Upload 3 files

Home.py

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+import streamlit as st
+import pickle
+from Prediction import  Prediction
+from transformers import TFAutoModel
+
+
+
+# resnet_model = pickle.load(open('models/ResNet01.pkl','rb'))
+cnn_model = pickle.load(open('models/CNNModel2.pkl','rb'))
+inc_model = pickle.load(open('models/Inception01.pkl','rb'))
+resnet_model = TFAutoModel.from_pretrained("yashpat85/ResNet01")
+
+
+def show_error_popup(message):
+    st.error(message, icon="🚨")
+
+st.set_page_config(layout="wide")
+
+st.title('Kidney Disease Classification using CNN')
+st.markdown('By 22DCS079 & 22DCS085')
+
+st.header('Add Ct Scan Image')
+
+uploaded_file = st.file_uploader("Choose a ct scan image", type=["jpg", "png", "jpeg"])
+
+st.header("Available Models")
+option = st.selectbox(
+    "Available Models",
+    ("ResNet", "CNN","InceptionNet"),
+)
+
+pm = Prediction()
+
+col1, col2= st.columns(2)
+
+if uploaded_file is not None:
+    with col1:
+        image_data = uploaded_file.read()
+        st.image(image_data, caption="Uploaded Image")
+    with col2:
+        if option=="CNN":
+            p = pm.predict_image(cnn_model, image_data)
+        elif option=="ResNet":
+            p = pm.predict_image(resnet_model, image_data)
+        elif option=="InceptionNet":
+            p = pm.predict_image(inc_model, image_data)
+        else:
+            p = "Other Models are still under training due to overfitting"
+
+        print(p)
+
+        if p=='Normal':
+            st.markdown("""
+            <style>
+            .big-font {
+                display: flex;
+                align-items:center;
+                justify-content: center;
+                font-size:50px !important;
+                color:green;
+                height: 50vh;
+            }
+            </style>
+            """, unsafe_allow_html=True)
+
+            st.markdown(f'<div class="big-font">{p}</div>', unsafe_allow_html=True)
+        else:
+            st.markdown("""
+            <style>
+            .big-font {
+                display: flex;
+                align-items:center;
+                justify-content: center;
+                font-size:50px !important;
+                color:red;
+                height: 50vh;
+            }
+            </style>
+            """, unsafe_allow_html=True)
+
+            st.markdown(f'<div class="big-font">{p}</div>', unsafe_allow_html=True)
+else:
+    show_error_popup("Please Upload Image...")

Prediction.py

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+import numpy as np
+import tensorflow as tf
+
+
+class Prediction:
+    def predict_image(self, model, img):
+        img = tf.image.decode_jpeg(img, channels=3)
+        resize = tf.image.resize(img, (224,224))
+        yhat = model.predict(np.expand_dims(resize/255, 0))
+        max_index = np.argmax(yhat)
+        print(yhat)
+        op_d = {0:'Cyst',1:'Normal',2:'Stone',3:'Tumor'}
+        return op_d[max_index]
+

requirements.txt

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+numpy==1.26.4
+pandas==2.2.2
+tensorflow==2.17.0
+matplotlib==3.9.2
+streamlit==1.37.1