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import gradio as gr
from transformers import LlamaTokenizer, LlamaForCausalLM
import torch
# Load the fine-tuned model and tokenizer
try:
tokenizer = LlamaTokenizer.from_pretrained("./fine_tuned_llama2")
model = LlamaForCausalLM.from_pretrained("./fine_tuned_llama2")
model.eval()
print("Model and tokenizer loaded successfully.")
except Exception as e:
print(f"Error loading model or tokenizer: {e}")
# Function to predict fraud based on text input
def predict(input_text):
if not input_text:
return "Please enter some text to analyze."
try:
# Tokenize input
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, padding="max_length", truncation=True)
# Generate output
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=50)
# Decode and return result
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return result
except Exception as e:
return f"Error during prediction: {e}"
# Create Gradio interface with text input
interface = gr.Interface(
fn=predict,
inputs=gr.Textbox(
lines=2,
placeholder="Enter text to analyze (e.g., 'Facility backdates policies. Is this fraudulent?')",
label="Input Text"
),
outputs=gr.Textbox(label="Prediction"),
title="Fine-Tune LLaMA 2 for Healthcare Fraud Analysis",
description="Test the fine-tuned LLaMA 2 model to detect healthcare fraud. Enter a description of a facility's behavior to analyze."
)
# Launch the interface
interface.launch() |