Upload app.py

app.py

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+import gradio as gr
+import torch
+import os
+import sys
+from huggingface_hub import login
+import base64
+import io
+from PIL import Image
+import requests
+import tempfile
+
+# Force CPU usage if needed
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# More details about the environment
+print(f"Gradio version: {gr.__version__}")
+print(f"Python version: {sys.version}")
+
+# Hugging Face API token'ı - önce environment variable olarak ara, 
+# sonra Hugging Face Secrets sisteminde ara
+hf_token = os.environ.get("HUGGINGFACE_TOKEN")
+if hf_token:
+    print("Found HUGGINGFACE_TOKEN in environment variables")
+    # Token ile giriş yap
+    login(token=hf_token)
+    print("Logged in with Hugging Face token")
+else:
+    print("HUGGINGFACE_TOKEN not found in environment variables")
+    # Hugging Face Spaces bu değişkeni otomatik olarak yükleyecek
+    # eğer Spaces UI üzerinden secret olarak eklediyseniz
+
+def process_image(img_data):
+    """Process image data to ensure it's in a valid format"""
+    try:
+        # If it's already a PIL Image
+        if isinstance(img_data, Image.Image):
+            return img_data
+            
+        # If it's a URL
+        if isinstance(img_data, str) and (img_data.startswith('http://') or img_data.startswith('https://')):
+            response = requests.get(img_data)
+            return Image.open(io.BytesIO(response.content))
+            
+        # If it's base64 encoded
+        if isinstance(img_data, str) and img_data.startswith('data:image'):
+            img_data = img_data.split(',')[1]
+            img_bytes = base64.b64decode(img_data)
+            return Image.open(io.BytesIO(img_bytes))
+            
+        # If it's bytes
+        if isinstance(img_data, bytes):
+            return Image.open(io.BytesIO(img_data))
+            
+        # If it's a numpy array
+        if hasattr(img_data, 'shape') and len(img_data.shape) >= 2:
+            return Image.fromarray(img_data)
+            
+        # Default fallback
+        print(f"Unknown image format: {type(img_data)}")
+        return None
+    except Exception as e:
+        print(f"Error processing image: {str(e)}")
+        return None
+
+def save_image(img, filename=None):
+    """Save image to a temporary file and return the path"""
+    try:
+        if not filename:
+            temp_dir = tempfile.gettempdir()
+            filename = os.path.join(temp_dir, f"generated_image_{id(img)}.png")
+        
+        img = process_image(img)
+        if img:
+            # Ensure the image is in RGB mode (not RGBA which can cause problems)
+            if img.mode == 'RGBA':
+                img = img.convert('RGB')
+            img.save(filename, format="PNG")
+            return filename
+        return None
+    except Exception as e:
+        print(f"Error saving image: {str(e)}")
+        return None
+
+def generate_3d_render(prompt):
+    """Generate a 3D render from the prompt"""
+    try:
+        # Attempt to use external API through Gradio
+        try:
+            print(f"Sending request to model with prompt: {prompt}")
+            
+            # HF Spaces'te önceden tanımlanmış bir model arayüzümüz var,
+            # bu modeli doğrudan çağırıyoruz
+            import gradio.external as ext
+            result = ext.call_space(
+                name="goofyai/3d_render_style_xl",
+                fn_index=0,  # Ana model fonksiyonu genellikle 0 indeksindedir
+                inputs=[prompt],
+                api_key=hf_token
+            )
+            
+            # Sonuçları işle
+            if result and isinstance(result, list) and len(result) > 0:
+                print("Received response from model API")
+                # Görsel varsa işle
+                if hasattr(result[0], 'shape') or isinstance(result[0], (str, bytes, Image.Image)):
+                    img = process_image(result[0])
+                    if img:
+                        # Görüntüyü PNG formatında kaydet (kaydedilmiş dosya yolunu döndürür)
+                        saved_path = save_image(img)
+                        if saved_path:
+                            print(f"Image saved to {saved_path}")
+                            return saved_path
+                return result[0]  # İşlenemezse orijinal sonucu döndür
+            else:
+                print("Empty or invalid response from model API")
+                return None
+        except Exception as e:
+            print(f"Error calling external API: {str(e)}")
+            # Geri dönüş mekanizması - basit metin yanıtı
+            return f"Model API'sine erişilemiyor: {str(e)}"
+    except Exception as e:
+        print(f"Error in generate_3d_render: {str(e)}")
+        return f"Hata: {str(e)}"
+
+def load_model():
+    try:
+        print("Setting up 3D render model interface...")
+        
+        # Basit bir Gradio arayüzü oluştur
+        interface = gr.Interface(
+            fn=generate_3d_render,
+            inputs=gr.Textbox(label="Input", placeholder="Enter a prompt for 3D rendering"),
+            outputs=gr.Image(label="Output", type="filepath"),
+            title="3D Render Style XL",
+            description="Enter a prompt to generate a 3D render in game-icon style"
+        )
+        
+        return interface
+    except Exception as e:
+        print(f"Error setting up interface: {str(e)}")
+        return None
+
+# Create the interface
+try:
+    interface = load_model()
+    if interface:
+        print("Interface set up successfully, launching...")
+        interface.launch(
+            share=False,
+            server_name="0.0.0.0",
+            server_port=7860,
+            show_error=True
+        )
+    else:
+        print("Failed to set up the interface")
+except Exception as e:
+    print(f"Error launching interface: {str(e)}")