Update app.py

app.py

@@ -1,11 +1,9 @@
+import os
 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"
+# We'll read the Hugging Face API key from environment variables (using Spaces "secrets").
+HF_API_KEY = os.getenv("HF_API_KEY")
 
 # Model endpoints on Hugging Face
 MODEL_ENDPOINTS = {
@@ -14,32 +12,34 @@ MODEL_ENDPOINTS = {
     "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):
+    """
+    Query a specific model using OpenAI-compatible ChatCompletion.
+    Since the Hugging Face Inference API is OpenAI-compatible here,
+    we just set openai.api_base to the model's 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
+        model="placeholder-model",
         messages=[{"role": "user", "content": prompt}],
         max_tokens=512,
-        temperature=0.7
+        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")
+    gr.Markdown("# Multi-LLM Chatbot using Hugging Face Inference API (OpenAI-compatible)")
 
     chatbot = gr.Chatbot()
     msg = gr.Textbox(label="Your Message")
@@ -48,7 +48,7 @@ with gr.Blocks() as demo:
     def clear_chat():
         return [], []
 
-    msg.submit(chat_with_models, [msg, chatbot], [chatbot, chatbot])
+    msg.submit(fn=chat_with_models, inputs=[msg, chatbot], outputs=[chatbot, chatbot])
     clear.click(fn=clear_chat, outputs=[chatbot, chatbot])
 
 demo.launch()