Delete app.py

app.py

@@ -1,158 +0,0 @@
-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)}")