app.py

import gradio as gr
from PIL import Image
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import soundfile as sf
import torch

# Load pre-trained model and tokenizer
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
gpt2_model = GPT2LMHeadModel.from_pretrained("gpt2")

# Load pre-trained model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

# Placeholder function to handle text input
def handle_text(text):
    # encode the new user input, add the eos_token and return a tensor in Pytorch
    new_user_input_ids = gpt2_tokenizer.encode(text + gpt2_tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = new_user_input_ids

    # generate a response
    chat_history_ids = gpt2_model.generate(bot_input_ids, max_length=1000, pad_token_id=gpt2_tokenizer.eos_token_id)

    # Print the generated chat
    chat_output = gpt2_tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    return chat_output

# Placeholder function to handle image input
def handle_image(img):
    # This is a placeholder function, replace with your own image processing function
    return "This image seems nice!"

# Placeholder function to handle audio input
def handle_audio(audio):
    # load audio
    speech, _ = sf.read(audio)
    # transcribe speech to text
    input_values = processor(speech, return_tensors="pt").input_values
    # perform forward pass
    logits = model(input_values).logits
    # take argmax and decode
    predicted_ids = torch.argmax(logits, dim=-1)
    transcriptions = processor.decode(predicted_ids[0])
    return handle_text(transcriptions)

def chatbot(inputs):
    text, img, audio = inputs
    text_output = handle_text(text) if text else None
    img_output = handle_image(img) if img else None
    audio_output = handle_audio(audio) if audio else None

    outputs = [o for o in [text_output, img_output, audio_output] if o]
    return "\n".join(outputs)

# Define the Gradio interface
iface = gr.Interface(
    fn=chatbot,
    inputs=[
        gr.inputs.Textbox(lines=2, placeholder="Input Text here..."),
        gr.inputs.Image(label="Upload Image"),
        gr.inputs.Audio(label="Input Audio"),
    ],
    outputs=gr.outputs.Textbox(label="Output"),
    title="Multimodal Chatbot",
    description="This chatbot can handle text, image, and audio inputs. Try it out!",
)

# Launch the Gradio interface
iface.launch()