Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, GlobalAveragePooling2D
+from tensorflow.keras.models import Model, load_model, Sequential
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.metrics import Precision, Recall
+from tensorflow.keras.callbacks import EarlyStopping
+from sklearn.utils.class_weight import compute_class_weight
+from sklearn.model_selection import train_test_split
+
+from tensorflow.keras.utils import to_categorical
+import matplotlib.pyplot as plt
+import warnings
+import warnings
+warnings.filterwarnings("ignore")
+
+# print ('modules loaded')
+
+
+import streamlit as st
+import pandas as pd 
+import numpy as np
+from PIL import Image
+import tensorflow.keras as keras
+
+st.title("Skin Cancer Classification App")
+
+models = {
+  "Le_Net": load_model('LeNet_5.h5'),
+  "Simple_CNN": load_model('Simple CNN.h5'),
+  "Alex_Net": load_model('AlexNet.h5'),
+  "Deeper_CNN": load_model('Deeper CNN.h5')
+}
+
+# Allow user to select model
+model_name = st.selectbox("Choose a model", list(models.keys()))
+model = models[model_name]
+
+# Upload CSV file 
+# file = st.file_uploader("Upload a CSV file", type=["csv"])
+file ='hmnist_28_28_RGB.csv'
+
+def image_resize(data):
+    Data = data.drop(columns=["label"])
+    Data = np.array(Data).reshape(-1, 28, 28, 3)
+    Data = Data / 255.0  # Normalizing the data
+    # Resize images to 32x32 pixels
+    Data_resized = resize(Data, [32, 32]).numpy()  # Ensure conversion to NumPy array
+    return Data_resized
+
+if file is not None:
+    df = pd.read_csv(file)  
+    # Get first row 
+    row = df.iloc[0]  
+    
+    # Load image 
+    image = np.array(Image.open(row[0])) 
+    # Reshape  
+    img_reshaped = image_resize(row)
+    
+    # Get prediction
+    pred = model.predict(img_reshaped)
+    label = np.argmax(pred)
+
+    label_map = {4: ('nv', ' melanocytic nevi'), 
+             6: ('mel', 'melanoma'),
+             2: ('bkl', 'benign keratosis-like lesions'),
+             1: ('bcc' , ' basal cell carcinoma'),
+             5: ('vasc', 'pyogenic granulomas and hemorrhage'),  
+             0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'),
+             3: ('df', 'dermatofibroma')}
+
+    if label in label_map:
+        label_name = label_map[label][0]
+        full_name = label_map[label][1]
+    
+    # Display image and result 
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header("Input Image")
+        st.image(image)
+    with col2:
+        st.header("Prediction")
+        st.metric("Digit", full_name)
+
+
+# import streamlit as st
+# import predict_model # our prediction model
+
+# # Label maps
+# label_map = {0: ('akiec', 'Actinic keratoses'), 
+#              1: ('bcc', 'basal cell carcinoma'),
+#              # Rest of label map
+#              }
+
+# # Get prediction        
+# img = st.file_uploader("Upload image")
+# if img:
+#     pred_id = predict_model.get_prediction(img) 
+    
+#     # Display prediction    
+#     if pred_id in label_map:
+#         label_name = label_map[pred_id][0]
+#         full_name = label_map[pred_id][1]
+        
+#         st.success(f"Predicted Label: {label_name} - {full_name}")
+#     else:
+#         st.warning("Unknown label predicted")        
+
+        
+
+# data_dir = 'hmnist_28_28_RGB.csv'
+# data = pd.read_csv(data_dir)
+# data.head()
+
+# Label = data["label"]
+# Data = data.drop(columns=["label"])
+
+# data["label"].value_counts()
+
+# classes = {4: ('nv', ' melanocytic nevi'),
+#            6: ('mel', 'melanoma'),
+#            2 :('bkl', 'benign keratosis-like lesions'), 
+#            1:('bcc' , ' basal cell carcinoma'),
+#            5: ('vasc', ' pyogenic granulomas and hemorrhage'),
+#            0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'),
+#            3: ('df', 'dermatofibroma')}
+
+# from tensorflow.image import resize
+
+# #preprocess data
+# Label = data["label"]
+
+
+
+# Label = to_categorical(Label, num_classes=7)  # Assuming 7 classes
+
+
+
+
+# # Later in Streamlit...
+
+
+
+