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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.Blocks()
# with demo:
# with gr.Row():
# text_input = gr.Textbox()
# choice = gr.Radio(choices=["Chat","Music","Image"])
# with gr.Row():
# chatbot_output = gr.Textbox()
# music_output =gr.Audio()
# image_output =gr.Image()
# submit_btn = gr.Button("Generate")
# submit_btn.click(fn=process_input,inputs=[text_input,choice],outputs=[chatbot_output,music_output,image_output])
# demo.launch(debug=True)
demo =gr.Interface(
fn=process_input,
inputs=[gr.Textbox(),gr.Radio(["Chat","Music","Image"])],
outputs = [gr.Textbox(),gr.Audio(),gr.Image()],
# outputs =["text","audio","image"]
title="Multimodal Assistant"
)
demo.launch(debug=True) |