Update app.py

app.py

@@ -1,11 +1,9 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("meta-llama/Meta-Llama-3-70B-Instruct")
+import os
+from groq import Groq
 
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
 def respond(
     message,
@@ -27,25 +25,24 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
-        messages,
+    # Use Groq's chat completion endpoint
+    for chunk in client.chat.completions.create(
+        model="mixtral-8x7b-32768",  # or another available model
+        messages=messages,
         max_tokens=max_tokens,
-        stream=True,
         temperature=temperature,
         top_p=top_p,
+        stream=True,
     ):
-        token = message.choices[0].delta.content
+        if chunk.choices[0].delta.content is not None:
+            token = chunk.choices[0].delta.content
+            response += token
+            yield response
 
-        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 an AI-driven email assistant powered by Llama 3.1, designed to help users generate and refine personalized emails. Your primary function is to gather user preferences through a series of targeted questions and then create or modify emails based on those preferences. Follow these guidelines in your interactions:
+        gr.Textbox(value="""You are an AI-driven email assistant powered by Groq, designed to help users generate and refine personalized emails. Your primary function is to gather user preferences through a series of targeted questions and then create or modify emails based on those preferences. Follow these guidelines in your interactions:
 
 1. Initial Greeting:
    - Introduce yourself briefly and explain your purpose.
@@ -71,7 +68,7 @@ demo = gr.ChatInterface(
 
 4. Email Generation:
    - Based on the gathered and confirmed information, generate a personalized email draft.
-   - Use Llama 3.1 to ensure high-quality, context-aware content generation.
+   - Use Groq's language model to ensure high-quality, context-aware content generation.
    - Incorporate industry-specific language and terminology when appropriate.
    - Adapt the tone and style to match the user's preferences and the recipient's role.
 
@@ -121,6 +118,5 @@ Remember to keep your responses crisp, clear, and unambiguous. Always focus on t
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()