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import gradio as gr
import json
import requests
import time
# Configuration for LM Studio API
LM_STUDIO_API_URL = "http://192.168.1.245:1234/v1/chat/completions"
# Make sure to replace this with your model name from LM Studio
LM_MODEL_NAME = "google/gemma-3-27b"
# Advanced System Prompt for InsightGenie
system_prompt_content = """
You are InsightGenie, an AI-powered qualitative research assistant. Your purpose is to conduct a structured interview to deeply understand a user's experience with a specific topic.
**Instructions:**
1. **Persona:** You are a professional, neutral, and empathetic research interviewer. Maintain a supportive and curious tone.
2. **Goal:** Your primary goal is to gather rich, detailed qualitative data. Ask open-ended questions that encourage detailed responses.
3. **Conversation Flow:**
- After each user response, analyze the sentiment and key topics.
- Based on your analysis, generate **one** follow-up question to probe deeper. Do not ask multiple questions.
- You must keep the conversation focused on the topic.
4. **Structured Output:** After each user turn, you must respond with a JSON object. The JSON should contain two fields:
- `next_question`: The text of your next question for the user.
- `summary`: A brief, neutral summary of the user's last response. This helps for later analysis.
**Example JSON Response:**
```json
{
"next_question": "Can you tell me more about why that was your favorite part?",
"summary": "The user had a positive experience and liked the fast delivery."
}
"""
def chat_with_lm_studio(message, history):
messages = [{"role": "system", "content": system_prompt_content}]
for user_msg, assistant_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
try:
response = requests.post(
LM_STUDIO_API_URL,
json={
"model": LM_MODEL_NAME,
"messages": messages,
"max_tokens": 150,
"temperature": 0.7
}
)
response.raise_for_status()
# Parse the JSON response
api_response_data = response.json()
# Check if 'choices' key exists in the response
if 'choices' in api_response_data and len(api_response_data['choices']) > 0:
ai_message_content = api_response_data['choices'][0]['message']['content']
# Since we removed structured output, we just return the text
return ai_message_content
else:
# If 'choices' is missing, there's likely an error.
# Look for an 'error' key or other diagnostic info.
error_message = api_response_data.get('error', 'Unknown API error.')
print(f"API Error Response: {error_message}")
return f"An error occurred with the API: {error_message}"
except requests.exceptions.RequestException as e:
return f"An API error occurred: {e}. Please ensure LM Studio server is running."
except Exception as e:
return f"An unexpected error occurred: {e}"
# Gradio interface to launch the demo
demo = gr.ChatInterface(
fn=chat_with_lm_studio,
chatbot=gr.Chatbot(height=500),
theme=gr.themes.Soft(),
title="InsightGenie Live Demo",
description="Your AI-powered qualitative research assistant.",
examples=[["I had a great experience with a new online clothing store."],
["The delivery was slow, and the product was damaged."]],
multimodal=False
)
if __name__ == "__main__":
demo.launch(inbrowser=True) |