| import gradio as gr |
| import requests |
| import os |
| import random |
| import uuid |
| from datetime import datetime |
| import csv |
| from huggingface_hub import HfApi |
|
|
| |
| |
| HF_TOKEN = os.getenv("HF_Key") |
|
|
| |
| MODEL_INFOS = [ |
| { |
| "name": "Base", |
| "endpoint_url": os.getenv("BASE_ENDPOINT_URL", "https://o3pz2i9x2k6otr2a.eu-west-1.aws.endpoints.huggingface.cloud/v1/"), |
| "model": os.getenv("BASE_ENDPOINT_MODEL", "qwen3-4b-instruct-2507-pxe") |
| }, |
| { |
| "name": "Math", |
| "endpoint_url": os.getenv("MATH_ENDPOINT_URL", "https://jockj5ko30gpg5lg.eu-west-1.aws.endpoints.huggingface.cloud/v1/"), |
| "model": os.getenv("MATH_ENDPOINT_MODEL", "teach-math-qwen3-4b-2507-r1--uab") |
| }, |
| { |
| "name": "General", |
| "endpoint_url": os.getenv("GENERAL_ENDPOINT_URL", "https://ucewop5x3jsguqwq.eu-west-1.aws.endpoints.huggingface.cloud/v1/"), |
| "model": os.getenv("GENERAL_ENDPOINT_MODEL", "CanisAI/teach-generalist-qwen3-4b-2507-r1-merged") |
| }, |
| ] |
|
|
| |
| DATASET_REPO_ID = "CanisAI/mvlg-data" |
|
|
| api = HfApi() |
|
|
| |
| FEEDBACK_POINTS = [ |
| "How clear was the explanation?", |
| "How helpful were the steps in guiding you to the solution?", |
| "How well did the assistant adapt to your learning style?", |
| "How motivating and encouraging was the response?", |
| "How accurate and reliable was the information provided?", |
| "How relevant was the information to your question?", |
| "How natural and conversational was the interaction?", |
| "How much do you trust the assistant?" |
| ] |
|
|
| def query_chat_endpoint(endpoint_url, model, messages, max_tokens=150, temperature=0.7): |
| url = endpoint_url.rstrip("/") + "/chat/completions" |
| headers = { |
| "Accept": "application/json", |
| "Content-Type": "application/json", |
| "Authorization": f"Bearer {HF_TOKEN}" |
| } |
| payload = { |
| "model": model, |
| "messages": messages, |
| "max_tokens": max_tokens, |
| "temperature": temperature, |
| "stream": False |
| } |
| try: |
| response = requests.post(url, headers=headers, json=payload) |
| response.raise_for_status() |
| result = response.json() |
| return result["choices"][0]["message"]["content"] |
| except Exception as e: |
| return f"Error: {str(e)}" |
|
|
| def chat_multi_llm(message, history, current_model_state, conversation_id_state): |
| if history is None: |
| history = [] |
| if current_model_state is None: |
| current_model_state = random.choice(MODEL_INFOS) |
| conversation_id_state = str(uuid.uuid4()) |
| messages = [{"role": msg["role"], "content": msg["content"]} for msg in history] |
| messages.append({"role": "user", "content": message}) |
| model_name = current_model_state["name"] |
| endpoint_url = current_model_state["endpoint_url"] |
| model = current_model_state["model"] |
| answer = query_chat_endpoint(endpoint_url, model, messages) |
| log_chat_to_csv(message, history, {model_name: answer}, model_name, conversation_id_state) |
| new_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": answer}] |
| return new_history, current_model_state, conversation_id_state |
|
|
| def log_chat_to_csv(message, history, results, used_model, conversation_id, filename="chat_conversations_log.csv"): |
| from os.path import isfile |
| file_exists = isfile(filename) |
| with open(filename, mode="a", encoding="utf-8", newline="") as csvfile: |
| writer = csv.writer(csvfile) |
| if not file_exists: |
| header = ["timestamp", "conversation_id", "history", "user_message", "used_model", "response"] |
| writer.writerow(header) |
| row = [datetime.now().isoformat(), conversation_id, str(history), message, used_model, list(results.values())[0]] |
| writer.writerow(row) |
|
|
| |
| try: |
| api.upload_file( |
| path_or_fileobj=filename, |
| path_in_repo=filename, |
| repo_id=DATASET_REPO_ID, |
| repo_type="dataset", |
| token=HF_TOKEN |
| ) |
| except Exception as e: |
| print(f"Error uploading to HF dataset: {e}") |
|
|
| def submit_feedback(current_model, conversation_id, *slider_values): |
