Update app.py

app.py

@@ -1,64 +1,60 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from groq import Groq
+
+# Get GROQ API key from environment variable
+GROQ_API_KEY = os.getenv("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")
+
+# Initialize the Groq client
+client = Groq(api_key=GROQ_API_KEY)
+
+# Define the chatbot function
+def chat_with_groq(message, history):
+    try:
+        # Prepare the conversation history
+        messages = [{"role": "system", "content": "You are a helpful assistant."}]
+        
+        for user, bot in history:
+            messages.append({"role": "user", "content": user})
+            messages.append({"role": "assistant", "content": bot})
+        
+        # Add the current user message
+        messages.append({"role": "user", "content": message})
+
+        # Call Groq API
+        response = client.chat.completions.create(
+            model="llama3-70b-8192",  # ✅ Updated to active supported model
+            messages=messages,
+            temperature=0.7,
+            max_tokens=500,
+        )
+
+        # Get assistant's reply
+        bot_reply = response.choices[0].message.content
+
+        # Update chat history
+        history.append((message, bot_reply))
+        return history, history
+
+    except Exception as e:
+        error_message = f"Error: {str(e)}"
+        history.append((message, error_message))
+        return history, history
+
+# Build Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>🤖 Chatbot Powered by Groq API</center></h1>")
+    chatbot = gr.Chatbot()
+    message = gr.Textbox(placeholder="Type your message...", label="Your Message")
+    send_button = gr.Button("Send")
+    state = gr.State([])
+
+    send_button.click(
+        chat_with_groq,
+        inputs=[message, state],
+        outputs=[chatbot, state],
+    )
+
+# Launch the Gradio app
+demo.launch()
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()