Update app.py

app.py

@@ -1,63 +1,47 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import os
+from groq import Groq
 
-"""
-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
+# Initialize Groq client
+client = Groq(
+    api_key=os.environ["GROQ_API_KEY"],
+)
 
-"""
-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)",
-        ),
+def generate_email(name, project, key_benefits):
+    prompt = f"""
+    Write a professional email to {name} about the {project} project. 
+    Highlight the following key benefits:
+    {key_benefits}
+    
+    The email should be concise, engaging, and persuasive.
+    """
+    
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        model="mixtral-8x7b-32768",  # or another appropriate Groq model
+        temperature=0.7,
+        max_tokens=500,
+    )
+    
+    return chat_completion.choices[0].message.content
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_email,
+    inputs=[
+        gr.Textbox(label="Recipient Name"),
+        gr.Textbox(label="Project Name"),
+        gr.Textbox(label="Key Benefits (comma-separated)")
     ],
+    outputs=gr.Textbox(label="Generated Email"),
+    title="AI Email Generator",
+    description="Generate personalized emails using Groq LLM"
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the interface
+iface.launch()