sync: ci: trigger initial deploy to Hugging Face

.env

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-OLLAMA_URL="http://localhost:11434"
-OLLAMA_API_KEY="87b344ea09c540848abd777349d64466.PeZFbKB2Y03ddFyD4YXW0TpT"
-OLLAMA_MODEL="llava-llama3"

.gitattributes

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README.md

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----
-title: Mission17 Ai
-emoji: 💻
-colorFrom: pink
-colorTo: purple
-sdk: docker
-pinned: false
-short_description: ai verify image
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

scripts/data_prep/count_dataset.py

@@ -1,43 +1,43 @@
-import os
-
-def count_images():
-    # Define the path to the dataset
-    # Based on your other scripts, it is in ../dataset/mission_dataset
-    base_dir = os.path.dirname(os.path.abspath(__file__))
-    dataset_dir = os.path.join(base_dir, '..', '..', '..', 'dataset', 'mission_dataset')
-
-    print(f"📊 Checking dataset at: {os.path.abspath(dataset_dir)}\n")
-
-    if not os.path.exists(dataset_dir):
-        print(f"❌ Error: Folder not found. Have you run 'organize_dataset.py'?")
-        return
-
-    total_images = 0
-    
-    # Get all subfolders (classes)
-    try:
-        classes = [d for d in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, d))]
-        classes.sort()
-    except Exception as e:
-        print(f"❌ Error reading directory: {e}")
-        return
-
-    print(f"{'CLASS NAME':<35} | {'COUNT':<10} | {'STATUS'}")
-    print("-" * 50)
-
-    for class_name in classes:
-        class_path = os.path.join(dataset_dir, class_name)
-        # Count files that look like images
-        images = [f for f in os.listdir(class_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]
-        count = len(images)
-        
-        status = "✅ Ready" if count >= 100 else "⚠️ Low Data" if count > 0 else "❌ Empty"
-        
-        print(f"{class_name:<35} | {count:<10} | {status}")
-        total_images += count
-
-    print("-" * 50)
-    print(f"✅ TOTAL IMAGES: {total_images}")
-
-if __name__ == "__main__":
+import os
+
+def count_images():
+    # Define the path to the dataset
+    # Based on your other scripts, it is in ../dataset/mission_dataset
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    dataset_dir = os.path.join(base_dir, '..', '..', '..', 'dataset', 'mission_dataset')
+
+    print(f"📊 Checking dataset at: {os.path.abspath(dataset_dir)}\n")
+
+    if not os.path.exists(dataset_dir):
+        print(f"❌ Error: Folder not found. Have you run 'organize_dataset.py'?")
+        return
+
+    total_images = 0
+    
+    # Get all subfolders (classes)
+    try:
+        classes = [d for d in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, d))]
+        classes.sort()
+    except Exception as e:
+        print(f"❌ Error reading directory: {e}")
+        return
+
+    print(f"{'CLASS NAME':<35} | {'COUNT':<10} | {'STATUS'}")
+    print("-" * 50)
+
+    for class_name in classes:
+        class_path = os.path.join(dataset_dir, class_name)
+        # Count files that look like images
+        images = [f for f in os.listdir(class_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]
+        count = len(images)
+        
+        status = "✅ Ready" if count >= 100 else "⚠️ Low Data" if count > 0 else "❌ Empty"
+        
+        print(f"{class_name:<35} | {count:<10} | {status}")
+        total_images += count
+
+    print("-" * 50)
+    print(f"✅ TOTAL IMAGES: {total_images}")
+
+if __name__ == "__main__":
     count_images()

