app.py

from transformers import pipeline, Conversation
import gradio as gr
from diffusers import DiffusionPipeline
import scipy

#Initializing Models
chatbot = pipeline(model="facebook/blenderbot-400M-distill")
ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
synthesiser = pipeline("text-to-audio", "facebook/musicgen-small")

message_list = []
response_list = []
def vanilla_chatbot(message):
    conversation = Conversation(text=message, past_user_inputs=message_list, generated_responses=response_list)
    bot = chatbot(conversation.messages[0]['content']) # working code
    return bot[-1]['generated_text']

def generate_image(Prompt):
    images = ldm([Prompt], num_inference_steps=50, eta=.3, guidance_scale=6)
    return images.images[0]

def generate_music(Prompt):
    music = synthesiser(Prompt, forward_params={"do_sample": True, "max_new_tokens":100})
    rate = music["sampling_rate"]
    mus = music["audio"][0].reshape(-1)
    return rate,mus

def process_input(Prompt,choice):
    if choice == "Chat":
        return vanilla_chatbot(Prompt),None,None
    elif choice == 'Music':
        rate,audio = generate_music(Prompt)
        return None, (rate,audio), None
    else:
        return None , None , generate_image(Prompt)

demo=gr.Interface(fn=process_input,inputs=["text",gr.Radio(["Chat","Music","Image"])],outputs=["text", "audio","image"],title="Multimodal assistance")
demo.launch(debug=True)