Update app.py

app.py

@@ -1,8 +1,12 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
 
-# Initialize Hugging Face model client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 🔑 Load Hugging Face API Token from environment variable
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")  # Ensure this is set
+
+# Initialize Hugging Face model client with authentication
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=HF_API_TOKEN)
 
 # System message setting the chatbot's behavior
 SYSTEM_MESSAGE = (
@@ -15,31 +19,26 @@ def respond(message, history):
     # Convert history into OpenAI-style messages
     messages = [{"role": "system", "content": SYSTEM_MESSAGE}]
     
-    # Convert Gradio history format (tuples) to OpenAI format
     for user, bot in history:
         messages.append({"role": "user", "content": user})
         messages.append({"role": "assistant", "content": bot})
 
-    # Append latest user input
     messages.append({"role": "user", "content": message})
 
     # Get response from Hugging Face model
     response = client.chat_completion(messages, max_tokens=200, temperature=0.7)
     bot_reply = response.choices[0].message.content
 
-    # Append new message in (user, bot) tuple format (✅ Correct for Gradio)
-    history.append((message, bot_reply))
-    
-    return history, bot_reply  # ✅ Ensure return matches Gradio format
+    history.append((message, bot_reply))  # ✅ Ensure return matches Gradio format
+    return history, bot_reply
 
 # Create Gradio chatbot UI
 demo = gr.ChatInterface(
     respond, 
-    chatbot=gr.Chatbot(),  # ✅ Default format expects (user, bot) tuples
+    chatbot=gr.Chatbot(),
     title="Misinformation Detection Chatbot",
     description="Ask anything, and the chatbot will verify whether it's true or false.",
 )
 
-# Launch Gradio UI
 if __name__ == "__main__":
     demo.launch()