app.py

import gradio as gr
import torch
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, pipeline
import soundfile as sf

# 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")

# Function to transcribe audio
def transcribe_audio(audio_path):
    audio_input, sample_rate = sf.read(audio_path)
    if sample_rate != 16000:
        raise ValueError("Audio must be sampled at 16kHz.")
    inputs = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = transcription_model(**inputs).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)[0]
    return transcription

# Function to analyze topics from summary
def analyze_topics(summary):
    if "customer service" in summary.lower():
        return "Customer Service"
    elif "retention" in summary.lower():
        return "Retention"
    else:
        return "Unknown"

# Function to transcribe, summarize, and analyze topics
def transcribe_summarize_analyze(audio_file):
    # Transcription
    transcription = transcribe_audio(audio_file)

    # Summarization
    summary = summarizer(transcription, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]

    # Topic Analysis
    topic = analyze_topics(summary)

    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_summarize_analyze,
    inputs=inputs,
    outputs=outputs,
    title="Moroccan Darija Audio Analysis",
    description=(
        "Upload an audio file in Moroccan Darija to get its transcription, a summarized version, "
        "and the detected topic (Customer Service or Retention)."
    )
)

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