Update app.py

app.py

@@ -43,12 +43,13 @@ def chat_with_cerebras(user_input, system_prompt, model, temperature, top_p, max
         return response, chain_of_thought, f"Compute Time: {compute_time:.2f} seconds"
 
     except Exception as e:
-        return f"Error: {str(e)}", "", "An error occurred. Please check your API key or the Cerebras service."
+       return f"Error: {str(e)}", "", "An error occurred. Please check your API key or the Cerebras service."
+
 
 # Gradio interface
 def gradio_ui():
     with gr.Blocks() as demo:
-        gr.Markdown("""# 🚀 IntellijMind: The Future of AI Chatbots\nExperience the most advanced chatbot for deep insights, chain-of-thought reasoning, and unmatched clarity!""")
+        gr.Markdown("""# 🚀 IntellijMind: The Future of AI Chatbots\nExperience the most advanced chatbot for deep insights and unmatched clarity!""")
 
         with gr.Row():
             with gr.Column(scale=6):
@@ -57,36 +58,39 @@ def gradio_ui():
                 compute_time = gr.Textbox(label="Compute Time", interactive=False)
                 chain_of_thought_display = gr.Textbox(label="Chain of Thought", interactive=False, lines=10)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            system_prompt_input = gr.Textbox(label="System Prompt", value="You are IntellijMind, an advanced AI designed to assist users with detailed insights, problem-solving, and chain-of-thought reasoning.", lines=3)
-            model_select = gr.Dropdown(label="Model", choices=["llama-3.3-70b", "llama-2-70b"], value="llama-3.3-70b")  # Add other model names as required
-            temperature_slider = gr.Slider(label="Temperature", minimum=0, maximum=2, step=0.1, value=0.2)
-            top_p_slider = gr.Slider(label="Top P", minimum=0, maximum=1, step=0.01, value=1)
-            max_tokens_slider = gr.Slider(label="Max Tokens", minimum=50, maximum=2048, step=50, value=1024)
-
-
         user_input = gr.Textbox(label="Type your message", placeholder="Ask me anything...", lines=2)
         send_button = gr.Button("Send", variant="primary")
         clear_button = gr.Button("Clear Chat")
 
-        def handle_chat(chat_history, user_input, system_prompt, model, temperature, top_p, max_tokens):
-            chat_history.append((user_input, None))
-            yield chat_history, "", "Thinking..."
+        # Set default values for system prompt, model, etc.
+        default_system_prompt = "You are IntellijMind, an advanced AI designed to assist users with detailed insights, problem-solving, and chain-of-thought reasoning."
+        default_model = "llama-3.3-70b"
+        default_temperature = 0.2
+        default_top_p = 1
+        default_max_tokens = 1024
+
+
+        def handle_chat(chat_history, user_input):
+           chat_history.append((user_input, None))  # Add user message immediately
+           yield chat_history, "", "Thinking..."
+
+           ai_response, chain_of_thought, compute_info = chat_with_cerebras(user_input, default_system_prompt, default_model, default_temperature, default_top_p, default_max_tokens)
+           chat_history[-1] = (user_input, ai_response) # Update the chatbot output with AI response
+           yield chat_history, chain_of_thought, compute_info
 
-            ai_response, chain_of_thought, compute_info = chat_with_cerebras(user_input, system_prompt, model, temperature, top_p, max_tokens)
-            chat_history[-1] = (user_input, ai_response)
-            yield chat_history, chain_of_thought, compute_info
 
         def clear_chat():
             return [], "", ""
 
+
         send_button.click(
             handle_chat,
-            inputs=[chat_history, user_input, system_prompt_input, model_select, temperature_slider, top_p_slider, max_tokens_slider],
+            inputs=[chat_history, user_input],
             outputs=[chat_history, chain_of_thought_display, compute_time]
         )
         clear_button.click(clear_chat, outputs=[chat_history, chain_of_thought_display, compute_time])
 
+
         gr.Markdown("""---\n### 🌟 Features:\n- **Advanced Reasoning**: Chain-of-thought explanations for complex queries.\n- **Real-Time Performance Metrics**: Measure response compute time instantly.\n- **Insightful Chain of Thought**: See the reasoning process behind AI decisions.\n- **User-Friendly Design**: Intuitive chatbot interface with powerful features.\n- **Powered by IntellijMind**: Setting new standards for AI interaction.\n""")
 
     return demo