Update app.py

app.py

@@ -1,137 +1,137 @@
-# Import required libraries
-import streamlit as st
-import numpy as np
-import pandas as pd
-from keras.models import load_model
-from keras.preprocessing import image
-import os
-import matplotlib.pyplot as plt
-import random
-
-# Set page configuration FIRST
-st.set_page_config(page_title="Blood Group Detection", layout="wide")
-
-# Title of the app
-st.title("🩸 Blood Group Detection using LeNet Model")
-
-# Sidebar navigation
-st.sidebar.header("Navigation")
-selected_option = st.sidebar.selectbox(
-    "Select an option:",
-    ["Home", "EDA", "Predict Blood Group"]
-)
-
-# Load the trained model
-@st.cache_resource
-def load_trained_model():
-    model = load_model('bloodgroup_mobilenet_finetuned.h5')  # Ensure correct path
-    return model
-
-model = load_trained_model()
-
-# Define class labels
-class_names = ['A+', 'A-', 'B+', 'B-', 'AB+', 'AB-', 'O+', 'O-']
-
-# Dataset directory (you must adjust this if needed)
-DATASET_DIR = "dataset"  # Example path
-
-# Home page
-if selected_option == "Home":
-    st.subheader("About the Project")
-    st.write("""
-        Welcome to the Blood Group Detection App! 
-        
-        This application uses a Deep Learning model (LeNet architecture) to detect blood groups from blood sample images.
-        
-        ### 🛠 Technologies Used:
-        - Streamlit for Web UI
-        - TensorFlow/Keras for Deep Learning
-        - Image Processing with Computer Vision
-
-        **Upload a blood sample image and predict the blood group instantly!**
-    """)
-    try:
-        st.image("blood_home.jpg", caption="Blood Sample Analysis", use_column_width=True)
-    except:
-        st.warning("Home image not found. (Optional)")
-
-# EDA page
-elif selected_option == "EDA":
-    st.subheader("Exploratory Data Analysis (EDA)")
-
-    # Check if dataset exists
-    if os.path.exists(DATASET_DIR):
-        st.write("### 📊 Number of Images per Blood Group:")
-
-        counts = {}
-        for class_name in class_names:
-            class_path = os.path.join(DATASET_DIR, class_name)
-            if os.path.exists(class_path):
-                counts[class_name] = len(os.listdir(class_path))
-            else:
-                counts[class_name] = 0
-
-        df_counts = pd.DataFrame(list(counts.items()), columns=['Blood Group', 'Number of Images'])
-        st.dataframe(df_counts)
-
-        # Bar Chart
-        st.bar_chart(df_counts.set_index('Blood Group'))
-
-        st.write("### 🖼️ Sample Images from Each Class:")
-
-        cols = st.columns(4)  # create 4 columns
-
-        for idx, class_name in enumerate(class_names):
-            class_path = os.path.join(DATASET_DIR, class_name)
-            if os.path.exists(class_path) and len(os.listdir(class_path)) > 0:
-                img_file = random.choice(os.listdir(class_path))
-                img_path = os.path.join(class_path, img_file)
-                img = image.load_img(img_path, target_size=(64, 64))  # resized
-                with cols[idx % 4]:  # arrange in 4 columns
-                    st.image(img, caption=class_name, width=150)
-
-        st.write("### 🧩 Image Properties:")
-        sample_class = class_names[0]
-        sample_path = os.path.join(DATASET_DIR, sample_class, os.listdir(os.path.join(DATASET_DIR, sample_class))[0])
-        sample_img = image.load_img(sample_path)
-        st.write(f"- **Image shape:** {np.array(sample_img).shape}")
-        st.write(f"- **Color channels:** {np.array(sample_img).shape[-1]} (RGB)")
-
-    else:
-        st.warning("Dataset not found! Please make sure the 'dataset/train' folder exists.")
