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b5f9ca4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_dir = "asadhu8/llama_3.2_1b_ddx_plus_medical"
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir)
# Define a function for text generation
def generate_text(prompt, max_length=100, temperature=0.7):
try:
inputs = tokenizer.encode(prompt, return_tensors="pt")
# Generate response using the model
outputs = model.generate(
inputs,
max_length=max_length,
temperature=temperature,
pad_token_id=tokenizer.eos_token_id # Handles padding
)
# Decode the model's output tokens into text
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
except Exception as e:
return f"Error: {str(e)}"
# Define the Gradio interface
demo = gr.Interface(
fn=generate_text, # Function to call
inputs=[
gr.Textbox(label="Input Prompt", placeholder="Type your input here..."),
gr.Slider(50, 300, value=100, step=10, label="Max Length"),
gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature"),
],
outputs=gr.Textbox(label="Generated Response"),
title="Local Hugging Face Model",
description="Interact with a locally stored Hugging Face model for text generation.",
)
# Launch the Gradio app
demo.launch()
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