| filename = "feedback_log.csv" |
| from os.path import isfile |
| file_exists = isfile(filename) |
|
|
| with open(filename, "a", encoding="utf-8", newline="") as f: |
| writer = csv.writer(f) |
| if not file_exists: |
| writer.writerow(["timestamp", "conversation_id", "used_model"] + FEEDBACK_POINTS) |
| writer.writerow([ |
| datetime.now().isoformat(), |
| conversation_id, |
| current_model["name"] if current_model else "None" |
| ] + list(slider_values)) |
|
|
| |
| try: |
| api.upload_file( |
| path_or_fileobj=filename, |
| path_in_repo=filename, |
| repo_id=DATASET_REPO_ID, |
| repo_type="dataset", |
| token=HF_TOKEN |
| ) |
| except Exception as e: |
| print(f"Error uploading feedback to HF dataset: {e}") |
|
|
| return ( |
| gr.update(visible=False), |
| gr.update(visible=True), |
| gr.update(value="Thank you! You can start a new conversation.", visible=True), |
| [], |
| None, |
| None, |
| gr.update(interactive=True), |
| gr.update(interactive=True), |
| [], |
| gr.update(visible=False) |
| ) |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown(""" |
| # LLM Case Study: Multi-Model Chat Comparison |
| Start a conversation. After finishing, you can provide feedback and start a new conversation. By Using the app you accept that your interactions and feedback will be logged and used for research purposes. Please don't share any personal, sensitive, or confidential information. |
| """) |
|
|
| history_state = gr.BrowserState([]) |
| |
| |
| |
| current_model_state = gr.BrowserState(None) |
| conversation_id_state = gr.BrowserState(None) |
|
|
| with gr.Column(visible=True) as chat_col: |
| chatbot = gr.Chatbot(type="messages", value=[]) |
| msg = gr.Textbox(placeholder="Enter your message...", show_label=False) |
| submit_btn = gr.Button("Send") |
| end_btn = gr.Button("End conversation and give feedback") |
| end_info = gr.Markdown("", visible=False) |
|
|
| with gr.Column(visible=False) as feedback_col: |
| sliders = [gr.Slider(1, 10, value=5, step=1, label=label) for label in FEEDBACK_POINTS] |
| feedback_btn = gr.Button("Submit feedback and start new conversation") |
| feedback_info = gr.Markdown("", visible=False) |
|
|
| def user_message(message, history, current_model, conversation_id): |
| if message is None or message.strip() == "": |
| return history, "", history, current_model, conversation_id |
| new_history, updated_model, updated_conv_id = chat_multi_llm(message, history, current_model, conversation_id) |
| return new_history, "", new_history, updated_model, updated_conv_id |
|
|
| def load_chat_history(history): |
| """Load the chat history into the chatbot on page load""" |
| if history is None: |
| return [] |
| return history |
|
|
| def end_conversation(): |
| return ( |
| gr.update(visible=True), |
| gr.update(visible=False), |
| gr.update(value="Please provide feedback on the last conversation.", visible=True), |
| gr.update(interactive=False), |
| gr.update(interactive=False) |
| ) |
|
|
| msg.submit( |
| user_message, |
| inputs=[msg, history_state, current_model_state, conversation_id_state], |
| outputs=[chatbot, msg, history_state, current_model_state, conversation_id_state], |
| queue=False |
| ) |
|
|
| submit_btn.click( |
| user_message, |
| inputs=[msg, history_state, current_model_state, conversation_id_state], |
| outputs=[chatbot, msg, history_state, current_model_state, conversation_id_state], |
| queue=False |
| ) |
|
|
| end_btn.click( |
| end_conversation, |
| inputs=None, |
| outputs=[feedback_col, chat_col, end_info, msg, submit_btn] |
| ) |
|
|
| feedback_btn.click( |
| submit_feedback, |
| inputs=[current_model_state, conversation_id_state] + sliders, |
| outputs=[feedback_col, chat_col, feedback_info, history_state, current_model_state, conversation_id_state, msg, submit_btn, chatbot, end_info] |
| ) |
|
|
| |
| demo.load( |
| load_chat_history, |
| inputs=[history_state], |
| outputs=[chatbot] |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
