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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct",
device_map="auto",
trust_remote_code=True)
# Function to generate text based on the prompt
def generate_text(prompt, max_length=100):
inputs = tokenizer(prompt, return_tensors="pt")
inputs = inputs.to(model.device)
outputs = model.generate(**inputs, max_length=max_length)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Create Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
title="Microsoft Phi 3.5B Instruct - Text Generation"
)
iface.launch()
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