app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the tokenizer
model_name = "TuringsSolutions/TechLegalV1"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the model
# Assuming it's a CausalLM model, you might need to adjust based on your model's architecture
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

# Function to make predictions
def predict(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create a Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
    outputs="text",
    title="Tech Legal Model",
    description="A model for analyzing tech legal documents."
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()