Update app.py

app.py

@@ -9,7 +9,6 @@ from sklearn.metrics.pairwise import cosine_similarity
 
 TOGETHER_API_KEY = os.environ.get("TOGETHER_API_KEY")
 SERPER_API_KEY = os.environ.get("SERPER_API_KEY")
-
 model = SentenceTransformer("all-MiniLM-L6-v2")
 doc_chunks = []
 doc_embeddings = []
@@ -27,9 +26,17 @@ def split_into_chunks(text, chunk_size=300):
 # --- Embed all chunks and cache ---
 def process_uploaded_file(file):
     global doc_chunks, doc_embeddings
-    text = extract_pdf_text(file)
-    doc_chunks = split_into_chunks(text)
-    doc_embeddings = model.encode(doc_chunks)
+    if file is None:
+        return "⚠️ No file selected", gr.update(visible=False)
+    
+    try:
+        text = extract_pdf_text(file)
+        doc_chunks = split_into_chunks(text)
+        doc_embeddings = model.encode(doc_chunks)
+        status = f"✅ Successfully processed {len(doc_chunks)} chunks from your document!"
+        return status, gr.update(visible=True, value=f"📄 Document loaded: {len(doc_chunks)} chunks ready")
+    except Exception as e:
+        return f"❌ Error processing file: {str(e)}", gr.update(visible=False)
 
 # --- RAG from file ---
 def retrieve_relevant_chunks(query):
@@ -46,7 +53,7 @@ def call_together_llm(context, question):
         "Content-Type": "application/json"
     }
     messages = [
-        {"role": "system", "content": "You are a helpful assistant answering from the given context."},
+        {"role": "system", "content": "You are a helpful assistant answering from the given context. Provide detailed, accurate responses based on the context provided."},
         {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}"}
     ]
     data = {
@@ -69,35 +76,244 @@ def web_search(query):
     return "\n".join([f"{r['title']} - {r['link']}\n{r['snippet']}" for r in results[:3]])
 
 # --- Main Chat Logic ---
-def answer_question(question, source):
+def answer_question(question, source, history):
+    if not question.strip():
+        return history, ""
+    
     try:
-        if source == "Web Search":
+        # Add user question to history
+        history = history + [[question, None]]
+        
+        if source == "🌐 Web Search":
             context = web_search(question)
-        elif source == "Uploaded File":
+            source_info = "🌐 **Source:** Web Search"
+        elif source == "📄 Uploaded File":
             if not doc_chunks:
-                return "❌ Please upload a document first."
+                answer = "❌ Please upload a PDF document first to use this feature."
+                history[-1][1] = answer
+                return history, ""
             context = retrieve_relevant_chunks(question)
+            source_info = "📄 **Source:** Uploaded Document"
         else:
-            return "❌ Unknown source selected."
+            answer = "❌ Please select a valid knowledge source."
+            history[-1][1] = answer
+            return history, ""
+        
+        # Get answer from LLM
+        answer = call_together_llm(context, question)
+        formatted_answer = f"{source_info}\n\n{answer}"
+        
+        # Update history with answer
+        history[-1][1] = formatted_answer
+        
+        return history, ""
         
-        return call_together_llm(context, question)
     except Exception as e:
-        return f"❌ Error: {e}"
+        error_msg = f"❌ **Error:** {str(e)}\n\nPlease check your API keys and try again."
+        history[-1][1] = error_msg
+        return history, ""
 
-# --- Gradio UI ---
-with gr.Blocks() as demo:
-    gr.Markdown("# 🔍 RAG Chatbot (Web + File Powered)")
+# --- Clear chat history ---
+def clear_chat():
+    return []
 
-    with gr.Row():
-        source_choice = gr.Radio(["Web Search", "Uploaded File"], label="Select Knowledge Source", value="Web Search")
+# --- Custom CSS ---
+custom_css = """
+.gradio-container {
+    max-width: 1200px !important;
+    margin: auto !important;
+}
 
-    file_input = gr.File(label="📁 Upload PDF File", visible=True, file_types=[".pdf"])
+.header-text {
+    text-align: center;
+    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    font-size: 2.5em;
+    font-weight: bold;
+    margin-bottom: 10px;
+}
 
-    with gr.Row():
-        question_input = gr.Textbox(label="Ask a question", placeholder="Ask anything...")
-        answer_output = gr.Textbox(label="Answer", lines=8)
+.subtitle-text {
+    text-align: center;
+    color: #666;
+    font-size: 1.2em;
+    margin-bottom: 30px;
+}
 
