app.py

import os
import json
import numpy as np
from tokenizers import Tokenizer
import onnxruntime as ort
from huggingface_hub import hf_hub_download
import gradio as gr

class ONNXInferencePipeline:
    def __init__(self, repo_id):
        # Retrieve the Hugging Face token from the environment variable
        hf_token = os.getenv("HF_TOKEN")
        if hf_token is None:
            raise ValueError("HF_TOKEN environment variable is not set.")

        # Download files from Hugging Face Hub using the token
        self.onnx_path = hf_hub_download(repo_id=repo_id, filename="RudeRater.onnx", use_auth_token=hf_token)
        self.tokenizer_path = hf_hub_download(repo_id=repo_id, filename="train_bpe_tokenizer.json", use_auth_token=hf_token)
        self.config_path = hf_hub_download(repo_id=repo_id, filename="hyperparameters.json", use_auth_token=hf_token)

        # Load configuration
        with open(self.config_path) as f:
            self.config = json.load(f)

        # Initialize tokenizer
        self.tokenizer = Tokenizer.from_file(self.tokenizer_path)
        self.max_len = self.config["tokenizer"]["max_len"]

        # Initialize ONNX runtime session
        self.session = ort.InferenceSession(self.onnx_path)
        self.providers = ['CPUExecutionProvider']  # Use CUDA if available
        if 'CUDAExecutionProvider' in ort.get_available_providers():
            self.providers = ['CUDAExecutionProvider']
        self.session.set_providers(self.providers)

    def preprocess(self, text):
        encoding = self.tokenizer.encode(text)
        ids = encoding.ids[:self.max_len]
        padding = [0] * (self.max_len - len(ids))
        return np.array(ids + padding, dtype=np.int64).reshape(1, -1)

    def predict(self, text):
        # Preprocess
        input_array = self.preprocess(text)

        # Run inference
        results = self.session.run(
            None,
            {'input': input_array}
        )

        # Post-process
        logits = results[0]
        probabilities = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
        predicted_class = int(np.argmax(probabilities))

        # Map to labels
        class_labels = ['Inappropriate Content', 'Not Inappropriate']
        return {
            'label': class_labels[predicted_class],
            'confidence': float(probabilities[0][predicted_class]),
            'probabilities': probabilities[0].tolist()
        }

# Example usage
if __name__ == "__main__":
    # Initialize the pipeline with the Hugging Face repository ID
    pipeline = ONNXInferencePipeline(repo_id="iimran/RudeRater")

    # Example texts for testing
    example_texts = [
        "This content contains explicit language and violent threats",
        "The weather today is pleasant and suitable for all ages",
        "You're a worthless piece of garbage who should die",
        "Please remember to submit your reports by Friday"
    ]
    
    for text in example_texts:
        result = pipeline.predict(text)
        print(f"Input: {text}")
        print(f"Prediction: {result['label']} ({result['confidence']:.2%})")
        print(f"Probabilities: Inappropriate={result['probabilities'][0]:.2%}, Not Inappropriate={result['probabilities'][1]:.2%}")
        print("-" * 80)

    # Define a function for Gradio to use
    def gradio_predict(text):
        result = pipeline.predict(text)
        return (
            f"Prediction: {result['label']} ({result['confidence']:.2%})\n"
            f"Probabilities: Inappropriate={result['probabilities'][0]:.2%}, Not Inappropriate={result['probabilities'][1]:.2%}"
        )

    # Create a Gradio interface
    iface = gr.Interface(
        fn=gradio_predict,
        inputs=gr.Textbox(lines=7, placeholder="Enter text here..."),
        outputs="text",
        title="RudeRater - Content Appropriateness Classifier",
        description="RudeRater is designed to identify inappropriate content in text. It analyzes input for offensive language, explicit content, or harmful material. Enter text to check its appropriateness.",
        examples=[
            "This is completely unacceptable behavior and I'll make sure you regret it",
            "The community guidelines clearly prohibit any form of discrimination",
            "Your mother should have done better raising such a useless idiot",
            "We appreciate your feedback and will improve our services",
            "I'm going to find you and make you pay for what you've done",
            "The park maintenance schedule has been updated for summer"
        ]
    )

    # Launch the Gradio app
    iface.launch()