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import gradio as gr
import openai
# We assume the Hugging Face Inference API is OpenAI-compatible.
# For each LLM, set openai.api_base to the model's endpoint and then call openai.ChatCompletion.
# Your Hugging Face API key
HF_API_KEY = "hf_1234"
# Model endpoints on Hugging Face
MODEL_ENDPOINTS = {
"Qwen2.5-72B-Instruct": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct",
"Llama3.3-70B-Instruct": "https://api-inference.huggingface.co/models/meta-llama/Llama-3.3-70B-Instruct",
"Qwen2.5-Coder-32B-Instruct": "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B-Instruct",
}
# Query a specific model using OpenAI-compatible ChatCompletion
def query_model(prompt, model_endpoint):
openai.api_key = HF_API_KEY
openai.api_base = model_endpoint
response = openai.ChatCompletion.create(
model="any-model-placeholder", # placeholder name, not actually used by the HF endpoint
messages=[{"role": "user", "content": prompt}],
max_tokens=512,
temperature=0.7
)
return response.choices[0].message["content"]
def chat_with_models(user_input, history):
# Let each model provide its own contribution
responses = []
for model_name, endpoint in MODEL_ENDPOINTS.items():
model_response = query_model(user_input, endpoint)
responses.append(f"**{model_name}**: {model_response}")
# Combine all responses in a single answer
combined_answer = "\n\n".join(responses)
history.append((user_input, combined_answer))
return history, history
with gr.Blocks() as demo:
gr.Markdown("# Multi-LLM Chatbot using Hugging Face Inference API")
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Your Message")
clear = gr.Button("Clear")
def clear_chat():
return [], []
msg.submit(chat_with_models, [msg, chatbot], [chatbot, chatbot])
clear.click(fn=clear_chat, outputs=[chatbot, chatbot])
demo.launch()
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