Update app.py

app.py

@@ -1,108 +1,108 @@
-import streamlit as st
-import os
-import cv2
-import numpy as np
-import pandas as pd
-import random
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.tree import DecisionTreeClassifier
-
-# ------------------------ PAGE CONFIG & STYLING ------------------------
-st.set_page_config(page_title="Potato Leaf Disease Classifier", layout="centered")
-
-# Custom background styling (you must place 'Plant.jpg' in the root folder)
-st.markdown("""
-    <style>
-    .stApp {
-        background-image: url('Plant.jpg');
-        background-size: cover;
-        background-attachment: fixed;
-        background-position: center;
-        backdrop-filter: blur(2px);
-    }
-    </style>
-""", unsafe_allow_html=True)
-
-# ------------------------ TITLE & DESCRIPTION ------------------------
-st.title("🥔 Potato Leaf Disease Classifier")
-st.markdown("""
-<div style='font-size: 17px; line-height: 1.6;'>
-This project aims to automatically classify the health condition of potato leaves using grayscale images from different disease categories.
-The model helps farmers and agricultural experts identify potential diseases early and take appropriate actions to ensure healthy crop production.
-</div>
-""", unsafe_allow_html=True)
-
-# ------------------------ CONFIG ------------------------
-BASE_PATH = r"C:/Users/Harshu/OneDrive/Documents/Potato/PotatoPlants"  # Ensure this folder exists in your app's directory
-folders = [f for f in os.listdir(BASE_PATH) if os.path.isdir(os.path.join(BASE_PATH, f))]
-
-# ------------------------ MODEL SELECTION ------------------------
-st.markdown("<h4 style='margin-top: 30px;'>🧠 <b>Select Classifier</b></h4>", unsafe_allow_html=True)
-
-model_choice = st.selectbox(
-    "Choose a machine learning model to classify potato diseases",
-    ["Logistic Regression", "KNN", "Decision Tree"],
-    index=0
-)
-
-# ------------------------ LOAD DATA ------------------------
-@st.cache_data
-def load_data():
-    images, labels = [], []
-    for folder in folders:
-        full_path = os.path.join(BASE_PATH, folder)
-        for img_name in os.listdir(full_path):
-            img_path = os.path.join(full_path, img_name)
-            img = cv2.imread(img_path, 0)  # Read in grayscale
-            if img is not None:
-                resized = cv2.resize(img, (100, 100))
-                images.append(resized.flatten())
-                labels.append(folder.split("___")[-1])
-    return pd.DataFrame(images), pd.Series(labels)
-
-X, y = load_data()
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=27)
-
-# ------------------------ MODEL TRAINING ------------------------
-st.markdown("### 🔧 Training the model...")
-
-with st.spinner("Training in progress..."):
-    if model_choice == "Logistic Regression":
-        model = LogisticRegression(max_iter=200)
-    elif model_choice == "KNN":
-        model = KNeighborsClassifier()
-    else:
-        model = DecisionTreeClassifier()
-
-    model.fit(X_train, y_train)
-    accuracy = model.score(X_test, y_test)
-
-st.success(f"✅ {model_choice} trained with accuracy: **{accuracy:.2f}**")
-
-# ------------------------ RANDOM IMAGE TESTING ------------------------
-st.markdown("### 🔍 Test on a Random Image")
-
-if st.button("Test Random Image"):
-    random_folder = random.choice(folders)
-    folder_path = os.path.join(BASE_PATH, random_folder)
-    random_image_file = random.choice(os.listdir(folder_path))
-    image_path = os.path.join(folder_path, random_image_file)
-
-    img = cv2.imread(image_path, 0)
-    resized = cv2.resize(img, (100, 100))
-    flat = resized.flatten().reshape(1, -1)
-    prediction = model.predict(flat)[0]
-    actual = random_folder.split("___")[-1]
-
-    # Convert grayscale to RGB for better browser compatibility
-    color_img = cv2.cvtColor(resized, cv2.COLOR_GRAY2RGB)
-
-    st.image(color_img, caption="📷 Randomly selected potato leaf", use_container_width=True)
-    st.markdown(f"<h3 style='color:green;'>✅ Actual: <b>{actual}</b></h3>", unsafe_allow_html=True)
-    st.markdown(f"<h3 style='color:blue;'>🤖 Predicted: <b>{prediction}</b></h3>", unsafe_allow_html=True)
-
-# ------------------------ FOOTER ------------------------
-st.markdown("---")
-st.markdown("<center>Made with ❤️ for smart agriculture 🌾</center>", unsafe_allow_html=True)
+import streamlit as st
+import os
+import cv2
+import numpy as np
+import pandas as pd
+import random
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+# ------------------------ PAGE CONFIG & STYLING ------------------------
+st.set_page_config(page_title="Potato Leaf Disease Classifier", layout="centered")
+
+# Custom background styling (you must place 'Plant.jpg' in the root folder)
+st.markdown("""
+    <style>
+    .stApp {
+        background-image: url('Plant.jpg');
+        background-size: cover;
+        background-attachment: fixed;
+        background-position: center;
+        backdrop-filter: blur(2px);
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# ------------------------ TITLE & DESCRIPTION ------------------------
+st.title("🥔 Potato Leaf Disease Classifier")
+st.markdown("""
+<div style='font-size: 17px; line-height: 1.6;'>
+This project aims to automatically classify the health condition of potato leaves using grayscale images from different disease categories.
