Upload 4 files

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 app/models/SatCoverClassifier.keras filter=lfs diff=lfs merge=lfs -text
+models/SatCoverClassifier.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,67 @@
+import os
+import numpy as np
+import cv2
+import tensorflow as tf
+from flask import Flask, request, render_template, jsonify
+from tensorflow.keras.models import load_model
+
+app = Flask(__name__)
+
+# Load the trained model
+MODEL_PATH = os.path.join('models', 'SatCoverClassifier.keras')
+model = load_model(MODEL_PATH)
+
+class_mapping = {
+    0: 'AnnualCrop', 1: 'Forest', 2: 'HerbaceousVegetation',
+    3: 'Highway', 4: 'Industrial', 5: 'Pasture',
+    6: 'PermanentCrop', 7: 'Residential', 8: 'River',
+    9: 'SeaLake'
+}
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file part'})
+    
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'})
+    
+    # Read and preprocess the image
+    img_bytes = file.read()
+    nparr = np.frombuffer(img_bytes, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    
+    # Resize to match model input
+    resized_img = tf.image.resize(img_rgb, (64, 64))
+    
+    # Normalize and expand dims for batch
+    normalized_img = resized_img / 255.0
+    input_data = np.expand_dims(normalized_img, 0)
+    
+    # Make prediction
+    predictions = model.predict(input_data)
+    
+    # Get top 3 predictions
+    top3_indices = np.argsort(predictions[0])[-3:][::-1]
+    results = []
+    
+    for idx in top3_indices:
+        results.append({
+            'class': class_mapping[idx],
+            'confidence': float(predictions[0][idx] * 100)
+        })
+    
+    return jsonify({
+        'prediction': results[0]['class'],
+        'confidence': results[0]['confidence'],
+        'top3': results
+    })
+
+if __name__ == '__main__':
+    app.run(debug=True)

models/SatCoverClassifier.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8555ae35fa182f3630d202f68885c3389f639510736ec2445c16643711541e3
+size 26390483

requirements.txt

@@ -1,4 +1,144 @@
-Flask==2.0.1
-Werkzeug==2.0.1
+absl-py==2.2.2
+affine==2.4.0
+anyio==4.9.0
+appnope==0.1.4
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==3.0.0
+astunparse==1.6.3
+async-lru==2.0.5
+attrs==25.3.0
+babel==2.17.0
+beautifulsoup4==4.13.4
+bleach==6.2.0
+blinker==1.9.0
+certifi==2025.1.31
+cffi==1.17.1
+charset-normalizer==3.4.1
+click==8.1.8
+click-plugins==1.1.1
+cligj==0.7.2
+comm==0.2.2
+contourpy==1.3.2
+cycler==0.12.1
+debugpy==1.8.14
+decorator==5.2.1
+defusedxml==0.7.1
+exceptiongroup==1.2.2
+executing==2.2.0
+fastjsonschema==2.21.1
+Flask==3.1.0
+flatbuffers==25.2.10
+fonttools==4.57.0
+fqdn==1.5.1
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.71.0
+h11==0.16.0
+h5py==3.13.0
+httpcore==1.0.9
+httpx==0.28.1
+idna==3.10
+ipykernel==6.29.5
+ipython==8.35.0
+isoduration==20.11.0
+itsdangerous==2.2.0
+jedi==0.19.2
+Jinja2==3.1.6
+joblib==1.4.2
+json5==0.12.0
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2025.4.1
+jupyter-events==0.12.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+jupyter_server==2.15.0
+jupyter_server_terminals==0.5.3
+jupyterlab==4.4.1
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+keras==3.9.2
+kiwisolver==1.4.8
+libclang==18.1.1
+Markdown==3.8
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.1
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mistune==3.1.3
+ml-dtypes==0.3.2
+namex==0.0.9
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
+nest-asyncio==1.6.0
+notebook_shim==0.2.4
+numpy==1.26.4
 opencv-python==4.11.0.86
-numpy==1.26.4
+opt_einsum==3.4.0
+optree==0.15.0
+overrides==7.7.0
+packaging==25.0
+pandas==2.2.3
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.2.1
+platformdirs==4.3.7
+prometheus_client==0.21.1
+prompt_toolkit==3.0.51
+protobuf==4.25.7
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pycparser==2.22
+Pygments==2.19.1
+pyngrok==7.2.5
+pyparsing==3.2.3
+python-dateutil==2.9.0.post0
+python-json-logger==3.3.0
+python-version==0.0.2
+pytz==2025.2
+PyYAML==6.0.2
+pyzmq==26.4.0
+rasterio==1.4.3
+referencing==0.36.2
+requests==2.32.3
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rich==14.0.0
+rpds-py==0.24.0
+scikit-learn==1.6.1
+scipy==1.15.2
+Send2Trash==1.8.3
+six==1.17.0
+sniffio==1.3.1
+soupsieve==2.7
+stack-data==0.6.3
+tensorboard==2.16.2
+tensorboard-data-server==0.7.2
+tensorflow==2.16.2
+tensorflow-io-gcs-filesystem==0.37.1
+termcolor==3.0.1
+terminado==0.18.1
+threadpoolctl==3.6.0
+tinycss2==1.4.0
+tomli==2.2.1
+tornado==6.4.2
+tqdm==4.67.1
+traitlets==5.14.3
+types-python-dateutil==2.9.0.20241206
+typing_extensions==4.13.2
+tzdata==2025.2
+uri-template==1.3.0
+urllib3==2.4.0
+wcwidth==0.2.13
+webcolors==24.11.1
+webencodings==0.5.1
+websocket-client==1.8.0
+Werkzeug==3.1.3
+wrapt==1.17.2

