Create app.py

app.py

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+import gradio as gr
+from gradio import ChatMessage
+import time
+
+def simulate_thinking_chat(message: str, history: list) -> list:
+    """Mimicking thinking process and response"""
+    # Add initial empty thinking message to chat history
+    history.append(  # Adds new message to the chat history list
+        ChatMessage(  # Creates a new chat message
+            role="assistant",  # Specifies this is from the assistant
+            content="",  # Initially empty content
+            metadata={"title": "💭 Thinking Process"}  # Setting a thinking header here
+        )
+    )
+    time.sleep(1)
+    yield history  # Returns current state of chat history
+    
+    # Define the thoughts that LLM will "think" through
+    thoughts = [
+        "First, I need to understand the core aspects of the query...",
+        "Now, considering the broader context and implications...",
+        "Analyzing potential approaches to formulate a comprehensive answer...",
+        "Finally, structuring the response for clarity and completeness..."
+    ]
+    
+    # Variable to store all thoughts as they accumulate
+    accumulated_thoughts = ""
+    
+    # Loop through each thought
+    for thought in thoughts:
+        time.sleep(0.5)  # Add a samll delay for realism
+        
+        # Add new thought to accumulated thoughts with markdown bullet point
+        accumulated_thoughts += f"- {thought}\n\n"  # \n\n creates line breaks
+        
+        # Update the thinking message with all thoughts so far
+        history[-1] = ChatMessage(  # Updates last message in history
+            role="assistant",
+            content=accumulated_thoughts.strip(),  # Remove extra whitespace
+            metadata={"title": "💭 Thinking Process"}  # Shows thinking header
+        )
+        yield history  # Returns updated chat history
+    
+    # After thinking is complete, adding the final response
+    history.append(
+        ChatMessage(
+            role="assistant",
+            content="Based on my thoughts and analysis above, my response is: This dummy repro shows how thoughts of a thinking LLM can be progressively shown before providing its final answer."
+        )
+    )
+    yield history  # Returns final state of chat history
+
+# Gradio blocks with gr.chatbot
+with gr.Blocks() as demo:
+    gr.Markdown("# Thinking LLM Demo 🤔")
+    chatbot = gr.Chatbot(type="messages", render_markdown=True)
+    msg = gr.Textbox(placeholder="Type your message...")
+    
+    # Handling message submission here
+    msg.submit(
+        lambda m, h: (m, h + [ChatMessage(role="user", content=m)]),
+        [msg, chatbot],
+        [msg, chatbot]
+    ).then(
+        simulate_thinking_chat,
+        [msg, chatbot],
+        chatbot
+    )
+
+if __name__ == "__main__":
+    demo.launch()