Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-from groq import Groq  # Import the Groq library
+from groq import Groq
 from cryptography.fernet import Fernet
 from huggingface_hub import login
 import os
@@ -8,9 +8,14 @@ os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '120'
 import numpy as np
 import random
 import spaces
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from io import BytesIO
+import base64
+
+
+
 os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '120'
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -28,9 +33,14 @@ def get_hf_token(encrypted_token):
     # Decrypt and decode the token
     decrypted_token = f.decrypt(encrypted_token).decode()
     return decrypted_token
+    
 groq_client = Groq(api_key="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")
 decrypted_token = get_hf_token("gAAAAABn3GfShExoJd50nau3B5ZJNiQ9dRD1ACO3XXMwVaIQMkmi59cL-MKGr6SYnsB0E2gGITJG2j29Ar9yjaZP-EC6hHsCBmwKSj4aFtTor9_n0_NdMBv1GtlxZRmwnQwriB-Xr94e")
 login(token=decrypted_token)
+decrypted_token = get_hf_token("gAAAAABn3GfShExoJd50nau3B5ZJNiQ9dRD1ACO3XXMwVaIQMkmi59cL-MKGr6SYnsB0E2gGITJG2j29Ar9yjaZP-EC6hHsCBmwKSj4aFtTor9_n0_NdMBv1GtlxZRmwnQwriB-Xr94e")
+login(token=decrypted_token)
+groq_client = Groq(api_key="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")
+
 
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
@@ -42,9 +52,45 @@ MAX_IMAGE_SIZE = 2048
 
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
-decrypted_token = get_hf_token("gAAAAABn3GfShExoJd50nau3B5ZJNiQ9dRD1ACO3XXMwVaIQMkmi59cL-MKGr6SYnsB0E2gGITJG2j29Ar9yjaZP-EC6hHsCBmwKSj4aFtTor9_n0_NdMBv1GtlxZRmwnQwriB-Xr94e")
-login(token=decrypted_token)
-groq_client = Groq(api_key="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")
+
+# History functions
+def append_to_history(image, prompt, seed, width, height, guidance_scale, steps, history):
+    if image is None:
+        return history
+    
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    img_bytes = buffered.getvalue()
+    
+    return history + [{
+        "image": img_bytes,
+        "prompt": prompt,
+        "seed": seed,
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "steps": steps,
+    }]
+
+def create_history_html(history):
+    html = "<div style='display: flex; flex-direction: column; gap: 20px; margin: 20px;'>"
+    for i, entry in enumerate(reversed(history)):
+        img_str = base64.b64encode(entry["image"]).decode()
+        html += f"""
+        <div style='display: flex; gap: 20px; padding: 20px; background: #f5f5f5; border-radius: 10px;'>
+            <img src="data:image/png;base64,{img_str}" style="width: 150px; height: 150px; object-fit: cover; border-radius: 5px;"/>
+            <div style='flex: 1;'>
+                <h3 style='margin: 0;'>Generation #{len(history)-i}</h3>
+                <p><strong>Prompt:</strong> {entry["prompt"]}</p>
+                <p><strong>Seed:</strong> {entry["seed"]}</p>
+                <p><strong>Size:</strong> {entry["width"]}x{entry["height"]}</p>
+                <p><strong>Guidance:</strong> {entry["guidance_scale"]}</p>
+                <p><strong>Steps:</strong> {entry["steps"]}</p>
+            </div>
+        </div>
+        """
+    return html + "</div>" if history else "<p style='margin: 20px;'>No generations yet</p>"
+
 
 @spaces.GPU(duration=75)
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
@@ -121,88 +167,79 @@ css = """
 """
 
 with gr.Blocks(css=css) as demo:
+    history_state = gr.State([])
+    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# FLUX.1 [dev] with Prompt Enhancement")
+        gr.Markdown("# FLUX.1 [dev] with History Tracking")
+        
+        # Prompt section
         gr.Markdown("### Step 1: Enhance Your Prompt")
-        # Original prompt input and enhancement button
-        original_prompt = gr.Textbox(
-            label="Original Prompt",
-            placeholder="Enter your idea here...",
-            lines=2
-        )
+        original_prompt = gr.Textbox(label="Original Prompt", lines=2)
         enhance_button = gr.Button("Enhance Prompt")
-        # Editable textbox that will hold the enhanced prompt
-        enhanced_prompt = gr.Textbox(
-            label="Enhanced Prompt (Editable)",
-            placeholder="The enhanced prompt will appear here...",
-            lines=2
-        )
-        # When clicked, this button calls the enhance_prompt function.
-        enhance_button.click(fn=enhance_prompt, inputs=original_prompt, outputs=enhanced_prompt)
+        enhanced_prompt = gr.Textbox(label="Enhanced Prompt (Editable)", lines=2)
+        enhance_button.click(enhance_prompt, original_prompt, enhanced_prompt)
         
-        gr.Markdown("### Step 2: Generate Image Using Enhanced Prompt")
+        # Generation section
+        gr.Markdown("### Step 2: Generate Image")
         with gr.Row():
-            run_button = gr.Button("Generate Image", scale=0)
+            run_button = gr.Button("Generate Image", variant="primary")
         result = gr.Image(label="Result", show_label=False)
         
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        # Advanced settings
+        with gr.Accordion("Advanced Settings"):
+            seed = gr.Slider(0, MAX_SEED, value=0, label="Seed")
+            randomize_seed = gr.Checkbox(True, label="Randomize seed")
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
+                width = gr.Slider(256, MAX_IMAGE_SIZE, 1024, step=32, label="Width")
+                height = gr.Slider(256, MAX_IMAGE_SIZE, 1024, step=32, label="Height")
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,
-                )
+                guidance_scale = gr.Slider(1, 15, 3.5, step=0.1, label="Guidance Scale")
+                num_inference_steps = gr.Slider(1, 50, 28, step=1, label="Inference Steps")
+        
+        # History section
+        with gr.Accordion("Generation History", open=False):
+            history_display = gr.HTML("<p style='margin: 20px;'>No generations yet</p>")
         
+        # Examples
         gr.Examples(
             examples=[
                 "a tiny astronaut hatching from an egg on the moon",
                 "a cat holding a sign that says hello world",
                 "an anime illustration of a wiener schnitzel",
             ],
-            fn=infer,
-            inputs=enhanced_prompt,  # Uses the enhanced prompt
+            inputs=enhanced_prompt,
             outputs=[result, seed],
+            fn=infer,
             cache_examples="lazy"
         )
-    
-    # Trigger the original image generation code using the enhanced prompt.
-    gr.on(
-        triggers=[run_button.click, enhanced_prompt.submit],
+
+    # Event handling
+    generation_event = run_button.click(
+        fn=infer,
+        inputs=[enhanced_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
+    ).then(
+        fn=append_to_history,
+        inputs=[result, enhanced_prompt, seed, width, height, guidance_scale, num_inference_steps, history_state],
+        outputs=history_state
+    ).then(
+        fn=create_history_html,
+        inputs=history_state,
+        outputs=history_display
+    )
+
+    enhanced_prompt.submit(
         fn=infer,
         inputs=[enhanced_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed]
+    ).then(
+        fn=append_to_history,
+        inputs=[result, enhanced_prompt, seed, width, height, guidance_scale, num_inference_steps, history_state],
+        outputs=history_state
+    ).then(
+        fn=create_history_html,
+        inputs=history_state,
+        outputs=history_display
     )
 
 demo.launch(share=True)