Update app.py

app.py

@@ -3,175 +3,32 @@ import gradio as gr
 from gradio import ChatMessage
 from typing import Iterator
 import google.generativeai as genai
-
-# get Gemini API Key from the environ variable
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=GEMINI_API_KEY)
-
-# we will be using the Gemini 2.0 Flash model with Thinking capabilities
-model = genai.GenerativeModel("gemini-2.0-flash-thinking-exp-1219")
-
-
-def format_chat_history(messages: list) -> list:
-    """
-    Formats the chat history into a structure Gemini can understand
-    """
-    formatted_history = []
-    for message in messages:
-        # Skip thinking messages (messages with metadata)
-        if not (message.get("role") == "assistant" and "metadata" in message):
-            formatted_history.append({
-                "role": "user" if message.get("role") == "user" else "assistant",
-                "parts": [message.get("content", "")]
-            })
-    return formatted_history
-
-def stream_gemini_response(user_message: str, messages: list) -> Iterator[list]:
-    """
-    Streams thoughts and response with conversation history support.
-    """
-    try:
-        print(f"\n=== New Request ===")
-        print(f"User message: {user_message}")
-        
-        # Format chat history for Gemini
-        chat_history = format_chat_history(messages)
-        
-        # Initialize Gemini chat
-        chat = model.start_chat(history=chat_history)
-        response = chat.send_message(user_message, stream=True)
-        
-        # Initialize buffers and flags
-        thought_buffer = ""
-        response_buffer = ""
-        thinking_complete = False
-        
-        # Add initial thinking message
-        messages.append(
-            ChatMessage(
-                role="assistant",
-                content="",
-                metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-            )
-        )
-        
-        for chunk in response:
-            parts = chunk.candidates[0].content.parts
-            current_chunk = parts[0].text
-            
-            if len(parts) == 2 and not thinking_complete:
-                # Complete thought and start response
-                thought_buffer += current_chunk
-                print(f"\n=== Complete Thought ===\n{thought_buffer}")
-                
-                messages[-1] = ChatMessage(
-                    role="assistant",
-                    content=thought_buffer,
-                    metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-                )
-                yield messages
-                
-                # Start response
-                response_buffer = parts[1].text
-                print(f"\n=== Starting Response ===\n{response_buffer}")
-                
-                messages.append(
-                    ChatMessage(
-                        role="assistant",
-                        content=response_buffer
-                    )
-                )
-                thinking_complete = True
-                
-            elif thinking_complete:
-                # Stream response
-                response_buffer += current_chunk
-                print(f"\n=== Response Chunk ===\n{current_chunk}")
-                
-                messages[-1] = ChatMessage(
-                    role="assistant",
-                    content=response_buffer
-                )
-                
-            else:
-                # Stream thinking
-                thought_buffer += current_chunk
-                print(f"\n=== Thinking Chunk ===\n{current_chunk}")
-                
-                messages[-1] = ChatMessage(
-                    role="assistant",
-                    content=thought_buffer,
-                    metadata={"title": "⚙️ Thinking: *The thoughts produced by the model are experimental"}
-                )
-            
-            yield messages
-            
-        print(f"\n=== Final Response ===\n{response_buffer}")
-                
-    except Exception as e:
-        print(f"\n=== Error ===\n{str(e)}")
-        messages.append(
-            ChatMessage(
-                role="assistant",
-                content=f"I apologize, but I encountered an error: {str(e)}"
-            )
+import random
+
+example_code = """
+Here's an example Python lambda function:
+
+lambda x: x + {}
+
+Is this correct?
+"""
+
+def chat(message, history):
+    if message == "Yes, that's correct.":
+        return "Great!"
+    else:
+        return gr.ChatMessage(
+            content=example_code.format(random.randint(1, 100)),
+            options=[
+                {"value": "Yes, that's correct.", "label": "Yes"},
+                {"value": "No"}
+            ]
         )
-        yield messages
-
-def user_message(msg: str, history: list) -> tuple[str, list]:
-    """Adds user message to chat history"""
-    history.append(ChatMessage(role="user", content=msg))
-    return "", history
-    
-
-# Create the Gradio interface
-with gr.Blocks(theme=gr.themes.Citrus(), fill_height=True) as demo:
-  #with gr.Column():
-    gr.Markdown("# Chat with Gemini 2.0 Flash and See its Thoughts 💭")
-
-    chatbot = gr.Chatbot(
-        type="messages",
-        label="Gemini2.0 'Thinking' Chatbot",
-        render_markdown=True,
-        scale=1,
-        avatar_images=(None,"https://lh3.googleusercontent.com/oxz0sUBF0iYoN4VvhqWTmux-cxfD1rxuYkuFEfm1SFaseXEsjjE4Je_C_V3UQPuJ87sImQK3HfQ3RXiaRnQetjaZbjJJUkiPL5jFJ1WRl5FKJZYibUA=w214-h214-n-nu")
-    )
-
-    with gr.Row(equal_height=True):
-        input_box = gr.Textbox(
-            lines=1,
-            label="Chat Message",
-            placeholder="Type your message here...",
-            scale=4
-        )
-
-        clear_button = gr.Button("Clear Chat", scale=1)
-
-    # Set up event handlers
-    msg_store = gr.State("")  # Store for preserving user message
-    
-    input_box.submit(
-        lambda msg: (msg, msg, ""),  # Store message and clear input
-        inputs=[input_box],
-        outputs=[msg_store, input_box, input_box],
-        queue=False
-    ).then(
-        user_message,  # Add user message to chat
-        inputs=[msg_store, chatbot],
-        outputs=[input_box, chatbot],
-        queue=False
-    ).then(
-        stream_gemini_response,  # Generate and stream response
-        inputs=[msg_store, chatbot],
-        outputs=chatbot
-    )
 
-    clear_button.click(
-        lambda: ([], "", ""),
-        outputs=[chatbot, input_box, msg_store],
-        queue=False
-    )
+demo = gr.ChatInterface(
+    chat,
+    type="messages",
+    examples=["Write an example Python lambda function."]
+)
 
-# Launch the interface
-if __name__ == "__main__":
-    demo.launch(debug=True)
+demo.launch()