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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()
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