Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import io
+import random
+import time
+from huggingface_hub import hf_hub_download
+
+# Load model from Hugging Face Model Hub
+model_path = hf_hub_download(repo_id="koulsahil/LandCoverClassification_EuroSat", filename="eurosat_rgb_model.h5")
+model = torch.load(model_path)
+model.eval()
+
+
+# Define the class labels (replace with your EuroSAT classes)
+class_labels = [
+    "AnnualCrop", "Forest", "HerbaceousVegetation", "Highway", "Industrial",
+    "Pasture", "PermanentCrop", "Residential", "River", "SeaLake"
+]
+
+# Function to preprocess the image for the model
+def preprocess_image(image):
+    # Convert image to RGB if it has an alpha channel (4 channels)
+    if image.mode == 'RGBA':
+        image = image.convert('RGB')
+    image = image.resize((64, 64))  # Resize to match EuroSAT input size
+    image = np.array(image) / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Function to make predictions
+def predict(image):
+    processed_image = preprocess_image(image)
+    predictions = model.predict(processed_image)
+    predicted_class = class_labels[np.argmax(predictions)]
+    confidence = np.max(predictions)
+    return predicted_class, confidence
+
+
+
+
+# Set the page title and favicon (emoji as the icon)
+st.set_page_config(
+    page_title="Land Cover Classification",  # Title of the app
+    page_icon="🌍",  # Use the world emoji as the icon
+)
+
+
+
+
+
+# Custom CSS for styling
+st.markdown("""
+<style>
+    .stButton button {
+        background-color: #4CAF50;
+        color: white;
+        border-radius: 5px;
+        padding: 10px 20px;
+        font-size: 16px;
+    }
+    .stButton button:hover {
+        background-color: #45a049;
+    }
+    .stProgress > div > div > div {
+        background-color: #4CAF50;
+    }
+    .stMarkdown h1 {
+        color: #4CAF50;
+    }
+    .stMarkdown h2 {
+        color: #2E86C1;
+    }
+    .upload-section {
+        height: 150px; /* Increase the height of the upload section */
+    }
+    .thumbnail {
+        cursor: pointer;
+        border: 2px solid transparent;
+        transition: border 0.3s ease;
+        margin: 5px;
+    }
+    .thumbnail:hover {
+        border: 2px solid #4CAF50;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Sidebar for project information
+import streamlit as st
+
+with st.sidebar:
+    st.title("About the Project 🌍")
+    st.write("""
+    Hi, I’m **Sahil!** I built this web app to classify satellite images using Neural Networks,
+    This app leverages a **custom VGG16 deep learning model** trained on the **EuroSAT dataset** to classify land cover types in satellite images. 
+    The EuroSAT dataset is a collection of **27,000 labeled satellite images** covering **10 distinct land cover classes**, such as forests, crops, industrial areas, and water bodies. 
+
+
+    ### How to Use:
+    1. **Select an Image**:
+       - Drag and drop one of the **sample image thumbnails** to the upload section to quickly test the model.
+       - Or use the dropdown menu to choose from a list of sample images, or click the select random button to choose a random image from the list.
+       - Alternatively, **upload your own satellite image** by dragging and dropping it into the upload section.
+    2. **Make a Prediction**:
+       - Once an image is selected or uploaded, the model will automatically analyze it and display the **predicted land cover type** along with a **confidence score**.
+    3. **Interpret the Results**:
+       - The app will show the **top predicted class** and its confidence level.
+       - You can also view the **top 3 predictions** to understand the model's certainty across multiple classes.
+
+
+    ### GitHub Repository:
+    Explore the code, dataset, and model training process on GitHub:  
+    [GitHub Repository](https://koulmesahil.github.io/) | [LinkedIn](https://www.linkedin.com/in/sahilkoul123/)
+    """)
+
+# Main layout
+st.title("Land Cover Classification from Satellite Images ")
+st.write("🖼️📸 Drag and drop one of the thumbnails below, select a random image from the dropdown, or upload your own image to classify its land cover type.🌍")
+
+# Define sample images
+sample_images = {
+    "Highway": "sample_images/Highway_1004.jpg",
+    "Annual Crop": "sample_images/AnnualCrop_102.jpg",
+    "Forest": "sample_images/Forest_1019.jpg",
+    "Herbaceous Vegetation": "sample_images/HerbaceousVegetation_1024.jpg",
+    "Industrial": "sample_images/Industrial_1015.jpg",
+    "Pasture": "sample_images/Pasture_1023.jpg",
+    "Sea Lake": "sample_images/SeaLake_1017.jpg",
+    "River": "sample_images/River_1014.jpg",
+    "Permenant Crop": "sample_images/PermanentCrop_1004.jpg",
+    "Residential": "sample_images/Residential_1019.jpg",
+    # Add more sample images here
+}
+
+# Thumbnail Section
+st.write("### Sample Image Thumbnails")
+cols = st.columns(len(sample_images))  # Create columns for thumbnails
+for idx, (label, image_path) in enumerate(sample_images.items()):
+    with cols[idx]:
+        # Display the thumbnail
+        image = Image.open(image_path)
+        image.thumbnail((100, 100))  # Resize the image to a smaller thumbnail
+
