id="gen-btn",
-                size="lg"
-            )
-
-            gr.Markdown(
-                """
-                <div style="margin-top: 2rem; padding: 1rem; background: #f9fafb; border-radius: 6px; font-size: 0.8rem; color: #6b7280;">
-                    <strong>System Status:</strong><br>
-                    🟢 Model: Llama-3.2-3B<br>
-                    🟢 RAG Engine: Active
-                </div>
-                """
-            )
-
-        # --- Right Content: Paper Preview ---
+            gr.Markdown("""
+            ### 📝 Instructions:
+            1. Upload a PDF containing study material
+            2. Select difficulty level
+            3. Choose number of questions
+            4. Click Generate!
+            """)
+        
         with gr.Column(scale=2):
-            with gr.Group():
-                gr.Markdown("### 📄 Examination Paper Preview")
-                output = gr.Markdown(
-                    value="<div style='text-align: center; color: #9ca3af; margin-top: 5rem;'><i>Generated paper will appear here formatted as a document.</i></div>",
-                    elem_classes="paper-preview"
-                )
+            output = gr.Markdown(label="Generated Question Paper")
 
-    # --- Interaction Wiring ---
-    btn_generate.click(
+    btn.click(
         fn=generate_question_paper,
         inputs=[pdf_input, difficulty, num_questions],
-        outputs=[output],
-        concurrency_limit=1
+        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()