Update app.py

app.py

@@ -1,56 +1,23 @@
-import numpy as np
-import cv2
-import glob
-import os
-import matplotlib.pyplot as plt
-import string
-from mlxtend.plotting import plot_decision_regions
-from mpl_toolkits.mplot3d import Axes3D
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.model_selection import train_test_split, cross_val_score
-from sklearn.utils.multiclass import unique_labels
-from sklearn import metrics
-from sklearn.svm import SVC
-dim = 100
 import torch
-from torchvision import transforms
-from PIL import Image
-
-# Define your model class
-class YourModelClass(torch.nn.Module):
-    # Define your model architecture here
-
-# Create an instance of your model
-model = YourModelClass()
 
-# Load the pre-trained weights
-model.load_state_dict(torch.load('model_weights.pth'))
-model.eval()
-def predict_leaf_health(image_path):
-    try:
-        # Open and preprocess the image
-        img = Image.open(image_path)
-        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]),
-        ])
-        img = transform(img)
-        img = img.unsqueeze(0)  # Add batch dimension
-
-        # Make prediction
-        with torch.no_grad():
-            output = model(img)
-            prediction = torch.argmax(output).item()
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+import requests
+from PIL import Image
+from torchvision import transforms
 
-        # Map the prediction to class labels (modify as needed)
-        class_labels = {0: 'Unhealthy', 1: 'Healthy'}
-        result = class_labels.get(prediction, 'Unknown')
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
 
-        return result
+def predict(inp):
+  inp = transforms.ToTensor()(inp).unsqueeze(0)
+  with torch.no_grad():
+    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+  return confidences
+import gradio as gr
 
-    except Exception as e:
-        return f"Error: {str(e)}"
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["lion.jpg", "cheetah.jpg"]).launch()