Update app.py

app.py

@@ -7,27 +7,15 @@ from PIL import Image
 import subprocess
 subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
-models = {
-    'J-LAB/Florence_2_B_FluxiAI_Product_Caption': AutoModelForCausalLM.from_pretrained('J-LAB/Florence_2_B_FluxiAI_Product_Caption', trust_remote_code=True).to("cuda").eval(),
-    'J-LAB/Florence_2_L_FluxiAI_Product_Caption': AutoModelForCausalLM.from_pretrained('J-LAB/Florence_2_L_FluxiAI_Product_Caption', trust_remote_code=True).to("cuda").eval()
-}
-
-processors = {
-    'J-LAB/Florence_2_B_FluxiAI_Product_Caption': AutoProcessor.from_pretrained('J-LAB/Florence_2_B_FluxiAI_Product_Caption', trust_remote_code=True),
-    'J-LAB/Florence_2_L_FluxiAI_Product_Caption': AutoProcessor.from_pretrained('J-LAB/Florence_2_L_FluxiAI_Product_Caption', trust_remote_code=True)
-}
+model_id = 'J-LAB/Florence_2_B_FluxiAI_Product_Caption'
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda").eval()
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
 
 DESCRIPTION = "# [Florence-2 Product Describe by Fluxi IA](https://huggingface.co/microsoft/Florence-2-large)"
 
 @spaces.GPU
-def run_example(task_prompt, image, text_input=None, model_id='J-LAB/Florence-Idesire'):
-    model = models[model_id]
-    processor = processors[model_id]
-    if text_input is None:
-        prompt = task_prompt
-    else:
-        prompt = task_prompt + text_input
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+def run_example(task_prompt, image):
+    inputs = processor(text=task_prompt, images=image, return_tensors="pt").to("cuda")
     generated_ids = model.generate(
         input_ids=inputs["input_ids"],
         pixel_values=inputs["pixel_values"],
@@ -43,18 +31,12 @@ def run_example(task_prompt, image, text_input=None, model_id='J-LAB/Florence-Id
         image_size=(image.width, image.height)
     )
     return parsed_answer
-    
-def process_image(image, task_prompt, text_input=None, model_id='J-LAB/Florence_2_B_FluxiAI_Product_Caption'):
-    image = Image.fromarray(image)  # Convert NumPy array to PIL Image
-    if task_prompt == 'Product Caption':
-        task_prompt = '<PC>'
-        results = run_example(task_prompt, image, model_id=model_id)
-    elif task_prompt == 'More Detailed Caption':
-        task_prompt = '<MORE_DETAILED_CAPTION>'
-        results = run_example(task_prompt, image, model_id=model_id)
-    else:
-        return "", None  # Return empty string and None for unknown task prompts
 
+def process_image(image):
+    image = Image.fromarray(image)  # Convert NumPy array to PIL Image
+    task_prompt = '<PC>'
+    results = run_example(task_prompt, image)
+    
     # Remove the key and get the text value
     if results and task_prompt in results:
         output_text = results[task_prompt]
@@ -71,27 +53,22 @@ css = """
     height: 500px; 
     overflow: auto; 
     border: 1px solid #ccc; 
+    padding: 10px;
+    background-color: #f9f9f9;
   }
 """
 
-single_task_list = [
-    'Product Caption', 'More Detailed Caption'
-]
-
 with gr.Blocks(css=css) as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Tab(label="Florence-2 Image Captioning"):
         with gr.Row():
             with gr.Column():
                 input_img = gr.Image(label="Input Picture")
-                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='J-LAB/Florence_2_B_FluxiAI_Product_Caption')
-                task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task')
-                task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption")
-                text_input = gr.Textbox(label="Text Input (optional)")
                 submit_btn = gr.Button(value="Submit")
             with gr.Column():
-                output_text = gr.HTML(label="Output Text")
+                with gr.Box():
+                    output_text = gr.HTML(label="Output Text", elem_id="output")
 
-        submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text])
+        submit_btn.click(process_image, [input_img], [output_text])
 
 demo.launch(debug=True)