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1656c11 81a987d 1656c11 23ace35 1656c11 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # 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() |