Update app.py

app.py

@@ -1,19 +1,32 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 import os
 from groq import Groq
-import groq_gradio
 
-# Initialize Groq client
-client = Groq(
-    api_key=os.environ.get("GROQ_API_KEY")
-)
+def chat_with_groq(user_input, history):
+    api_key = os.getenv("GROQ_API_KEY")  # Set this in your environment variables
+    if not api_key:
+        return "Error: API key not found. Set GROQ_API_KEY as an environment variable."
+    
+    client = Groq(api_key=api_key)
+    
+    # Format the history into Groq's expected format
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
+    
+    # Append the latest user input
+    messages.append({"role": "user", "content": user_input})
+    
+    chat_completion = client.chat.completions.create(
+        messages=messages,
+        model="llama-3.3-70b-versatile",
+    )
+    
+    response = chat_completion.choices[0].message.content
+    return response
 
-gr.load(
-    name='llama-3.3-70b-versatile', # The specific model powered by Groq to use
-    src=groq_gradio.registry, # Tells Gradio to use our custom interface registry as the source
-    title='Groq-Gradio Integration', # The title shown at the top of our UI
-    description="Chat with the Llama 3.3 70B model powered by Groq.", # Subtitle
-    examples=["Explain quantum gravity to a 5-year old.", "How many R are there in the word Strawberry?"] # Pre-written prompts users can click to try
-).launch() # Creates and starts the web server!
+# Gradio Interface
+demo = gr.ChatInterface(chat_with_groq)
 
+demo.launch()