Update app.py

app.py

@@ -1,34 +1,60 @@
 import gradio as gr
-import requests
+import subprocess
+import torch
 from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-import time
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
-
-def caption(img, min_len, max_len):
-    raw_image = Image.open(img).convert('RGB')
-  
-    inputs = processor(raw_image, return_tensors="pt")
-    
-    out = model.generate(**inputs, min_length=min_len, max_length=max_len)
-    return processor.decode(out[0], skip_special_tokens=True)
-
-def greet(img, min_len, max_len):
-    start = time.time()
-    result = caption(img, min_len, max_len)
-    end = time.time()
-    total_time = str(end - start)
-    result = result + '\n' + total_time + ' seconds'
-    return result
-
-iface = gr.Interface(fn=greet,
-                     title='Image Captioning with BLIP', 
-                     description="Generate captions for images using the BLIP model.",
-                     inputs=[gr.Image(type='filepath', label='Image'), 
-                             gr.Slider(label='Minimum Length', minimum=1, maximum=1000, value=30), 
-                             gr.Slider(label='Maximum Length', minimum=1, maximum=1000, value=100)], 
-                     outputs=gr.Textbox(label='Caption'))
-
-iface.launch()
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+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.")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+vision_language_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
+vision_language_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
+
+def describe_image(uploaded_image):
+    """
+    Generates a detailed description of the input image.
+
+    Args:
+        uploaded_image (PIL.Image.Image or numpy.ndarray): The image to describe.
+
+    Returns:
+        str: A detailed textual description of the image.
+    """
+    if not isinstance(uploaded_image, Image.Image):
+        uploaded_image = Image.fromarray(uploaded_image)
+
+    inputs = vision_language_processor(text="<MORE_DETAILED_CAPTION>", images=uploaded_image, return_tensors="pt").to(device)
+    with torch.no_grad():
+        generated_ids = vision_language_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 = vision_language_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    processed_description = vision_language_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
+
+image_description_interface = gr.Interface(
+    fn=describe_image,
+    inputs=gr.Image(label="Upload Image"),
+    outputs=gr.Textbox(label="Generated Description", lines=3, show_copy_button=True),
+    title="Detailed Image Description Generator",
+    description="Upload an image and let the model generate a detailed textual description.",
+    live=False,
+    examples=[["./logo.png"]] # You can add example image paths here
+)
+
+image_description_interface.launch(debug=True)