app.py

import gradio as gr
   from transformers import AutoTokenizer, AutoModelForCausalLM

   model_name = "microsoft/Phi-3.5-MoE-instruct"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)

   def generate_response(instruction):
       input_text = f"Human: {instruction}\n\nAssistant:"
       inputs = tokenizer(input_text, return_tensors="pt")
       
       outputs = model.generate(**inputs, max_length=200, num_return_sequences=1)
       response = tokenizer.decode(outputs[0], skip_special_tokens=True)
       
       return response.split("Assistant:")[-1].strip()

   iface = gr.Interface(
       fn=generate_response,
       inputs="text",
       outputs="text",
       title="Phi-3.5-MoE-instruct Demo",
       description="Enter an instruction or question to get a response from the model."
   )

   iface.launch()