scripts/data_prep/fix_dataset.py

@@ -1,28 +1,28 @@
-import os
-import shutil
-
-# Define paths
-import os
-base_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', '..', 'dataset', 'garbage_classification')
-old_planting = os.path.join(base_dir, "planting")
-new_planting = os.path.join(base_dir, "SDG13_15_Planting")
-
-# Create new folder if it doesn't exist
-if not os.path.exists(new_planting):
-    os.makedirs(new_planting)
-
-# Move files from Old -> New
-if os.path.exists(old_planting):
-    print(f"🔄 Moving files from '{old_planting}' to '{new_planting}'...")
-    files = os.listdir(old_planting)
-    for file in files:
-        old_path = os.path.join(old_planting, file)
-        new_path = os.path.join(new_planting, f"old_{file}") # Rename to avoid conflicts
-        shutil.move(old_path, new_path)
-    
-    # Delete the empty old folder
-    os.rmdir(old_planting)
-    print("✅ Successfully merged folders!")
-    print("🗑️  Deleted old 'planting' folder.")
-else:
+import os
+import shutil
+
+# Define paths
+import os
+base_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', '..', 'dataset', 'garbage_classification')
+old_planting = os.path.join(base_dir, "planting")
+new_planting = os.path.join(base_dir, "SDG13_15_Planting")
+
+# Create new folder if it doesn't exist
+if not os.path.exists(new_planting):
+    os.makedirs(new_planting)
+
+# Move files from Old -> New
+if os.path.exists(old_planting):
+    print(f"🔄 Moving files from '{old_planting}' to '{new_planting}'...")
+    files = os.listdir(old_planting)
+    for file in files:
+        old_path = os.path.join(old_planting, file)
+        new_path = os.path.join(new_planting, f"old_{file}") # Rename to avoid conflicts
+        shutil.move(old_path, new_path)
+    
+    # Delete the empty old folder
+    os.rmdir(old_planting)
+    print("✅ Successfully merged folders!")
+    print("🗑️  Deleted old 'planting' folder.")
+else:
     print("⚠️  Old 'planting' folder not found. Already merged?")

