Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+# Model ve işlemciyi yükle
+model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base", torch_dtype=torch.float16, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
+
+def process_image(image, task):
+    # Girdiyi hazırla
+    inputs = processor(text=task, images=image, return_tensors="pt")
+
+    # Çıktıyı oluştur
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        num_beams=3,
+        do_sample=False
+    )
+
+    # Sonucu işle
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
+
+    return parsed_answer
+
+# Gradio arayüzü
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Radio(["<IC>", "<OD>", "<VQA>", "<IS>"], label="Task")
+    ],
+    outputs="text",
+    title="Florence-2 Image Processing",
+    description="Upload an image and select a task to process with Florence-2."
+)
+
+iface.launch()