Update app.py

app.py

@@ -1,29 +1,51 @@
 from transformers import pipeline, Conversation
 import gradio as gr
 from diffusers import DiffusionPipeline
-import gradio as gr
-from transformers import pipeline
 import scipy
-import gradio as gr
+
+#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, history):
+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']
 
-
-model_id = "CompVis/ldm-text2im-large-256"
-ldm = DiffusionPipeline.from_pretrained(model_id)
 def generate_image(Prompt):
     images = ldm([Prompt], num_inference_steps=50, eta=.3, guidance_scale=6)
     return images.images[0]
 
-
-synthesiser = pipeline("text-to-audio", "facebook/musicgen-small")
 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(text),None,None
+    elif choice == 'Music':
+        rate,audio = generate_music(Prompt)
+        return None, (rate,audio), None
+    else:
+        return None , None , generate_image(Prompt)
+
+with gr.Blocks as 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)