township_chatbot.py

import subprocess
import gradio as gr

def chat_with_ollama(user_message, chat_history):
    """
    Sends the full chat history + current user message to Ollama model,
    captures the response, and appends it to the history.
    """
    # Prepare prompt by joining past conversation + current message
    prompt = ""
    for i, (user, bot) in enumerate(chat_history):
        prompt += f"User: {user}\nAssistant: {bot}\n"
    prompt += f"User: {user_message}\nAssistant:"

    # Call Ollama CLI to get model response
    try:
        result = subprocess.run(
            ["ollama", "run", "township_business_growth_coach", prompt],
            capture_output=True,
            text=True,
            timeout=30
        )
        if result.returncode == 0:
            bot_reply = result.stdout.strip()
        else:
            bot_reply = f"Error from Ollama: {result.stderr.strip()}"
    except Exception as e:
        bot_reply = f"Exception: {str(e)}"

    # Update chat history
    chat_history.append((user_message, bot_reply))

    # Format output as list of dicts for chat UI
    chat_formatted = [{"User": u, "Bot": b} for u, b in chat_history]

    return chat_formatted, chat_history

with gr.Blocks(title="Township Business Growth Coach Chatbot") as demo:
    gr.Markdown("## 💬 Township Business Growth Coach Chatbot")
    chatbox = gr.Chatbot(label="Chat History")
    msg = gr.Textbox(placeholder="Ask your township business growth question here...")
    state = gr.State([])  # stores history as list of (user, bot) tuples
    submit = gr.Button("Send")

    submit.click(chat_with_ollama, inputs=[msg, state], outputs=[chatbox, state])
    msg.submit(chat_with_ollama, inputs=[msg, state], outputs=[chatbox, state])

demo.launch(server_name="0.0.0.0", server_port=7860)