Update app.py

app.py

@@ -1,3 +1,13 @@
+import gradio as gr
+import random
+import os
+import torch
+import subprocess
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+from diffusers import DiffusionPipeline
+
 import gradio as gr
 import cv2
 import numpy as np
@@ -327,6 +337,47 @@ with gr.Blocks(theme=theme, css=css) as app:
                 inputs=[image_input, filter_type],
                 outputs=image_output
             )
+
+    with gr.Tab("Image to Prompt"): 
+        subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+        # Initialize Florence model
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
+        florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
+
+        # api_key = os.getenv("HF_READ_TOKEN")
+        
+        def generate_caption(image):
+            if not isinstance(image, Image.Image):
+                image = Image.fromarray(image)
+            
+            inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
+            generated_ids = florence_model.generate(
+                input_ids=inputs["input_ids"],
+                pixel_values=inputs["pixel_values"],
+                max_new_tokens=1024,
+                early_stopping=False,
+                do_sample=False,
+                num_beams=3,
+            )
+            generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+            parsed_answer = florence_processor.post_process_generation(
+                generated_text,
+                task="<MORE_DETAILED_CAPTION>",
+                image_size=(image.width, image.height)
+            )
+            prompt =  parsed_answer["<MORE_DETAILED_CAPTION>"]
+            print("\n\nGeneration completed!:"+ prompt)
+            return prompt
+
+        io = gr.Interface(generate_caption,
+            inputs=[gr.Image(label="Input Image")],
+            outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
+            # gr.Image(label="Output Image")
+                       ]
+                    )
+            
     with gr.Tab("Image Upscaler"):    
         with gr.Row():
             with gr.Column():