app.py

# app.py
import gradio as gr
import torch
import torchvision.transforms as transforms
from torchvision import models
import torch.nn as nn
from PIL import Image
import json
import os

# Korrekten Pfad zur class_names.json Datei verwenden
with open('class_names.json', 'r') as f:
    class_names = json.load(f)
    print(f"Klassen geladen: {len(class_names)} Klassen gefunden")

# Define the model
def load_model():
    model = models.resnet50(pretrained=False)
    model.fc = nn.Linear(model.fc.in_features, len(class_names))
    
    # Korrekten Pfad zur Modelldatei verwenden
    model_path = 'reptile_classifier.pth'
    print(f"Lade Modell von: {model_path}")
    
    # Load the trained model weights
    checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
    model.load_state_dict(checkpoint['model_state_dict'])
    model.eval()
    return model

# Load the model
model = load_model()
print("Modell erfolgreich geladen")

# Define image transformation
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Prediction function
def predict_image(image):
    if image is None:
        return None
    
    # Preprocess the image
    image = transform(image).unsqueeze(0)
    
    # Make prediction
    with torch.no_grad():
        outputs = model(image)
        probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
        
    # Get top 3 predictions
    top3_prob, top3_indices = torch.topk(probabilities, 3)
    
    # Format results
    results = [(class_names[idx], float(prob)) for idx, prob in zip(top3_indices, top3_prob)]
    
    return {class_name: float(prob) for class_name, prob in results}

# Create Gradio interface
def main():
    title = "Reptilien- und Amphibien-Klassifikation"
    description = "Lade ein Bild eines Reptils oder Amphibiums hoch, um es zu klassifizieren. Dieses Modell kann verschiedene Arten basierend auf dem Reptiles and Amphibians Dataset von Kaggle identifizieren."
    
    # Define the interface
    interface = gr.Interface(
        fn=predict_image,
        inputs=gr.Image(type="pil"),
        outputs=gr.Label(num_top_classes=3),
        title=title,
        description=description
    )
    
    # Launch the app
    interface.launch()

if __name__ == "__main__":
    main()