scripts/data_prep/organize_dataset.py

@@ -1,91 +1,91 @@
-import os
-import shutil
-
-# 👇 CONFIGURATION
-# Current script directory
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-# The main project dataset folder (../dataset)
-BASE_DIR = os.path.join(CURRENT_DIR, '..', '..', '..', 'dataset')
-
-# 1. The "Correct" Destination
-FINAL_DEST = os.path.join(BASE_DIR, "mission_dataset")
-
-# 2. The "Old" Kaggle Dataset
-OLD_GARBAGE_DIR = os.path.join(BASE_DIR, "garbage_classification")
-
-# 3. The "Misplaced" Downloads (if any) inside mission17-ai/dataset
-MISPLACED_DIR = os.path.join(CURRENT_DIR, '..', '..', "dataset", "mission_dataset")
-
-# Map OLD folders to NEW SDG destinations
-# We are putting ALL waste items into SDG12 (Responsible Consumption & Production)
-MOVES = {
-    "SDG12_Recycling": [
-        "battery", "brown-glass", "cardboard", 
-        "clothes", "green-glass", "metal", "paper", 
-        "plastic", "shoes", "white-glass"
-    ],
-    "Non_SDG_Invalid": [
-        "trash", "biological"
-    ]
-}
-
-def organize_files():
-    print(f"📦 Organizing dataset...")
-    
-    # Ensure destination exists
-    if not os.path.exists(FINAL_DEST):
-        os.makedirs(FINAL_DEST)
-        print(f"   ✅ Created '{FINAL_DEST}'")
-
-    # --- STEP 1: Merge Kaggle Data ---
-    if os.path.exists(OLD_GARBAGE_DIR):
-        print(f"   🔄 Merging 'garbage_classification'...")
-        for dest_folder, source_folders in MOVES.items():
-            dest_path = os.path.join(FINAL_DEST, dest_folder)
-            if not os.path.exists(dest_path): os.makedirs(dest_path)
-
-            for folder in source_folders:
-                src_path = os.path.join(OLD_GARBAGE_DIR, folder)
-                if os.path.exists(src_path):
-                    # Move files
-                    for file in os.listdir(src_path):
-                        try:
-                            shutil.move(os.path.join(src_path, file), os.path.join(dest_path, f"{folder}_{file}"))
-                        except Exception: pass
-                    # Remove empty folder
-                    try:
-                        os.rmdir(src_path)
-                    except: pass
-        
-        # Try to remove root garbage dir
-        try: os.rmdir(OLD_GARBAGE_DIR)
-        except: pass
-        print("   ✅ Kaggle data merged.")
-
-    # --- STEP 2: Fix Misplaced Downloads ---
-    if os.path.exists(MISPLACED_DIR):
-        print(f"   ⚠️ Found misplaced images in '{MISPLACED_DIR}'. Moving them...")
-        for category in os.listdir(MISPLACED_DIR):
-            src = os.path.join(MISPLACED_DIR, category)
-            dest = os.path.join(FINAL_DEST, category)
-            
-            if os.path.isdir(src):
-                if not os.path.exists(dest): os.makedirs(dest)
-                for file in os.listdir(src):
-                    try:
-                        shutil.move(os.path.join(src, file), os.path.join(dest, file))
-                    except: pass
-                try: os.rmdir(src)
-                except: pass
-        
-        # Cleanup parent 'dataset' in mission17-ai if empty
-        try: 
-            os.rmdir(MISPLACED_DIR)
-            os.rmdir(os.path.join(CURRENT_DIR, '..', '..', "dataset"))
-        except: pass
-        print("   ✅ Misplaced images moved to correct folder.")
-
-    print(f"\n✨ SUCCESS! Dataset is ready at: {FINAL_DEST}")
-
-if __name__ == "__main__":
+import os
+import shutil
+
+# 👇 CONFIGURATION
+# Current script directory
+CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+# The main project dataset folder (../dataset)
+BASE_DIR = os.path.join(CURRENT_DIR, '..', '..', '..', 'dataset')
+
+# 1. The "Correct" Destination
+FINAL_DEST = os.path.join(BASE_DIR, "mission_dataset")
+
+# 2. The "Old" Kaggle Dataset
+OLD_GARBAGE_DIR = os.path.join(BASE_DIR, "garbage_classification")
+
+# 3. The "Misplaced" Downloads (if any) inside mission17-ai/dataset
+MISPLACED_DIR = os.path.join(CURRENT_DIR, '..', '..', "dataset", "mission_dataset")
+
+# Map OLD folders to NEW SDG destinations
+# We are putting ALL waste items into SDG12 (Responsible Consumption & Production)
+MOVES = {
+    "SDG12_Recycling": [
+        "battery", "brown-glass", "cardboard", 
+        "clothes", "green-glass", "metal", "paper", 
+        "plastic", "shoes", "white-glass"
+    ],
+    "Non_SDG_Invalid": [
+        "trash", "biological"
+    ]
+}
+
+def organize_files():
+    print(f"📦 Organizing dataset...")
+    
+    # Ensure destination exists
+    if not os.path.exists(FINAL_DEST):
+        os.makedirs(FINAL_DEST)
+        print(f"   ✅ Created '{FINAL_DEST}'")
+
+    # --- STEP 1: Merge Kaggle Data ---
+    if os.path.exists(OLD_GARBAGE_DIR):
+        print(f"   🔄 Merging 'garbage_classification'...")
+        for dest_folder, source_folders in MOVES.items():
+            dest_path = os.path.join(FINAL_DEST, dest_folder)
+            if not os.path.exists(dest_path): os.makedirs(dest_path)
+
+            for folder in source_folders:
+                src_path = os.path.join(OLD_GARBAGE_DIR, folder)
+                if os.path.exists(src_path):
+                    # Move files
+                    for file in os.listdir(src_path):
+                        try:
+                            shutil.move(os.path.join(src_path, file), os.path.join(dest_path, f"{folder}_{file}"))
+                        except Exception: pass
+                    # Remove empty folder
+                    try:
+                        os.rmdir(src_path)
+                    except: pass
+        
+        # Try to remove root garbage dir
+        try: os.rmdir(OLD_GARBAGE_DIR)
+        except: pass
+        print("   ✅ Kaggle data merged.")
+
+    # --- STEP 2: Fix Misplaced Downloads ---
+    if os.path.exists(MISPLACED_DIR):
+        print(f"   ⚠️ Found misplaced images in '{MISPLACED_DIR}'. Moving them...")
+        for category in os.listdir(MISPLACED_DIR):
+            src = os.path.join(MISPLACED_DIR, category)
+            dest = os.path.join(FINAL_DEST, category)
+            
+            if os.path.isdir(src):
+                if not os.path.exists(dest): os.makedirs(dest)
+                for file in os.listdir(src):
+                    try:
+                        shutil.move(os.path.join(src, file), os.path.join(dest, file))
+                    except: pass
+                try: os.rmdir(src)
+                except: pass
+        
+        # Cleanup parent 'dataset' in mission17-ai if empty
+        try: 
+            os.rmdir(MISPLACED_DIR)
+            os.rmdir(os.path.join(CURRENT_DIR, '..', '..', "dataset"))
+        except: pass
+        print("   ✅ Misplaced images moved to correct folder.")
+
+    print(f"\n✨ SUCCESS! Dataset is ready at: {FINAL_DEST}")
+
+if __name__ == "__main__":
     organize_files()

