Create app.py

app.py

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+import gradio as gr
+import subprocess
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# Attempt to install flash-attn
+try:
+    subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, check=True, shell=True)
+except subprocess.CalledProcessError as e:
+    print(f"Error installing flash-attn: {e}")
+    print("Continuing without flash-attn.")
+
+# Determine the device to use
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+# Load the large model and processor
+try:
+    vision_language_model_large = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to(device).eval()
+    vision_language_processor_large = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True)
+except Exception as e:
+    print(f"Error loading large model: {e}")
+    vision_language_model_large = None
+    vision_language_processor_large = None
+
+def describe_image(uploaded_image, model_choice):
+    """
+    Generates a detailed description of the input image using the selected model.
+    Args:
+        uploaded_image (PIL.Image.Image): The image to describe.
+        model_choice (str): The model to use, either "Base" or "Large".
+    Returns:
+        str: A detailed textual description of the image or an error message.
+    """
+    if uploaded_image is None:
+        return "Please upload an image."
+
+    if model_choice == "Base":
+        if vision_language_model_base is None:
+            return "Base model failed to load."
+        model = vision_language_model_base
+        processor = vision_language_processor_base
+    elif model_choice == "Large":
+        if vision_language_model_large is None:
+            return "Large model failed to load."
+        model = vision_language_model_large
+        processor = vision_language_processor_large
+    else:
+        return "Invalid model choice."
+
+    if not isinstance(uploaded_image, Image.Image):
+        uploaded_image = Image.fromarray(uploaded_image)
+
+    inputs = processor(text="<MORE_DETAILED_CAPTION>", images=uploaded_image, return_tensors="pt").to(device)
+    with torch.no_grad():
+        generated_ids = 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 = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    processed_description = processor.post_process_generation(
+        generated_text,
+        task="<MORE_DETAILED_CAPTION>",
+        image_size=(uploaded_image.width, uploaded_image.height)
+    )
+    image_description = processed_description["<MORE_DETAILED_CAPTION>"]
+    print("\nImage description generated!:", image_description)
+    return image_description
+
+# Description for the interface
+description = "Select the model to use for generating the image description. 'Base' is smaller and faster, while 'Large' is more accurate but slower."
+if device == "cpu":
+    description += " Note: Running on CPU, which may be slow for large models."
+
+# Create the Gradio interface
+image_description_interface = gr.Interface(
+    fn=describe_image,
+    inputs=[
+        gr.Image(label="Upload Image", type="pil"),
+        gr.Radio(["Base", "Large"], label="Model Choice", value="Base")
+    ],
+    outputs=gr.Textbox(label="Generated Caption", lines=4, show_copy_button=True),
+    live=False,
+    title="Florence-2 Models Image Captions",
+    description=description
+)
+
+# Launch the interface
+image_description_interface.launch(debug=True, ssr_mode=False)