Create app.py

app.py

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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()