app.py

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

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

# Function to make predictions
def predict(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    # Assuming we need to extract some specific information from outputs
    # Modify this part based on your model's output format
    return outputs.last_hidden_state.mean(dim=1).squeeze().tolist()

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

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