app.py

import gradio as gr
import torch
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, pipeline
from transformers import BertTokenizer, BertForSequenceClassification
import librosa

# Load models
# Transcription model for Moroccan Darija
processor = Wav2Vec2Processor.from_pretrained("boumehdi/wav2vec2-large-xlsr-moroccan-darija")
transcription_model = Wav2Vec2ForCTC.from_pretrained("boumehdi/wav2vec2-large-xlsr-moroccan-darija")

# Summarization model
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Topic Classification Model (BERT for example)
topic_model = BertForSequenceClassification.from_pretrained("bert-base-uncased")  # Example model
topic_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")


# Function to resample audio to 16kHz if necessary
def resample_audio(audio_path, target_sr=16000):
    audio_input, original_sr = librosa.load(audio_path, sr=None)  # Load audio with original sampling rate
    if original_sr != target_sr:
        audio_input = librosa.resample(audio_input, orig_sr=original_sr, target_sr=target_sr)  # Resample to 16kHz
    return audio_input, target_sr

# Function to transcribe audio using Wav2Vec2
def transcribe_audio(audio_path):
    # Load and preprocess audio
    audio_input, sample_rate = resample_audio(audio_path)
    inputs = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt", padding=True)

    # Get predictions
    with torch.no_grad():
        logits = transcription_model(**inputs).logits

    # Decode predictions
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)[0]
    return transcription

# Function to classify the transcription into topics
def classify_topic(transcription):
    # Tokenize the transcription and pass it through the BERT classifier
    inputs = topic_tokenizer(transcription, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = topic_model(**inputs)
    
    # Get the predicted label (0 for Customer Service, 1 for Retention Service, etc.)
    predicted_class = torch.argmax(outputs.logits, dim=1).item()

    # Map prediction to a topic
    if predicted_class == 0:
        return "Customer Service"
    elif predicted_class == 1:
        return "Retention Service"
    else:
        return "Other"

# Function to transcribe, summarize, and classify topic
def transcribe_and_summarize(audio_file):
    # Transcription
    transcription = transcribe_audio(audio_file)

    # Summarization
    summary = summarizer(transcription, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]

    # Topic classification
    topic = classify_topic(transcription)

    return transcription, summary, topic

# Gradio Interface
inputs = gr.Audio(type="filepath", label="Upload your audio file")
outputs = [
    gr.Textbox(label="Transcription"),
    gr.Textbox(label="Summary"),
    gr.Textbox(label="Topic")
]

app = gr.Interface(
    fn=transcribe_and_summarize,
    inputs=inputs,
    outputs=outputs,
    title="Moroccan Darija Audio Transcription, Summarization, and Topic Classification (JABRI)",
    description="Upload an audio file in Moroccan Darija to get its transcription, a summarized version of the content, and the detected topic."
)

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