scripts/testing/test_upload.html

@@ -1,61 +1,61 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <title>Mission 17 AI Scanner</title>
-    <style>
-        body { font-family: 'Segoe UI', sans-serif; text-align: center; padding: 50px; background-color: #f4f4f9; }
-        .card { background: white; padding: 40px; border-radius: 15px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); display: inline-block; max-width: 400px; }
-        button { background: #007bff; color: white; border: none; padding: 12px 24px; border-radius: 5px; cursor: pointer; font-size: 16px; margin-top: 15px; }
-        button:hover { background: #0056b3; }
-        #result { margin-top: 25px; font-weight: bold; }
-        .verified { color: #28a745; background: #e6fffa; padding: 15px; border-radius: 8px; border: 1px solid #28a745; }
-        .rejected { color: #dc3545; background: #fff5f5; padding: 15px; border-radius: 8px; border: 1px solid #dc3545; }
-    </style>
-</head>
-<body>
-    <div class="card">
-        <h2>🤖 Mission 17 AI Scanner</h2>
-        <p>Upload a photo to verify your mission!</p>
-        
-        <input type="file" id="fileInput" accept="image/*">
-        <br>
-        <button onclick="scanImage()">🔍 Scan Mission</button>
-
-        <div id="result"></div>
-    </div>
-
-    <script>
-        async function scanImage() {
-            const fileInput = document.getElementById('fileInput');
-            const resultDiv = document.getElementById('result');
-            
-            if (!fileInput.files[0]) {
-                alert("Please select an image first!");
-                return;
-            }
-
-            resultDiv.innerHTML = "⏳ Scanning...";
-            
-            const formData = new FormData();
-            formData.append("file", fileInput.files[0]);
-
-            try {
-                const response = await fetch("http://127.0.0.1:5000/predict", { method: "POST", body: formData });
-                const data = await response.json();
-                
-                const colorClass = data.verdict === "VERIFIED" ? "verified" : "rejected";
-                resultDiv.innerHTML = `
-                    <div class="${colorClass}">
-                        <h3>${data.message}</h3>
-                        <p><strong>📷 Type:</strong> ${data.source_check}</p>
-                        <p><strong>🎯 Accuracy:</strong> ${data.confidence}</p>
-                        <p><strong>🌍 SDG:</strong> ${data.sdg}</p>
-                    </div>`;
-            } catch (error) {
-                resultDiv.innerHTML = "❌ Error connecting to server. Is app.py running?";
-            }
-        }
-    </script>
-</body>
-</html>
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Mission 17 AI Scanner</title>
+    <style>
+        body { font-family: 'Segoe UI', sans-serif; text-align: center; padding: 50px; background-color: #f4f4f9; }
+        .card { background: white; padding: 40px; border-radius: 15px; box-shadow: 0 4px 15px rgba(0,0,0,0.1); display: inline-block; max-width: 400px; }
+        button { background: #007bff; color: white; border: none; padding: 12px 24px; border-radius: 5px; cursor: pointer; font-size: 16px; margin-top: 15px; }
+        button:hover { background: #0056b3; }
+        #result { margin-top: 25px; font-weight: bold; }
+        .verified { color: #28a745; background: #e6fffa; padding: 15px; border-radius: 8px; border: 1px solid #28a745; }
+        .rejected { color: #dc3545; background: #fff5f5; padding: 15px; border-radius: 8px; border: 1px solid #dc3545; }
+    </style>
+</head>
+<body>
+    <div class="card">
+        <h2>🤖 Mission 17 AI Scanner</h2>
+        <p>Upload a photo to verify your mission!</p>
+        
+        <input type="file" id="fileInput" accept="image/*">
+        <br>
+        <button onclick="scanImage()">🔍 Scan Mission</button>
+
+        <div id="result"></div>
+    </div>
+
+    <script>
+        async function scanImage() {
+            const fileInput = document.getElementById('fileInput');
+            const resultDiv = document.getElementById('result');
+            
+            if (!fileInput.files[0]) {
+                alert("Please select an image first!");
+                return;
+            }
+
+            resultDiv.innerHTML = "⏳ Scanning...";
+            
+            const formData = new FormData();
+            formData.append("file", fileInput.files[0]);
+
+            try {
+                const response = await fetch("http://127.0.0.1:5000/predict", { method: "POST", body: formData });
+                const data = await response.json();
+                
+                const colorClass = data.verdict === "VERIFIED" ? "verified" : "rejected";
+                resultDiv.innerHTML = `
+                    <div class="${colorClass}">
+                        <h3>${data.message}</h3>
+                        <p><strong>📷 Type:</strong> ${data.source_check}</p>
+                        <p><strong>🎯 Accuracy:</strong> ${data.confidence}</p>
+                        <p><strong>🌍 SDG:</strong> ${data.sdg}</p>
+                    </div>`;
+            } catch (error) {
+                resultDiv.innerHTML = "❌ Error connecting to server. Is app.py running?";
+            }
+        }
+    </script>
+</body>
+</html>

