Add vision analyzer app with Florence-2

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# Load Florence-2 (runs on CPU, free tier compatible)
+model_id = "microsoft/Florence-2-large"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+print(f"Loading model on {device}...")
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=dtype,
+    trust_remote_code=True
+).to(device)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+print("Model loaded.")
+
+def analyze_image(image, prompt):
+    if image is None:
+        return "No image uploaded."
+    
+    if not prompt:
+        prompt = "<MORE_DETAILED_CAPTION>"
+    
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, dtype)
+    
+    with torch.no_grad():
+        generated_ids = model.generate(
+            input_ids=inputs["input_ids"],
+            pixel_values=inputs["pixel_values"],
+            max_new_tokens=512,
+            do_sample=False
+        )
+    
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
+    
+    # Return the first value from parsed dict
+    if isinstance(parsed, dict):
+        return list(parsed.values())[0]
+    return str(parsed)
+
+# Available tasks for Florence-2
+TASKS = [
+    "<CAPTION>",
+    "<DETAILED_CAPTION>",
+    "<MORE_DETAILED_CAPTION>",
+    "<OCR>",
+    "<OCR_WITH_REGION>",
+    "<OBJECT_DETECTION>",
+    "<REGION_TO_CATEGORY>",
+    "<REGION_TO_DESCRIPTION>",
+]
+
+with gr.Blocks(title="Vision Analyzer") as demo:
+    gr.Markdown("# Image Understanding")
+    gr.Markdown("Upload an image and select what you want to extract from it.")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Image")
+            task_dropdown = gr.Dropdown(choices=TASKS, value="<MORE_DETAILED_CAPTION>", label="Analysis Type")
+            text_prompt = gr.Textbox(label="Or enter custom prompt (overrides dropdown)", placeholder="Describe what you see...", lines=2)
+            analyze_btn = gr.Button("Analyze")
+        with gr.Column():
+            output = gr.Textbox(label="Result", lines=15, show_copy_button=True)
+    
+    analyze_btn.click(fn=analyze_image, inputs=[image_input, text_prompt], outputs=output)
+    
+    gr.Markdown("---")
+    gr.Markdown("Powered by Microsoft Florence-2-large on HuggingFace free tier.")
+
+demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+torch
+transformers
+accelerate
+Pillow
+gradio