-    file_input.change(fn=process_uploaded_file, inputs=file_input, outputs=[])
-    question_input.submit(fn=answer_question, inputs=[question_input, source_choice], outputs=answer_output)
+.source-radio .wrap {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    border-radius: 15px;
+    padding: 15px;
+    margin: 10px 0;
+}
+
+.source-radio label {
+    color: white !important;
+    font-weight: 600;
+}
+
+.upload-area {
+    border: 2px dashed #667eea;
+    border-radius: 15px;
+    padding: 20px;
+    text-align: center;
+    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+    transition: all 0.3s ease;
+}
+
+.upload-area:hover {
+    border-color: #764ba2;
+    transform: translateY(-2px);
+}
+
+.chat-container {
+    border-radius: 15px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
+    background: white;
+    padding: 20px;
+    margin: 20px 0;
+}
+
+.status-box {
+    background: linear-gradient(135deg, #84fab0 0%, #8fd3f4 100%);
+    border-radius: 10px;
+    padding: 15px;
+    margin: 10px 0;
+    border: none;
+    color: #2d3748;
+    font-weight: 500;
+}
+
+.footer-text {
+    text-align: center;
+    color: #888;
+    font-size: 0.9em;
+    margin-top: 30px;
+    padding: 20px;
+    border-top: 1px solid #eee;
+}
+"""
+
+# --- Enhanced Gradio UI ---
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="🤖 RAG Chatbot") as demo:
+    
+    # Header
+    gr.HTML("""
+        <div class="header-text">🤖 Intelligent RAG Chatbot</div>
+        <div class="subtitle-text">Ask questions from web or upload your documents for AI-powered answers</div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Knowledge Source Selection
+            gr.Markdown("### 🎯 **Choose Your Knowledge Source**")
+            source_choice = gr.Radio(
+                ["🌐 Web Search", "📄 Uploaded File"], 
+                label="Select Knowledge Source", 
+                value="🌐 Web Search",
+                elem_classes=["source-radio"]
+            )
+            
+            # File Upload Section
+            gr.Markdown("### 📁 **Document Upload**")
+            file_input = gr.File(
+                label="Upload PDF Document", 
+                file_types=[".pdf"],
+                elem_classes=["upload-area"]
+            )
+            
+            file_status = gr.Textbox(
+                label="📊 Processing Status",
+                interactive=False,
+                elem_classes=["status-box"]
+            )
+            
+            document_info = gr.Textbox(
+                label="📄 Document Info",
+                visible=False,
+                interactive=False,
+                elem_classes=["status-box"]
+            )
+            
+        with gr.Column(scale=2):
+            # Chat Interface
+            gr.Markdown("### 💬 **Chat Interface**")
+            
+            chatbot = gr.Chatbot(
+                label="Conversation",
+                height=500,
+                elem_classes=["chat-container"],
+                bubble_full_width=False,
+                show_label=False
+            )
+            
+            with gr.Row():
+                question_input = gr.Textbox(
+                    label="Ask your question",
+                    placeholder="Type your question here... (Press Enter to send)",
+                    lines=2,
+                    scale=4
+                )
+                
+                with gr.Column(scale=1, min_width=100):
+                    send_btn = gr.Button("🚀 Send", variant="primary", size="lg")
+                    clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="lg")
+    
+    # Advanced Settings (Collapsible)
+    with gr.Accordion("⚙️ Advanced Settings", open=False):
+        gr.Markdown("""
+        **API Configuration:**
+        - Ensure your `TOGETHER_API_KEY` environment variable is set
+        - Ensure your `SERPER_API_KEY` environment variable is set for web search
+        
+        **Features:**
+        - 🌐 **Web Search**: Get real-time information from the internet
+        - 📄 **Document Upload**: Upload PDF files and ask questions about their content
+        - 🤖 **AI-Powered**: Uses Mixtral-8x7B model for intelligent responses
+        - 🔍 **Semantic Search**: Advanced embedding-based document retrieval
+        """)
+    
+    # Footer
+    gr.HTML("""
+        <div class="footer-text">
+            🚀 Powered by Together AI & Serper API | 
+            📚 Built with Sentence Transformers & Gradio |
+            💡 Enhanced RAG System
+        </div>
+    """)
+    
+    # Event Handlers
+    file_input.change(
+        fn=process_uploaded_file, 
+        inputs=file_input, 
+        outputs=[file_status, document_info]
+    )
+    
+    # Send message on button click or Enter key
+    question_input.submit(
+        fn=answer_question, 
+        inputs=[question_input, source_choice, chatbot], 
+        outputs=[chatbot, question_input]
+    )
+    
+    send_btn.click(
+        fn=answer_question, 
+        inputs=[question_input, source_choice, chatbot], 
+        outputs=[chatbot, question_input]
+    )
+    
+    clear_btn.click(
+        fn=clear_chat,
+        inputs=[],
+        outputs=[chatbot]
+    )
 
-demo.launch()
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )