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import os
import gradio as gr
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from transformers import pipeline
# Fetch API token from environment variable
api_token = os.getenv("Llama_Token")
# Authenticate with Hugging Face
login(api_token)
# Load LLaMA 3.2 model and tokenizer with the API token
model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name, token=api_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=api_token)
# Define the function to generate text
def generate_text(prompt, max_length=100, temperature=0.7):
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(
inputs['input_ids'],
max_length=max_length,
temperature=temperature,
pad_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Create the Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Enter your prompt", placeholder="Start typing...", lines=5),
gr.Slider(minimum=50, maximum=200, value=100, step=1, label="Max Length"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
],
outputs="text",
title="LLaMA 3.2 Text Generator",
description="Generate text using the LLaMA 3.2 model. Adjust the settings and input a prompt to generate responses.",
)
# Launch the Gradio app
iface.launch(share=True)
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