Update app.py

app.py

@@ -27,7 +27,7 @@ qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
 qwen_processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux", trust_remote_code=True)
 
 # Prompt Enhancer
-enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)
+enhancer_long = pipeline("summarization", model="prithivMLmods/t5-Flan-Prompt-Enhance", device=device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
@@ -138,36 +138,33 @@ title = """<h1 align="center">FLUX.1-dev with Qwen2VL Captioner and Prompt Enhan
 <p><center>
 <a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" target="_blank">[FLUX.1-dev Model]</a>
 <a href="https://huggingface.co/prithivMLmods/JSONify-Flux" target="_blank">[JSONify Flux Model]</a>
-<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
+<a href="https://huggingface.co/prithivMLmods/t5-Flan-Prompt-Enhance" target="_blank">[Prompt Enhancer t5]</a>
 <p align="center">Create long prompts from images or enhance your short prompts with prompt enhancer</p>
 </center></p>
 """
 
 with gr.Blocks(css=custom_css) as demo:
+    gr.HTML(title)
     
-    # Sidebar with About details
     with gr.Sidebar(label="Parameters", open=True):
         gr.Markdown(
             """
             ### About
             
-            #### FLUX.1-Dev
-            FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.  
-            FLUX.1 [dev] is an open-weight, guidance-distilled model for non-commercial applications. Directly distilled from FLUX.1 [pro], FLUX.1 [dev] obtains similar quality and prompt adherence capabilities, while being more efficient than a standard model of the same size.  
-            [FLUX.1-dev on Hugging Face](https://huggingface.co/black-forest-labs/FLUX.1-dev)
+            #### Flux.1-Dev
+            FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. FLUX.1 [dev] is an open-weight, guidance-distilled model for non-commercial applications. Directly distilled from FLUX.1 [pro], FLUX.1 [dev] obtains similar quality and prompt adherence capabilities, while being more efficient than a standard model of the same size.  
+            [FLUX.1-dev Model](https://huggingface.co/black-forest-labs/FLUX.1-dev)
             
             #### JSONify-Flux
-            JSONify-Flux is a multimodal image-text model based on the dataset of flux-generated images and architectures and built upon the Qwen2VL architecture. The JSON-base instruction has been manually removed to avoid JSON formatted captions.  
-            [JSONify-Flux on Hugging Face](https://huggingface.co/prithivMLmods/JSONify-Flux)
+            JSONify-Flux is a multimodal image-text-text model trained on a dataset of FLUX-generated images with context-rich captions based on the Qwen2VL architecture. The JSON-based instruction has been manually removed to avoid JSON format captions.  
+            [JSONify-Flux Model](https://huggingface.co/prithivMLmods/JSONify-Flux)
             
             #### t5-Flan-Prompt-Enhance
-            t5-Flan-Prompt-Enhance is a prompt summarization model based on flux synthetic prompts designed to enhance the richness of prompt details.  
-            [t5-Flan-Prompt-Enhance on Hugging Face](https://huggingface.co/prithivMLmods/t5-Flan-Prompt-Enhance)
+            t5-Flan-Prompt-Enhance is a prompt summarization model that enriches synthetic FLUX prompts with more detailed descriptions.  
+            [t5-Flan-Prompt-Enhance Model](https://huggingface.co/prithivMLmods/t5-Flan-Prompt-Enhance)
             """
         )
     
-    gr.HTML(title)
-    
     with gr.Row():
         with gr.Column(scale=1):
             with gr.Group(elem_classes="input-group"):
@@ -181,15 +178,15 @@ with gr.Blocks(css=custom_css) as demo:
                 width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
                 height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
                 guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
-                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=20)
+                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=32)
             
-            generate_btn = gr.Button("Generate Image + Prompt Enhanced", elem_classes="submit-btn")
+            generate_btn = gr.Button("Generate Image & Prompt", elem_classes="submit-btn")
         
         with gr.Column(scale=1):
             with gr.Group(elem_classes="output-group"):
-                output_image = gr.Image(label="Result", elem_id="gallery", show_label=False)
-                final_prompt = gr.Textbox(label="Final Prompt Used")
-                used_seed = gr.Number(label="Seed Used")
+                output_image = gr.Image(label="result", elem_id="gallery", show_label=False)
+                final_prompt = gr.Textbox(label="prompt")
+                used_seed = gr.Number(label="seed")
     
     generate_btn.click(
         fn=process_workflow,
@@ -200,4 +197,4 @@ with gr.Blocks(css=custom_css) as demo:
         outputs=[output_image, final_prompt, used_seed]
     )
 
-demo.launch(debug=True)
+demo.launch(debug=True)