Delete app.py

app.py

@@ -1,190 +0,0 @@
-import matplotlib.pyplot as plt
-import seaborn as sns
-plt.rcParams["figure.figsize"] = (15, 10)
-plt.rcParams["figure.dpi"] = 125
-plt.rcParams["font.size"] = 14
-plt.rcParams['font.family'] = ['sans-serif']
-plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
-plt.style.use('ggplot')
-sns.set_style("whitegrid", {'axes.grid': False})
-plt.rcParams['image.cmap'] = 'gray' # grayscale looks better
-from pathlib import Path
-import numpy as np
-import pandas as pd
-import os
-from skimage.io import imread as imread
-from skimage.util import montage
-from PIL import Image
-montage_rgb = lambda x: np.stack([montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
-from skimage.color import label2rgb
-
-image_dir = Path('D:/vscodeim/food classification/feature_food/')
-
-
-mapping_file = Path('D:/vscodeim/food classification/feature_food/clean_list.json')
-alleg_df = pd.read_json(mapping_file)
-
-alleg_df['image_path'] = alleg_df['image_path'].map(lambda x: image_dir / 'subset' / x) 
-print(alleg_df['image_path'].map(lambda x: x.exists()).value_counts())
-allergens = alleg_df.columns[3:].tolist()
-alleg_df.sample(2)
-
-import os
-from pathlib import Path
-
-
-color_file = Path('D:/vscodeim/food classification/feature_food/color_features.json')
-color_feat_df = pd.read_json(color_file)
-color_feat_df['image_path'] = color_feat_df['image_path'].map(lambda x: image_dir / 'subset' / x) 
-
-color_feat_dict = {c_row['image_path']: c_row['color_features'] for _, c_row in color_feat_df.iterrows()}
-# add a new color feature column
-alleg_df['color_features'] = alleg_df['image_path'].map(color_feat_dict.get)
-alleg_df.sample(2)
-
-
-co_all = np.corrcoef(np.stack(alleg_df[allergens].applymap(lambda x: 1 if x>0 else 0).values, 0).T)
-fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
-sns.heatmap(co_all, annot=True, fmt='2.1%', ax=ax1, cmap='RdBu', vmin=-1, vmax=1)
-ax1.set_xticklabels(allergens, rotation=90)
-ax1.set_yticklabels(allergens);
-
-
-# package the allergens together
-alleg_df['allergy_vec'] = alleg_df[allergens].applymap(lambda x: 1 if x>0 else 0).values.tolist()
-
-from sklearn.model_selection import train_test_split
-train_df, valid_df = train_test_split(alleg_df.drop(columns='ingredients_list'), 
-                                      test_size=0.1, 
-                                      random_state=2019, 
-                                      stratify=alleg_df['allergy_vec'].map(lambda x: x[0:3]))
-
-train_df.reset_index(inplace=True)
-valid_df.reset_index(inplace=True)
-
-
-print(train_df.shape[0], 'training images')
-print(valid_df.shape[0], 'validation images')
-
-train_x_vec = np.stack(train_df['color_features'].values, 0)
-train_y_vec = np.stack(train_df['allergy_vec'], 0)
-
-
-
-
-
-from sklearn.pipeline import make_pipeline
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.preprocessing import RobustScaler  # 导入 RobustScaler
-
-rf_pipe = make_pipeline(RobustScaler(), RandomForestRegressor(n_estimators=15))
-rf_pipe.fit(train_x_vec, train_y_vec)
-
-
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score
-import matplotlib.pyplot as plt
-from imageio import imread
-
-@st.cache
-def show_model_results(in_model, picture_number=None):
-    # Your data loading code here
-    # Ensure 'valid_df', 'valid_x_vec', 'valid_y_vec', 'train_df', 'train_x_vec', 'train_y_vec', 'allergens' are defined
-
-    
-    
