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d937c98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | 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"
LM_MODEL_NAME = "google/gemma-3-27b"
# --- Dynamic System Prompt ---
DEFAULT_PROMPT = """
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.
**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."
}
"""
# Global variable to store the conversation log for the current session
conversation_log = []
# --- Helper Functions ---
def handle_save_and_display_status():
save_message = save_conversation_log()
# Returns a Gradio component update
return gr.Textbox(value=save_message, visible=True)
def log_conversation_turn(user_message, ai_response, ai_summary):
"""Appends a single turn to the in-memory conversation log."""
global conversation_log
conversation_log.append({
"user_message": user_message,
"ai_response": ai_response,
"ai_summary": ai_summary,
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
})
def save_conversation_log():
"""Saves the entire conversation log to a JSON file."""
global conversation_log
if not conversation_log:
return "No conversation to save."
file_name = f"conversation_log_{int(time.time())}.json"
try:
with open(file_name, 'w', encoding='utf-8') as f:
json.dump(conversation_log, f, indent=4, ensure_ascii=False)
return f"Conversation saved to {file_name}"
except Exception as e:
return f"Failed to save conversation: {e}"
def chat_with_lm_studio(message, history, prompt_text):
messages = [{"role": "system", "content": prompt_text}]
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": 250, # Increased max tokens to give the model more room
"temperature": 0.7
}
)
response.raise_for_status()
api_response_data = response.json()
if 'choices' in api_response_data and len(api_response_data['choices']) > 0:
raw_content = api_response_data['choices'][0]['message']['content']
# --- Robust JSON Extraction and Parsing Logic ---
try:
# Find the start and end of the JSON block
json_start = raw_content.find("```json")
if json_start != -1:
json_end = raw_content.find("```", json_start + 1)
if json_end != -1:
# Extract the pure JSON string
json_string = raw_content[json_start + 7:json_end].strip()
else:
# Fallback if the closing tag is missing
json_string = raw_content[json_start + 7:].strip()
else:
# Fallback to the entire response if no JSON block is found
json_string = raw_content.strip()
parsed_response = json.loads(json_string)
next_question = parsed_response.get("next_question", "Thank you for your response.")
summary = parsed_response.get("summary", "No summary provided.")
log_conversation_turn(message, next_question, summary)
print(f"User: {message}\nAI Summary: {summary}\nAI Question: {next_question}\n---")
history.append((message, next_question))
return "", history
except json.JSONDecodeError:
print("LLM failed to produce valid JSON. Raw output:", raw_content)
history.append((message, "I'm sorry, I couldn't process that. Can you please rephrase?"))
return "", history
else:
error_message = api_response_data.get('error', 'Unknown API error.')
print(f"API Error Response: {error_message}")
history.append((message, f"An API error occurred: {error_message}. Please check the console."))
return "", history
except requests.exceptions.RequestException as e:
history.append((message, f"An API error occurred: {e}. Please ensure LM Studio server is running."))
return "", history
except Exception as e:
history.append((message, f"An unexpected error occurred: {e}"))
return "", history
# --- Gradio Interface Layout ---
with gr.Blocks(theme=gr.themes.Soft(), title="InsightGenie Live Demo") as demo:
gr.Markdown("# InsightGenie: Your AI-powered Qualitative Assistant 🧠")
with gr.Tabs():
with gr.Tab("Live Demo"):
gr.Markdown(
"Start a conversation with the AI researcher. "
"The conversation data is structured for analysis and can be saved."
)
chatbot = gr.Chatbot(height=500, placeholder="Type your first message to begin the interview...")
with gr.Row():
msg = gr.Textbox(label="Your message", scale=4)
chat_submit_btn = gr.Button("Send", scale=1)
gr.Examples(
examples=[
["I had a great experience with a new online clothing store."],
["The delivery was slow, and the product was damaged."]
],
inputs=msg
)
with gr.Row():
clear_btn = gr.Button("Clear Chat")
save_btn = gr.Button("Save Conversation")
save_status = gr.Textbox(label="Save Status", interactive=False, visible=False)
with gr.Tab("Prompt Settings"):
gr.Markdown(
"Customize the AI's persona and instructions. "
"Changing this prompt will affect the next conversation turn."
)
prompt_input = gr.Textbox(
label="System Prompt",
value=DEFAULT_PROMPT,
lines=20,
interactive=True
)
# Event Handlers
msg.submit(chat_with_lm_studio, [msg, chatbot, prompt_input], [msg, chatbot], concurrency_limit=None)
chat_submit_btn.click(chat_with_lm_studio, [msg, chatbot, prompt_input], [msg, chatbot], concurrency_limit=None)
clear_btn.click(lambda: [], None, [chatbot])
save_btn.click(handle_save_and_display_status, None, save_status)
# --- Launch the Demo ---
if __name__ == "__main__":
demo.launch(share=True) |