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23aebfe ae8b4e7 132e9a9 ae8b4e7 132e9a9 ae8b4e7 132e9a9 23aebfe 132e9a9 23aebfe 132e9a9 23aebfe | 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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | # -*- coding: utf-8 -*-
import gradio as gr
import torch
import torch.nn as nn
import pickle
from PIL import Image
import torchvision.transforms as transforms
from torchvision.models import resnet50
import os
def load_model_and_labels():
"""Load model and label mappings"""
# Load label mappings
try:
with open('label_maps.pkl', 'rb') as f:
label_mappings = pickle.load(f)
except:
# Fallback if file not found
label_mappings = {'index_label': {0: 'dry', 1: 'oily'}}
# Initialize and load model
model = resnet50(weights=None)
model.fc = nn.Linear(model.fc.in_features, 2)
# Load trained weights
device = torch.device('cpu')
try:
state_dict = torch.load('best_skin_model.pth', map_location=device)
model.load_state_dict(state_dict)
except:
print("Warning: Could not load model weights, using random initialization")
model.eval()
return model, label_mappings
def predict_skin_type(image):
"""Predict skin type from image"""
if image is None:
return "Please upload an image"
try:
# Preprocess image
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
if image.mode != 'RGB':
image = image.convert('RGB')
input_tensor = transform(image).unsqueeze(0)
# Make prediction
with torch.no_grad():
outputs = model(input_tensor)
probabilities = torch.softmax(outputs, dim=1)
predicted_class = torch.argmax(probabilities, dim=1).item()
confidence = probabilities[0][predicted_class].item()
# Get label
predicted_label = label_mappings['index_label'][predicted_class]
# Format result
dry_prob = float(probabilities[0][0])
oily_prob = float(probabilities[0][1])
result = f"""Skin Type Prediction: {predicted_label.upper()}
Confidence: {confidence:.1%}
Detailed Probabilities:
- Dry Skin: {dry_prob:.1%}
- Oily Skin: {oily_prob:.1%}
Note: This is an AI prediction for educational purposes only."""
return result
except Exception as e:
return f"Error processing image: {str(e)}"
# Load model at startup
print("Loading model...")
model, label_mappings = load_model_and_labels()
print("Model loaded successfully!")
# Create interface
iface = gr.Interface(
fn=predict_skin_type,
inputs=gr.Image(type="pil", label="Upload facial skin image"),
outputs=gr.Textbox(label="Prediction Results", lines=8),
title="AI Skin Type Classifier",
description="Upload a clear facial image to classify skin type as dry or oily."
)
# Launch
if __name__ == "__main__":
iface.launch(share=False)
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