app.py

import gradio as gr
import openai
import deepgram_sdk
import assemblyai
from transformers import pipeline

# Initialize sentiment analysis model
sentiment_analyzer = pipeline("text-classification", model="Vasanth/tamil-sentiment-distilbert")

# Placeholder transcription functions - Replace with your actual API implementation
def whisper_openai_transcribe(audio_file):
    # Replace with actual Whisper API transcription code
    transcription = "This is a dummy transcription from Whisper OpenAI API"
    return transcription

def deepgram_transcribe(audio_file):
    # Replace with actual Deepgram API transcription code
    transcription = "This is a dummy transcription from Deepgram API"
    return transcription

def assemblyai_transcribe(audio_file):
    # Replace with actual Assembly AI transcription code
    transcription = "This is a dummy transcription from Assembly AI API"
    return transcription

# Sentiment analysis function
def analyze_sentiment(text):
    sentiment = sentiment_analyzer(text)
    return sentiment[0]['label'], sentiment[0]['score']

# Main function to process audio and sentiment analysis
def process_transcription_and_sentiment(audio_file, model_choice):
    # Transcription
    if model_choice == "Whisper OpenAI":
        transcription = whisper_openai_transcribe(audio_file)
    elif model_choice == "Deepgram API":
        transcription = deepgram_transcribe(audio_file)
    elif model_choice == "Assembly AI API":
        transcription = assemblyai_transcribe(audio_file)
    
    # Sentiment analysis
    sentiment_label, sentiment_score = analyze_sentiment(transcription)
    
    return transcription, f"Sentiment: {sentiment_label} with score {sentiment_score}"

# Gradio interface setup
def create_interface():
    with gr.Blocks() as demo:
        gr.Markdown("### Audio Transcription and Sentiment Analysis")
        
        with gr.Row():
            audio_input = gr.Audio(source="upload", type="file", label="Upload Audio File")
            model_choice = gr.Dropdown(
                choices=["Whisper OpenAI", "Deepgram API", "Assembly AI API"],
                label="Choose Transcription Model",
                value="Whisper OpenAI"
            )
        
        # Textboxes for transcription and sentiment analysis
        transcription_output = gr.Textbox(label="Transcription", lines=5)
        sentiment_output = gr.Textbox(label="Sentiment Analysis", lines=5)
        
        # Submit button
        submit_button = gr.Button("Process")
        
        # When the button is clicked, call the `process_transcription_and_sentiment` function
        submit_button.click(process_transcription_and_sentiment, 
                            inputs=[audio_input, model_choice], 
                            outputs=[transcription_output, sentiment_output])
        
    demo.launch()

if __name__ == "__main__":
    create_interface()