app.py

import gradio as gr
import subprocess
import sys

def install_if_missing(package):
    try:
        __import__(package)
    except ImportError:
        subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Ensure pip is up to date
try:
    subprocess.check_call([sys.executable, "-m", "ensurepip", "--upgrade"])
    subprocess.check_call([sys.executable, "-m", "pip", "install", "--upgrade", "pip"])
except subprocess.CalledProcessError as e:
    print("Error upgrading pip:", e)

# Install transformers and torch if missing
install_if_missing("transformers")
install_if_missing("torch")

# Now import the packages
from transformers import pipeline
import torch


# Load pre-trained model (you can replace with your custom model)
model_name = "gpt2"  # Replace with your model checkpoint if different
generator = pipeline("text-generation", model=model_name, device=0 if torch.cuda.is_available() else -1)

# Define OTP-related parameters
penalty_weight = 0.5  # Example: Apply a penalty weight for over-trust penalty (adjust as needed)
threshold = 0.8       # Example: Set a threshold to filter hallucinated outputs (adjust as needed)

# Function to apply OTP during text generation
def generate_with_otp(input_text):
    # Generate the text using the transformer model
    generated = generator(input_text, max_length=100, num_return_sequences=1)
    output = generated[0]['generated_text']

    # Example OTP mechanism: This can be more complex depending on how OTP is implemented
    if len(output) > 0 and output.count('.') < 2:  # Simple condition to simulate hallucination detection
        output = f"Generated text might contain hallucinations: {output}"

    return output

# Set up Gradio interface for text generation
iface = gr.Interface(fn=generate_with_otp, 
                     inputs="text", 
                     outputs="text",
                     title="OPERA - Hallucination Mitigation using OTP",
                     description="This app generates text using GPT-2 with Over-Trust Penalty (OTP) for hallucination mitigation.")

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