Update app.py

app.py

@@ -2,12 +2,11 @@ import gradio as gr
 import os
 import time
 from cerebras.cloud.sdk import Cerebras
-import markdown
 
 # Set up the Cerebras client
 client = Cerebras(api_key=os.getenv("CEREBRAS_API_KEY"))
 
-def chat_with_cerebras(user_input, system_prompt, model, temperature, top_p, max_completion_tokens):
+def chat_with_cerebras(user_input):
     """
     Handles interaction with the Cerebras model.
     Sends user input and returns the model's response along with compute time and chain-of-thought reasoning.
@@ -19,14 +18,14 @@ def chat_with_cerebras(user_input, system_prompt, model, temperature, top_p, max
         # Create a chat stream with Cerebras
         stream = client.chat.completions.create(
             messages=[
-                {"role": "system", "content": system_prompt},
+                {"role": "system", "content": "You are IntellijMind, an advanced AI designed to assist users with detailed insights, problem-solving, and chain-of-thought reasoning."},
                 {"role": "user", "content": user_input}
             ],
-            model=model,
+            model="llama-3.3-70b",
             stream=True,
-            max_completion_tokens=max_completion_tokens,
-            temperature=temperature,
-            top_p=top_p
+            max_completion_tokens=1024,
+            temperature=0.2,
+            top_p=1
         )
 
         # Collect response from the stream
@@ -41,20 +40,15 @@ def chat_with_cerebras(user_input, system_prompt, model, temperature, top_p, max
         # End compute time measurement
         compute_time = time.time() - start_time
 
-        # Improved formatting for chain of thought
-        formatted_response = response
-        if chain_of_thought:
-           formatted_response += f"\n\n**Chain of Thought:**\n{chain_of_thought}"
-
-        return formatted_response, chain_of_thought, f"Compute Time: {compute_time:.2f} seconds"
+        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 "Error: Unable to process your request.", "", str(e)
 
 # Gradio interface
 def gradio_ui():
     with gr.Blocks() as demo:
-        gr.Markdown("""# 🚀 IntellijMind Release 1st \nExperience the most advanced chatbot for deep insights and unmatched clarity!""")
+        gr.Markdown("""# 🚀 IntellijMind: The Future of AI Chatbots\nExperience the most advanced chatbot for deep insights, chain-of-thought reasoning, and unmatched clarity!""")
 
         with gr.Row():
             with gr.Column(scale=6):
@@ -67,39 +61,21 @@ def gradio_ui():
         send_button = gr.Button("Send", variant="primary")
         clear_button = gr.Button("Clear Chat")
 
-         # 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. Provide your answers in markdown format. If you do not know the answer, mention that you do not know and don't make things up. Also, remember to be concise and get straight to the point without unnecessary fluff."""
-        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))
-            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, markdown.markdown(ai_response))  # render markdown output to HTML
-            yield chat_history, chain_of_thought, compute_info
-
+            ai_response, chain_of_thought, compute_info = chat_with_cerebras(user_input)
+            chat_history.append((user_input, ai_response))
+            return chat_history, chain_of_thought, compute_info
 
         def clear_chat():
             return [], "", ""
 
-        send_button.click(
-            handle_chat,
-            inputs=[chat_history, user_input],
-            outputs=[chat_history, chain_of_thought_display, compute_time]
-        )
+        send_button.click(handle_chat, 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 Release 1st**: Setting new standards for AI interaction.\n""")
-
-        gr.Markdown("""\n\n## About\nThis project was created by Aniket Kumar as a showcase of AI capabilities with Cerebras. Feel free to explore and share!""")
+        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
 
 # Run the Gradio app
 demo = gradio_ui()
-demo.launch()
+demo.launch()