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app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import numpy as np
+from transformers import AutoModel, AutoConfig
+from model import Pix2TextModel, Pix2TextConfig
+import os
+
+# Model yükleme fonksiyonu
+def load_model():
+    try:
+        # Hugging Face'den model yükle
+        config = AutoConfig.from_pretrained("./", trust_remote_code=True)
+        model = AutoModel.from_pretrained("./", trust_remote_code=True)
+        return model
+    except Exception as e:
+        print(f"Model yükleme hatası: {e}")
+        # Yerel model oluştur
+        config = Pix2TextConfig()
+        model = Pix2TextModel(config)
+        return model
+
+model = load_model()
+
+def predict_text(image):
+    """Görüntüden metin çıkarma fonksiyonu"""
+    try:
+        if image is None:
+            return "Lütfen bir görüntü yükleyin."
+        
+        # PIL Image'a çevir
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+        
+        # Model ile tahmin yap
+        result = model.predict(image)
+        
+        return f"Çıkarılan Metin: {result}"
+    
+    except Exception as e:
+        return f"Hata oluştu: {str(e)}"
+
+def create_demo():
+    """Gradio demo arayüzü oluştur"""
+    
+    with gr.Blocks(title="Pix2Text - Görüntüden Metin Çıkarma") as demo:
+        gr.Markdown("# 🔤 Pix2Text Model Test Arayüzü")
+        gr.Markdown("Görüntü yükleyerek metin çıkarma işlemini test edebilirsiniz.")
+        
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(
+                    label="Görüntü Yükleyin",
+                    type="pil",
+                    height=400
+                )
+                
+                predict_btn = gr.Button("Metni Çıkar", variant="primary")
+                
+            with gr.Column():
+                output_text = gr.Textbox(
+                    label="Çıkarılan Metin",
+                    lines=10,
+                    placeholder="Sonuç burada görünecek..."
+                )
+        
+        # Event handlers
+        predict_btn.click(
+            fn=predict_text,
+            inputs=[image_input],
+            outputs=[output_text]
+        )
+        
+        # Örnek görüntüler
+        gr.Examples(
+            examples=[
+                ["example1.jpg"],
+                ["example2.png"]
+            ],
+            inputs=[image_input],
+            outputs=[output_text],
+            fn=predict_text,
+            cache_examples=False
+        )
+        
+        gr.Markdown("### Model Bilgileri")
+        gr.Markdown("""
+        - **Model Tipi**: Pix2Text (Görüntüden Metin Çıkarma)
+        - **Framework**: PyTorch + Transformers
+        - **Desteklenen Formatlar**: JPG, PNG, JPEG
+        - **Maksimum Görüntü Boyutu**: 2048x2048 piksel
+        """)
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_demo()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )