Create app.py

app.py

@@ -0,0 +1,54 @@
+import os
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the Hugging Face Inference Client
+# Make sure to add your HF_TOKEN in the Space Settings if it's a gated model
+client = InferenceClient(
+    model="meta-llama/Llama-3.3-70B-Instruct",
+    token=os.getenv("HF_TOKEN")
+)
+
+def respond(message, chat_history, system_message, max_tokens, temperature, top_p):
+    # Format the chat history for the conversational model
+    messages = [{"role": "system", "content": system_message}]
+    
+    for val in chat_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 = ""
+
+    # Stream the response back from the Llama 3.3 model
+    for msg in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = msg.choices[0].delta.content
+        if token:
+            response += token
+            yield response
+
+# Define a clean Gradio Chat Interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a helpful, smart AI assistant powered by Llama 3.3.", 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"),
+    ],
+    title="Llama 3.3 70B Instruct - Agent Demo",
+    description="A simple conversational agent interface leveraging Meta's Llama-3.3-70B-Instruct model.",
+)
+
+if __name__ == "__main__":
+    demo.launch()
+