Update app.py

app.py

@@ -4,11 +4,6 @@ import random
 import torch
 import spaces
 from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-from threading import Thread
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
 
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
 
@@ -38,162 +33,18 @@ examples = [
 ]
 
 
-# Image Gen css - Only saving for backup - ***disregard***
-#css="""
-#col-container {
-#    margin: 0 auto;
-#    max-width: 520px;
-#}
-#"""
 
-# Llama + Flux CSS
-CSS = """
-.duplicate-button {
-    margin: auto !important;
-    color: white !important;
-    background: black !important;
-    border-radius: 100vh !important;
-}
-h3 {
-    text-align: center;
-}
-.chatbox .messages .message.user {
-    background-color: #e1f5fe;
-}
-.chatbox .messages .message.bot {
-    background-color: #eeeeee;
-}
-#col-container {
+css="""
+col-container {
     margin: 0 auto;
     max-width: 520px;
 }
 """
-#################################################################################################################################
-##########################################
-
-
-model_id = "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16,
-  device_map="sequential",
-  offload_folder="offload",
-  offload_state_dict=True 
-)
-
-TITLE = "Quick Description"
-
-DESCRIPTION = """
-Generate a longer description for your image from a simple basic prompt
-"""
-
-
-
-@spaces.GPU(duration=120)
-def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
-    print(f'Message: {message}')
-    print(f'History: {history}')
-    
-    conversation = []
-    for prompt, answer in history:
-        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
-    conversation.append({"role": "user", "content": message})
-    
-    input_ids = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
-    inputs = tokenizer(input_ids, return_tensors="pt").to(0)
-    
-    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
-
-    generate_kwargs = dict(
-        inputs, 
-        streamer=streamer,
-        top_k=top_k,
-        top_p=top_p,
-        repetition_penalty=penalty,
-        max_new_tokens=max_new_tokens, 
-        do_sample=True, 
-        temperature=temperature,
-        eos_token_id=[128001, 128009],
-    )
-    
-    thread = Thread(target=model.generate, kwargs=generate_kwargs)
-    thread.start()
-
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        yield buffer
 
-chatbot = gr.Chatbot(height=500)
-
-with gr.Blocks(css=CSS) as demo:
-    gr.HTML(TITLE)
-    gr.HTML(DESCRIPTION)
-    gr.ChatInterface(
-        fn=stream_chat,
-        chatbot=chatbot,
-        fill_height=True,
-        theme="soft",
-        retry_btn=None,
-        undo_btn="Delete Previous",
-        clear_btn="Clear",
-        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-        additional_inputs=[
-            gr.Slider(
-                minimum=0,
-                maximum=1,
-                step=0.1,
-                value=0.8,
-                label="Temperature",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=128,
-                maximum=4096,
-                step=1,
-                value=1024,
-                label="Max new tokens",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                step=0.1,
-                value=0.8,
-                label="top_p",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=1,
-                maximum=20,
-                step=1,
-                value=20,
-                label="top_k",
-                render=False,
-            ),
-            gr.Slider(
-                minimum=0.0,
-                maximum=2.0,
-                step=0.1,
-                value=1.2,
-                label="Repetition penalty",
-                render=False,
-            ),
-        ],
-        examples=[
-            ["Explain Deep Learning as a pirate."],
-            ["Give me five ideas for a child's summer science project."],
-            ["Provide advice for writing a script for a puzzle game."],
-            ["Create a tutorial for building a breakout game using markdown."]
-        ],
-        cache_examples=False,
-    )
-#################################################################################################################################
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""TESTTESTTESTTESTTESTTEST]
+        gr.Markdown(f"""Generate an image with Flux. Please review the license if you are not familiar with it. Not for Commercial Use!]
         """)
         
         with gr.Row():
@@ -266,13 +117,6 @@ with gr.Blocks(css=css) as demo:
             cache_examples="lazy"
         )
 
-        def greet(name):
-            return "Hello " + name + "! Imagine an image with Flux"
-
-        name = gr.Textbox(label="Name")
-        output = gr.Textbox(label="Output Box")
-        greet_btn = gr.Button("Greet")
-        greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
 
     gr.on(
         triggers=[run_button.click, prompt.submit],