Update app.py

app.py

@@ -1,64 +1,46 @@
+import os
+import groq
 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("HuggingFaceH4/zephyr-7b-beta")
+# Ensure the API key is set as an environment variable
+api_key = os.getenv("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")
 
+if not api_key:
+    raise RuntimeError("❌ Error: GROQ_API_KEY is not set. Please add it in Hugging Face 'Settings' → 'Secrets'.")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Initialize the Groq client with the API key
+client = groq.Client(api_key=api_key)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Chatbot function using Groq API
+def chat_with_groq(user_input, history):
+    messages = [{"role": "system", "content": "You are a helpful AI assistant."}]
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
+    messages.append({"role": "user", "content": user_input})
 
-    messages.append({"role": "user", "content": message})
+    response = client.chat.completions.create(
+        model="llama3-8b-8192",  # Replace with the actual model you intend to use
+        messages=messages,
+        temperature=0.7
+    )
 
-    response = ""
+    bot_reply = response.choices[0].message.content
+    history.append((user_input, bot_reply))
+    return history
 
-    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
+# Create Gradio UI for the chatbot
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 Groq AI Chatbot")
+    chatbot = gr.Chatbot()
+    user_input = gr.Textbox(label="Type your message...")
+    state = gr.State([])
 
-        response += token
-        yield response
+    def respond(input_text, chat_history):
+        chat_history = chat_with_groq(input_text, chat_history)
+        return chat_history
 
+    user_input.submit(respond, [user_input, state], [chatbot])
 
-"""
-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()
+# Launch Gradio app
+demo.launch()