+        st.image(image, use_container_width=True, caption=None)
+
+
+# Dropdown menu for sample images
+#st.write("### Select an Image from Dropdown")
+selected_image_label = st.selectbox("Choose a category to view a random image:", ["Pick a category"] + list(sample_images.keys()))
+
+if selected_image_label != "Pick a category":
+    image_path = sample_images[selected_image_label]
+    with open(image_path, "rb") as file:
+        uploaded_file = file.read()
+    st.session_state.uploaded_file = uploaded_file
+    st.session_state.selected_image_label = selected_image_label
+
+
+# Add a "Select Random" button
+if st.button("Random Selection"):
+    random_label, random_image_path = random.choice(list(sample_images.items()))
+    with open(random_image_path, "rb") as file:
+        uploaded_file = file.read()
+    st.session_state.uploaded_file = uploaded_file
+    st.session_state.selected_image_label = random_label
+    st.success(f"Randomly selected image: **{random_label}**")
+
+
+# JavaScript & CSS to dynamically change the border color
+st.markdown(
+    """
+    <style>
+        /* Base styling for the upload box */
+        div.stFileUploader {
+            height: 250px !important;
+            width: 100% !important;
+            border: 3px dashed grey !important;
+            padding: 40px !important;
+            border-radius: 10px;
+            transition: all 0.3s ease-in-out;
+        }
+
+        /* Upload text styling */
+        div.stFileUploader > label {
+            font-size: 20px !important;
+            font-weight: bold !important;
+            color: grey !important;
+            text-align: center !important;
+        }
+
+        /* JavaScript to change colors dynamically */
+        <script>
+            function updateUploaderColor() {
+                var uploader = document.querySelector("div.stFileUploader");
+                if (uploader && uploader.querySelector("input").files.length > 0) {
+                    uploader.style.borderColor = "#4CAF50";
+                    uploader.style.color = "#4CAF50";
+                } else {
+                    uploader.style.borderColor = "grey";
+                    uploader.style.color = "grey";
+                }
+            }
+            
+            document.addEventListener("DOMContentLoaded", function() {
+                var fileInput = document.querySelector("div.stFileUploader input");
+                if (fileInput) {
+                    fileInput.addEventListener("change", updateUploaderColor);
+                }
+            });
+        </script>
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+st.write("### Upload Your Image")
+uploaded_file = st.file_uploader(
+    "Drag and drop an image here",
+    type=["jpg", "jpeg", "png"],
+    key="uploader",
+    accept_multiple_files=False,
+    help="Upload an image to classify its land cover type."
+)
+
+if uploaded_file:
+    st.markdown(
+        """
+        <style>
+            div.stFileUploader {
+                border-color: #4CAF50 !important;
+                color: #4CAF50 !important;
+            }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+# Display the uploaded/selected image and make predictions
+if uploaded_file is not None:
+    try:
+        file_type = uploaded_file.type
+        if file_type not in ["image/jpeg", "image/png"]:
+            st.error("Unsupported file type. Please upload a JPG or PNG image.")
+        else:
+            image = Image.open(uploaded_file)
+            st.session_state.uploaded_file = uploaded_file.getvalue()
+            st.session_state.selected_image_label = "Uploaded Image"
+    except Exception as e:
+        st.error(f"Error loading image: {e}")
+
+
+# Display the uploaded/selected image and make predictions
+if "uploaded_file" in st.session_state:
+    image = Image.open(io.BytesIO(st.session_state.uploaded_file))
+
+    # Display the image with a smaller size
+    st.image(image, caption=f"Selected Image: {st.session_state.selected_image_label}", width=300)
+    
+    # Show balloons effect after the prediction is done
+    with st.spinner("Analyzing the image..."):
+        time.sleep(1)  # Simulate processing delay
+        predicted_class, confidence = predict(image)
+        st.balloons()  # Display balloons when prediction is complete
+
+    # Display the prediction results in a more prominent section
+    st.markdown("## Prediction Results")
+    st.success(f"**Predicted Class:** {predicted_class}")
+    st.info(f"**Confidence:** {confidence * 100:.2f}%")
+    
+    # Visualize confidence as a progress bar with a label
+    st.markdown("**Confidence Level:**")
+    st.progress(float(confidence))
+    
+    # Show top 3 predictions
+    st.markdown("### Top 3 Predictions")
+    processed_image = preprocess_image(image)
+    predictions = model.predict(processed_image)
+    top_indices = np.argsort(predictions[0])[-3:][::-1]  # Get top 3 predictions
+    for i in top_indices:
+        st.write(f"- **{class_labels[i]}**: {predictions[0][i] * 100:.2f}%")
+
+
+
+# Footer
+st.markdown("---")
+st.markdown("""
+[GitHub Repository](https://koulmesahil.github.io/) | [LinkedIn](https://www.linkedin.com/in/sahilkoul123/)
+""")