-
-# Prediction page
-elif selected_option == "Predict Blood Group":
-    st.subheader("Upload an Image to Predict Blood Group")
-
-    uploaded_file = st.file_uploader("Choose a blood sample image...", type=["jpg", "jpeg", "png", "bmp"])
-
-    if uploaded_file is not None:
-        # Display uploaded image
-        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-
-        # Ensure temp directory exists
-        if not os.path.exists('temp'):
-            os.makedirs('temp')
-
-        # Save uploaded file temporarily
-        temp_file_path = os.path.join("temp", uploaded_file.name)
-        with open(temp_file_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-
-        # Preprocess the image
-        img = image.load_img(temp_file_path, target_size=(224, 224))  # Adjust size if needed
-        img_array = image.img_to_array(img)
-        img_array = np.expand_dims(img_array, axis=0)
-        img_array = img_array / 255.0  # Normalize pixel values
-
-        # Predict the blood group
-        with st.spinner('Predicting...'):
-            prediction = model.predict(img_array)
-            predicted_class = class_names[np.argmax(prediction)]
-
-        # Show result
-        st.success(f"🧬 Predicted Blood Group: **{predicted_class}**")
-
-        # Remove temporary file
-        os.remove(temp_file_path)
+# Import required libraries
+import streamlit as st
+import numpy as np
+import pandas as pd
+from keras.models import load_model
+from keras.preprocessing import image
+import os
+import matplotlib.pyplot as plt
+import random
+
+# Set page configuration FIRST
+st.set_page_config(page_title="Blood Group Detection", layout="wide")
+
+# Title of the app
+st.title("🩸 Blood Group Detection using LeNet Model")
+
+# Sidebar navigation
+st.sidebar.header("Navigation")
+selected_option = st.sidebar.selectbox(
+    "Select an option:",
+    ["Home", "EDA", "Predict Blood Group"]
+)
+
+# Load the trained model
+@st.cache_resource
+def load_trained_model():
+    model = load_model('best_model.h5')  # Ensure correct path
+    return model
+
+model = load_trained_model()
+
+# Define class labels
+class_names = ['A+', 'A-', 'B+', 'B-', 'AB+', 'AB-', 'O+', 'O-']
+
+# Dataset directory (you must adjust this if needed)
+DATASET_DIR = "dataset"  # Example path
+
+# Home page
+if selected_option == "Home":
+    st.subheader("About the Project")
+    st.write("""
+        Welcome to the Blood Group Detection App! 
+        
+        This application uses a Deep Learning model (LeNet architecture) to detect blood groups from blood sample images.
+        
+        ### 🛠 Technologies Used:
+        - Streamlit for Web UI
+        - TensorFlow/Keras for Deep Learning
+        - Image Processing with Computer Vision
+
+        **Upload a blood sample image and predict the blood group instantly!**
+    """)
+    try:
+        st.image("blood_home.jpg", caption="Blood Sample Analysis", use_column_width=True)
+    except:
+        st.warning("Home image not found. (Optional)")
+
+# EDA page
+elif selected_option == "EDA":
+    st.subheader("Exploratory Data Analysis (EDA)")
+
+    # Check if dataset exists
+    if os.path.exists(DATASET_DIR):
+        st.write("### 📊 Number of Images per Blood Group:")
+
+        counts = {}
+        for class_name in class_names:
+            class_path = os.path.join(DATASET_DIR, class_name)
+            if os.path.exists(class_path):
+                counts[class_name] = len(os.listdir(class_path))
+            else:
+                counts[class_name] = 0
+
+        df_counts = pd.DataFrame(list(counts.items()), columns=['Blood Group', 'Number of Images'])
+        st.dataframe(df_counts)
+
+        # Bar Chart
+        st.bar_chart(df_counts.set_index('Blood Group'))
+
+        st.write("### 🖼️ Sample Images from Each Class:")
+
+        cols = st.columns(4)  # create 4 columns
+
+        for idx, class_name in enumerate(class_names):
+            class_path = os.path.join(DATASET_DIR, class_name)
+            if os.path.exists(class_path) and len(os.listdir(class_path)) > 0:
+                img_file = random.choice(os.listdir(class_path))
+                img_path = os.path.join(class_path, img_file)
+                img = image.load_img(img_path, target_size=(64, 64))  # resized
+                with cols[idx % 4]:  # arrange in 4 columns
+                    st.image(img, caption=class_name, width=150)
+
+        st.write("### 🧩 Image Properties:")
+        sample_class = class_names[0]
+        sample_path = os.path.join(DATASET_DIR, sample_class, os.listdir(os.path.join(DATASET_DIR, sample_class))[0])
+        sample_img = image.load_img(sample_path)
+        st.write(f"- **Image shape:** {np.array(sample_img).shape}")
+        st.write(f"- **Color channels:** {np.array(sample_img).shape[-1]} (RGB)")
+
+    else:
+        st.warning("Dataset not found! Please make sure the 'dataset/train' folder exists.")
+
+# Prediction page
+elif selected_option == "Predict Blood Group":
+    st.subheader("Upload an Image to Predict Blood Group")
+
+    uploaded_file = st.file_uploader("Choose a blood sample image...", type=["jpg", "jpeg", "png", "bmp"])
+
+    if uploaded_file is not None:
+        # Display uploaded image
+        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+        # Ensure temp directory exists
+        if not os.path.exists('temp'):
+            os.makedirs('temp')
+
+        # Save uploaded file temporarily
+        temp_file_path = os.path.join("temp", uploaded_file.name)
+        with open(temp_file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        # Preprocess the image
+        img = image.load_img(temp_file_path, target_size=(224, 224))  # Adjust size if needed
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0  # Normalize pixel values
+
+        # Predict the blood group
+        with st.spinner('Predicting...'):
+            prediction = model.predict(img_array)
+            predicted_class = class_names[np.argmax(prediction)]
+
+        # Show result
+        st.success(f"🧬 Predicted Blood Group: **{predicted_class}**")
+
+        # Remove temporary file
+        os.remove(temp_file_path)