Update app.py

app.py

@@ -11,7 +11,7 @@ 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)"
+DESCRIPTION = "#Product Describe by Fluxi IA\n### Base Model [Florence-2] (https://huggingface.co/microsoft/Florence-2-large) ]"
 
 @spaces.GPU
 def run_example(task_prompt, image):
@@ -61,14 +61,31 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     gr.Markdown(DESCRIPTION)
-    with gr.Tab(label="Florence-2 Image Captioning"):
+    with gr.Tab(label="Product Image Select"):
         with gr.Row():
             with gr.Column():
                 input_img = gr.Image(label="Input Picture")
                 submit_btn = gr.Button(value="Submit")
             with gr.Column():
                 output_text = gr.HTML(label="Output Text", elem_id="output")
+        
+        gr.Markdown("""
+        ## How to use via API
+        To use this model via API, you can follow the example code below:
 
+        ```python
+        !pip install gradio_client
+        from gradio_client import Client, handle_file
+
+        client = Client("J-LAB/Fluxi-IA")
+        result = client.predict(
+            image=handle_file('https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'),
+            api_name="/process_image"
+        )
+        print(result)
+        ```
+        """)
+        
         submit_btn.click(process_image, [input_img], [output_text])
 
 demo.launch(debug=True)