-    valid_x_vec = np.stack(valid_df['color_features'].values, 0)
-    valid_y_vec = np.stack(valid_df['allergy_vec'], 0)
-
-    x_vec = valid_x_vec
-    y_vec = valid_y_vec
-
-    
-    valid_pred = in_model.predict(x_vec)
-    valid_num = picture_number
-
-    fig, m_axs = plt.subplots(2, 2, figsize=(10, 10))
-    all_rows = []
-    ax1 = m_axs[0, 0]
-    for i, c_allergen in enumerate(allergens):
-        tpr, fpr, _ = roc_curve(y_vec[:, i], valid_pred[:, i])
-        auc = roc_auc_score(y_vec[:, i], valid_pred[:, i])
-        acc = accuracy_score(y_vec[:, i], valid_pred[:, i] > 0.5)
-        ax1.plot(tpr, fpr, '.-', label='{}: AUC {:0.2f}, Accuracy: {:2.0%}'.format(c_allergen, auc, acc))
-        all_rows += [{'allegen': c_allergen, 
-                      'prediction': valid_pred[j, i], 
-                      'class': 'Positive' if y_vec[j, i] > 0.5 else 'Negative'} 
-                     for j in range(valid_pred.shape[0])]
-    
-    d_ax = m_axs[0, 1]
-    t_yp = np.mean(valid_pred, 0)
-    t_y = np.mean(y_vec, 0)
-    d_ax.barh(np.arange(len(allergens)) + 0.1, t_yp, alpha=0.5, label='Predicted')
-    d_ax.barh(np.arange(len(allergens)) - 0.1, t_y + 0.001, alpha=0.5, label='Ground Truth')
-    d_ax.set_xlim(0, 1)
-    d_ax.set_yticks(range(len(allergens)))
-    d_ax.set_yticklabels(allergens, rotation=0)
-    d_ax.set_title('Overall')
-    d_ax.legend()
-    
-    ax1.legend()
-    for (_, c_row), (c_ax, d_ax) in zip(
-    valid_df.iloc[valid_num:valid_num+1].iterrows(), 
-    m_axs[1:]):
-        c_ax.imshow(imread(c_row['image_path']))
-        c_ax.set_title(c_row['title'])
-        c_ax.axis('off')
-        t_yp = in_model.predict(np.expand_dims(c_row['color_features'], 0))
-        t_y = np.array(c_row['allergy_vec'])
-        d_ax.barh(np.arange(len(allergens)) + 0.1, t_yp[0], alpha=0.5, label='Predicted')
-        d_ax.barh(np.arange(len(allergens)) - 0.1, t_y + 0.001, alpha=0.5, label='Ground Truth')
-        d_ax.set_yticks(range(len(allergens)))
-        d_ax.set_yticklabels(allergens, rotation=0)
-        d_ax.set_xlim(0, 1)
-        d_ax.legend()
-
-        # 将当前图像添加到 Streamlit 页面
-    st.pyplot(fig)
-    return st.write("Completed")
-
-# Assuming you have already defined 'rf_pipe' and 'valid_df' with image paths
-image_paths = valid_df['image_path'].tolist()
-
-
-# Streamlit app
-# Streamlit app
-def main():
-    st.title('Model Results')
-    st.write(f'<span style="font-size:20px;">This is a prototype, so we use the images from the test set as examples.</span>', unsafe_allow_html=True)
-
-    image_paths = valid_df['image_path'].tolist()
-
-    num_rows = 2
-    num_cols = 5
-    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 6))
-
-    for i in range(num_rows):
-        for j in range(num_cols):
-            index = i * num_cols + j + 21  # Starting from index 21
-            image = imread(image_paths[index])
-            axes[i, j].imshow(image)
-            axes[i, j].axis('off')
-            axes[i, j].set_title(f'Image {index-20}')
-
-    st.pyplot(fig)
-
-    num_images = 10 
-
-    # User interaction to select image
-    st.write(f'<span style="font-size:20px;">Enter the image number you want to analyze.</span>', unsafe_allow_html=True)
-
-    choice = st.number_input(f"Range (1-{num_images}): ", min_value=1, max_value=num_images)
-    
-    # Show model results
-    if st.button('Show Results'):
-        show_model_results(rf_pipe, choice+20)
-
-if __name__ == "__main__":
-    main()