src/streamlit_app.py

import torch
import torchaudio
import os
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import streamlit as st

def load_models():
    whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
    whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
    text_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
    return whisper_processor, whisper_model, text_model, tokenizer

whisper_processor, whisper_model, text_model, tokenizer = load_models()

def transcribe(audio_path):
    waveform, sample_rate = torchaudio.load(audio_path)
    input_features = whisper_processor(
        waveform.squeeze().numpy(),
        sampling_rate=sample_rate,
        return_tensors="pt"
    ).input_features
    predicted_ids = whisper_model.generate(input_features)
    transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
    return transcription

def extract_text_features(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = text_model(**inputs)
    predicted_class = outputs.logits.argmax(dim=1).item()
    return "Hate Speech" if predicted_class == 1 else "Not Hate Speech"

def predict(audio_file, text_input):
    if not audio_file and not text_input:
        return "Please provide either an audio file or some text."
    if audio_file is not None:
        audio_path = "temp_audio.wav"
        with open(audio_path, "wb") as f:
            f.write(audio_file.read())
        transcribed_text = transcribe(audio_path)
        prediction = extract_text_features(text_input or transcribed_text)
        return f"Predicted: {prediction} \n\n(Transcribed: {transcribed_text})" if not text_input else f"Predicted: {prediction}"
    elif text_input:
        prediction = extract_text_features(text_input)
        return f"Predicted: {prediction}"

st.title("Hate Speech Detector")

uploaded_audio = st.file_uploader("Upload Audio File (.mp3, .wav, .ogg, .flac, .opus)", type=["mp3", "wav", "ogg", "flac", "opus"])
text_input = st.text_input("Or enter text:")

if st.button("Predict"):
    result = predict(uploaded_audio, text_input)
    st.success(result)