+The model helps farmers and agricultural experts identify potential diseases early and take appropriate actions to ensure healthy crop production.
+</div>
+""", unsafe_allow_html=True)
+
+# ------------------------ CONFIG ------------------------
+BASE_PATH = r"PotatoPlants"  # Ensure this folder exists in your app's directory
+folders = [f for f in os.listdir(BASE_PATH) if os.path.isdir(os.path.join(BASE_PATH, f))]
+
+# ------------------------ MODEL SELECTION ------------------------
+st.markdown("<h4 style='margin-top: 30px;'>🧠 <b>Select Classifier</b></h4>", unsafe_allow_html=True)
+
+model_choice = st.selectbox(
+    "Choose a machine learning model to classify potato diseases",
+    ["Logistic Regression", "KNN", "Decision Tree"],
+    index=0
+)
+
+# ------------------------ LOAD DATA ------------------------
+@st.cache_data
+def load_data():
+    images, labels = [], []
+    for folder in folders:
+        full_path = os.path.join(BASE_PATH, folder)
+        for img_name in os.listdir(full_path):
+            img_path = os.path.join(full_path, img_name)
+            img = cv2.imread(img_path, 0)  # Read in grayscale
+            if img is not None:
+                resized = cv2.resize(img, (100, 100))
+                images.append(resized.flatten())
+                labels.append(folder.split("___")[-1])
+    return pd.DataFrame(images), pd.Series(labels)
+
+X, y = load_data()
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=27)
+
+# ------------------------ MODEL TRAINING ------------------------
+st.markdown("### 🔧 Training the model...")
+
+with st.spinner("Training in progress..."):
+    if model_choice == "Logistic Regression":
+        model = LogisticRegression(max_iter=200)
+    elif model_choice == "KNN":
+        model = KNeighborsClassifier()
+    else:
+        model = DecisionTreeClassifier()
+
+    model.fit(X_train, y_train)
+    accuracy = model.score(X_test, y_test)
+
+st.success(f"✅ {model_choice} trained with accuracy: **{accuracy:.2f}**")
+
+# ------------------------ RANDOM IMAGE TESTING ------------------------
+st.markdown("### 🔍 Test on a Random Image")
+
+if st.button("Test Random Image"):
+    random_folder = random.choice(folders)
+    folder_path = os.path.join(BASE_PATH, random_folder)
+    random_image_file = random.choice(os.listdir(folder_path))
+    image_path = os.path.join(folder_path, random_image_file)
+
+    img = cv2.imread(image_path, 0)
+    resized = cv2.resize(img, (100, 100))
+    flat = resized.flatten().reshape(1, -1)
+    prediction = model.predict(flat)[0]
+    actual = random_folder.split("___")[-1]
+
+    # Convert grayscale to RGB for better browser compatibility
+    color_img = cv2.cvtColor(resized, cv2.COLOR_GRAY2RGB)
+
+    st.image(color_img, caption="📷 Randomly selected potato leaf", use_container_width=True)
+    st.markdown(f"<h3 style='color:green;'>✅ Actual: <b>{actual}</b></h3>", unsafe_allow_html=True)
+    st.markdown(f"<h3 style='color:blue;'>🤖 Predicted: <b>{prediction}</b></h3>", unsafe_allow_html=True)
+
+# ------------------------ FOOTER ------------------------
+st.markdown("---")
+st.markdown("<center>Made with ❤️ for smart agriculture 🌾</center>", unsafe_allow_html=True)