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

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

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)
    return outputs.logits.argmax(dim=1).item()

def predict_hate_speech(audio_path, text):
    transcription = transcribe(audio_path)
    text_input = text if text else transcription
    prediction = extract_text_features(text_input)
    return "Hate Speech" if prediction == 1 else "Not Hate Speech"

st.title("Hate Speech Detector with Audio and Text")
audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "flac"])
text_input = st.text_input("Optional text input")
if st.button("Predict"):
    if audio_file is not None:
        with open("temp_audio.wav", "wb") as f:
            f.write(audio_file.read())
        prediction = predict_hate_speech("temp_audio.wav", text_input)
        st.success(prediction)
    else:
        st.warning("Please upload an audio file.")