Add application file

app.py

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+# -*- coding: utf-8 -*-
+"""демо.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1NK3gtM_1xpqJt79c_lDgu45FY4aMd3kr
+"""
+
+from huggingface_hub import hf_hub_download
+
+# Загрузка файла конфигурации и модели
+config_path = hf_hub_download(repo_id="alexakup05/eye_disease_classifier", filename="config.json")
+model_path = hf_hub_download(repo_id="alexakup05/eye_disease_classifier", filename="model1.pth")
+
+print(f"Модель и конфигурация загружены: {config_path}, {model_path}")
+
+import json
+
+# Загружаем конфигурацию
+with open(config_path, 'r') as f:
+    config = json.load(f)
+
+print(config)  # Проверим содержимое конфигурации
+
+import torch
+import torch.nn as nn
+from torchvision import models
+
+class EyeDiseaseEfficientNet(nn.Module):
+    def __init__(self, config):
+        super(EyeDiseaseEfficientNet, self).__init__()
+        self.efficientnet = models.efficientnet_b4(pretrained=False)
+        self.efficientnet.classifier = nn.Identity()
+        self.fc_age_sex = nn.Sequential(
+            nn.Linear(2, 64),
+            nn.ReLU(),
+            nn.Dropout(0.5)
+        )
+        self.fc_combined = nn.Sequential(
+            nn.Linear(1792 + 64, 512),
+            nn.ReLU(),
+            nn.Dropout(0.6),
+            nn.Linear(512, 8)
+        )
+
+    def forward(self, x_img, x_age_sex):
+        x_img = self.efficientnet(x_img)
+        x_age_sex = self.fc_age_sex(x_age_sex)
+        x = torch.cat((x_img, x_age_sex), dim=1)
+        x = self.fc_combined(x)
+        return x
+
+model = EyeDiseaseEfficientNet(config)
+model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+
+device = torch.device("cpu")
+model = model.to(device)
+model.eval()
+
+input_image = torch.randn(1, 3, 224, 224).to(device)
+input_age_sex = torch.tensor([[45, 1]], dtype=torch.float32).to(device)
+
+with torch.no_grad():
+    output = model(input_image, input_age_sex)
+    print(output)
+
+import torch.nn.functional as F
+
+logits = torch.tensor([[-2.6384, -1.8599,  0.0206,  2.0523,  0.2476,  1.9363,  1.5297, -1.0108]], device='cpu')
+probabilities = F.softmax(logits, dim=1)
+predicted_class = torch.argmax(probabilities, dim=1)
+print(f"Предсказанный класс: {predicted_class.item()}")
+
+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+
+def detect_eye(img):
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    eyes = eye_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+    if len(eyes) > 0:
+        (x, y, w, h) = eyes[0]
+        img = img[y:y+h, x:x+w]
+    return img
+
+def preprocess_image(img):
+    img = cv2.medianBlur(img, 3)
+    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    l = clahe.apply(l)
+    lab = cv2.merge((l, a, b))
+    img = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
+    return img
+
+def resize_with_padding(img, target_size=(224, 224)):
+    h, w = img.shape[:2]
+    scale = min(target_size[0] / h, target_size[1] / w)
+    new_w, new_h = int(w * scale), int(h * scale)
+    resized_img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
+    pad_w = (target_size[1] - new_w) // 2
+    pad_h = (target_size[0] - new_h) // 2
+    padded_img = cv2.copyMakeBorder(
+        resized_img, pad_h, target_size[0] - new_h - pad_h, pad_w, target_size[1] - new_w - pad_w,
+        cv2.BORDER_CONSTANT, value=[0, 0, 0]
+    )
+    return padded_img
+
+def predict(age, sex, img):
+    img = detect_eye(img)
+    img = preprocess_image(img)
+    img = resize_with_padding(img)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = Image.fromarray(img)
+    img_tensor = torch.tensor(np.array(img)).permute(2, 0, 1).unsqueeze(0).float()
+    age_sex_tensor = torch.tensor([[age, 0 if sex == "Male" else 1]]).float()
+    with torch.no_grad():
+        outputs = model(img_tensor, age_sex_tensor)
+    probabilities = torch.softmax(outputs, dim=1).cpu().numpy()[0]
+    disease_labels = [
+        "Normal",
+        "Diabetic Retinopathy",
+        "Glaucoma",
+        "Cataract",
+        "Age-related Macular Degeneration",
+        "Hypertension",
+        "Pathological Myopia",
+        "Other Diseases/Abnormalities"
+    ]
+
+    result = {disease_labels[i]: f"{probabilities[i]*100:.2f}%" for i in range(len(disease_labels))}
+    return result, img
+
+examples = [
+    [30, "Male", "myopia.png"]
+]
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Slider(minimum=0, maximum=100, step=1, label="Age"),
+        gr.Radio(["Male", "Female"], label="Gener"),
+        gr.Image(type="numpy", label="Upload Eye Image/ your Selfies / photo")
+    ],
+    outputs=[gr.JSON(label="Predictions"), gr.Image(label="Processed Image")],
+    examples=examples
+)
+
+iface.launch()