app.py

from transformers import pipeline,Conversation
import gradio as gr

pipe = pipeline(
    "automatic-speech-recognition",
    model="openai/whisper-base",
)

model= pipeline("conversational", model="facebook/blenderbot-400M-distill")



# Get a response

# Initialize the conversational model
# conversational_model = pipeline("conversational", model="microsoft/DialoGPT-medium")

# Mock implementation of transcribe_speech for demonstration
def transcribe_speech(filepath):
    output = pipe(
        filepath,
        max_new_tokens=256,
        generate_kwargs={
            "task": "transcribe",
            "language": "english",
        },  # update with the language you've fine-tuned on
        chunk_length_s=30,
        batch_size=8,
    )
    return output["text"]

def handle_audio_input(audio_file):
    try:
        # Step 1: Transcribe the audio
        transcribed_text = transcribe_speech(audio_file)
        print(f"Transcribed text: {transcribed_text}")

        # Step 2: Create a conversation and generate a response from transcribed text
        conversation = Conversation(transcribed_text)
        response = model(conversation)
        chatbot_response = response.generated_responses[-1]
        print(f"Chatbot response: {chatbot_response}")

        return transcribed_text, chatbot_response
    except Exception as e:
        print(f"Error: {e}")
        return "Error in processing audio", str(e)

# Create the Gradio Blocks container
with gr.Blocks() as demo:
    gr.Markdown("## Customer query audio to text chatbot")

    with gr.Tab("Microphone"):
        mic_transcribe = gr.Interface(
            fn=handle_audio_input,
            inputs=gr.Audio(sources="microphone", type="filepath"),
            outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Chatbot Response")],
        )
        mic_transcribe.render()

    with gr.Tab("File Upload"):
        file_transcribe = gr.Interface(
            fn=handle_audio_input,
            inputs=gr.Audio(sources="upload", type="filepath"),
            outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Chatbot Response")],
        )
        file_transcribe.render()

# Launch the Gradio app
demo.launch(share=True)