templates/index.html

@@ -0,0 +1,131 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Land Cover Classification</title>
+    <script src="https://cdn.jsdelivr.net/npm/@tailwindcss/browser@4"></script>
+  </head>
+  <body class="bg-slate-900 text-slate-50">
+    <div class="flex flex-col items-center justify-center gap-6 border-b-2 border-teal-700 p-4 ">
+      <h1 class="text-3xl">Land Cover Classifier</h1>
+      <p>Upload a satellite image to classify the land cover type</p>
+    </div>
+    <div class="flex items-start justify-center min-h-screen p-4 gap-8">
+      <div class="h-[40vh] flex items-center p-4">
+        <form id="upload-form" enctype="multipart/form-data" class="flex flex-col items-center gap-4">
+          <div class="cursor-pointer">
+            <label for="image-upload" class="bg-teal-700 px-4 py-2 rounded cursor-pointer">
+              Select Image
+            </label>
+            <input
+              type="file"
+              id="image-upload"
+              name="file"
+              accept="image/*"
+              class="hidden"
+            />
+          </div>
+          <button type="submit" class="bg-teal-700 px-4 py-2 rounded cursor-pointer">Classify Image</button>
+        </form>
+      </div>
+
+      <div class="min-h-[50vh] flex flex-col items-center justify-center gap-4">
+        <div id="preview-image" class="hidden">
+          <h3>Uploaded Image</h3>
+          <img src="#" alt="Preview" />
+        </div>
+        <div
+          class="hidden flex-col items-center justify-center gap-4"
+          id="results-container"
+        >
+          <div>
+            <h3>Prediction Results</h3>
+            <div>
+              <span id="main-class">Class</span>:
+              <span id="confidence">0%</span> confidence
+            </div>
+  
+            <h4>Top 3 Predictions</h4>
+            <ul id="predictions-list"></ul>
+          </div>
+        </div>
+  
+        <div class="hidden" id="loading">
+          <div class="w-12 h-12 border-4 border-teal-200 border-t-teal-700 rounded-full animate-spin mb-3 mx-auto"></div>
+          <p>Processing image...</p>
+        </div>
+      </div>
+    </div>
+
+    <script>
+      document.addEventListener("DOMContentLoaded", function () {
+        const form = document.getElementById("upload-form");
+        const imageUpload = document.getElementById("image-upload");
+        const previewImage = document.getElementById("preview-image");
+        const resultsContainer = document.getElementById("results-container");
+        const mainClass = document.getElementById("main-class");
+        const confidence = document.getElementById("confidence");
+        const predictionsList = document.getElementById("predictions-list");
+        const loadingIndicator = document.getElementById("loading");
+
+        // Preview the selected image
+        imageUpload.addEventListener("change", function () {
+          const file = this.files[0];
+          if (file) {
+            const reader = new FileReader();
+            reader.onload = function (e) {
+              previewImage.getElementsByTagName('img')[0].src = e.target.result;
+            };
+            reader.readAsDataURL(file);
+            previewImage.classList.remove("hidden");
+          }
+        });
+
+        form.addEventListener("submit", function (e) {
+          e.preventDefault();
+
+          const formData = new FormData(this);
+
+          loadingIndicator.classList.remove("hidden");
+          loadingIndicator.classList.add("flex");
+          resultsContainer.classList.remove("hideen");
+
+          fetch("/predict", {
+            method: "POST",
+            body: formData,
+          })
+            .then((response) => response.json())
+            .then((data) => {
+              loadingIndicator.style.display = "none";
+
+              if (data.error) {
+                alert(data.error);
+                return;
+              }
+
+              mainClass.textContent = data.prediction;
+              confidence.textContent = data.confidence.toFixed(2) + "%";
+
+              predictionsList.innerHTML = "";
+
+              data.top3.forEach((pred) => {
+                const li = document.createElement("li");
+                li.textContent = `${pred.class}: ${pred.confidence.toFixed(
+                  2
+                )}%`;
+                predictionsList.appendChild(li);
+              });
+
+              resultsContainer.style.display = "flex";
+            })
+            .catch((error) => {
+              loadingIndicator.style.display = "none";
+              alert("Error processing the image. Please try again.");
+              console.error("Error:", error);
+            });
+        });
+      });
+    </script>
+  </body>
+</html>