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

# Load pretrained models from Hugging Face
nlp_pipeline = pipeline("text-generation", model="gpt2")  # For text generation (voice assistant)
speech_recognition_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
speech_recognition_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")

# Function for voice-to-text conversion using Wav2Vec2
def recognize_speech(audio):
    # Process the audio file
    input_values = speech_recognition_processor(audio, return_tensors="pt").input_values
    with torch.no_grad():
        logits = speech_recognition_model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    # Decode the prediction
    transcription = speech_recognition_processor.decode(predicted_ids[0])
    return transcription

# Function for generating device commands using GPT-2 (e.g., for controlling smart devices)
def generate_response(user_input):
    response = nlp_pipeline(user_input, max_length=50, num_return_sequences=1)[0]['generated_text']
    return response

# Gradio Interface
def interact_with_system(audio=None, user_input=None):
    if audio:
        # Convert speech to text
        transcription = recognize_speech(audio)
        return transcription
    elif user_input:
        # Generate response to control devices
        response = generate_response(user_input)
        return response
    else:
        return "Please provide either voice or text input."

# Create a Gradio interface
interface = gr.Interface(
    fn=interact_with_system, 
    inputs=[
        gr.Audio(source="microphone", type="numpy", label="Voice Command"),  # Voice input
        gr.Textbox(label="Text Command")  # Text input
    ], 
    outputs="text",  # Output the text response (device control command)
    title="AI-Driven Consumer Device Ecosystem", 
    description="Use voice or text commands to interact with smart devices in your ecosystem."
)

# Launch the interface
interface.launch()