scripts/training/evaluate_model.py

@@ -1,57 +1,57 @@
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-from tensorflow.keras.applications.efficientnet import preprocess_input
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
-import matplotlib.pyplot as plt
-import seaborn as sns
-import os
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-
-print("⏳ Loading AI Model...")
-# 👇 Ensure this is your correct model name!
-model = tf.keras.models.load_model(os.path.join(BASE_DIR, '..', '..', 'mission_model.h5')) 
-
-print("📁 Loading Test Dataset...")
-# 👇 Pointing to the new TEST split folder
-test_dir = os.path.join(BASE_DIR, '..', '..', '..', 'dataset', 'mission_dataset_split', 'test')
-
-test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
-test_generator = test_datagen.flow_from_directory(
-    test_dir,
-    target_size=(224, 224), 
-    batch_size=32,
-    class_mode='categorical',
-    shuffle=False
-)
-
-print("🤖 Running Predictions (This may take a minute)...")
-Y_pred = model.predict(test_generator)
-y_pred_classes = np.argmax(Y_pred, axis=1) # 👈 FIXED: Grabs the top prediction per image
-y_true = test_generator.classes
-
-print("\n" + "="*50)
-print("🏆 CAPSTONE AI PERFORMANCE METRICS 🏆")
-print("="*50)
-
-# 👈 FIXED: Added average='weighted' to handle all 10 classes correctly
-accuracy = accuracy_score(y_true, y_pred_classes)
-precision = precision_score(y_true, y_pred_classes, average='weighted', zero_division=0)
-recall = recall_score(y_true, y_pred_classes, average='weighted', zero_division=0)
-f1 = f1_score(y_true, y_pred_classes, average='weighted', zero_division=0)
-
-print(f"✅ Accuracy:  {accuracy * 100:.2f}%")
-print(f"🎯 Precision: {precision * 100:.2f}%")
-print(f"🔍 Recall:    {recall * 100:.2f}%")
-print(f"⚖️ F1-Score:  {f1 * 100:.2f}%")
-print("="*50)
-
-# Make the confusion matrix chart larger to fit 10 classes
-cm = confusion_matrix(y_true, y_pred_classes)
-plt.figure(figsize=(10, 8))
-sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
-plt.title('AI Confusion Matrix (10 Classes)')
-plt.ylabel('Actual Image Class')
-plt.xlabel('AI Prediction')
-plt.savefig(os.path.join(BASE_DIR, '..', '..', 'outputs', 'confusion_matrix.png'))
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+print("⏳ Loading AI Model...")
+# 👇 Ensure this is your correct model name!
+model = tf.keras.models.load_model(os.path.join(BASE_DIR, '..', '..', 'mission_model.h5')) 
+
+print("📁 Loading Test Dataset...")
+# 👇 Pointing to the new TEST split folder
+test_dir = os.path.join(BASE_DIR, '..', '..', '..', 'dataset', 'mission_dataset_split', 'test')
+
+test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
+test_generator = test_datagen.flow_from_directory(
+    test_dir,
+    target_size=(224, 224), 
+    batch_size=32,
+    class_mode='categorical',
+    shuffle=False
+)
+
+print("🤖 Running Predictions (This may take a minute)...")
+Y_pred = model.predict(test_generator)
+y_pred_classes = np.argmax(Y_pred, axis=1) # 👈 FIXED: Grabs the top prediction per image
+y_true = test_generator.classes
+
+print("\n" + "="*50)
+print("🏆 CAPSTONE AI PERFORMANCE METRICS 🏆")
+print("="*50)
+
+# 👈 FIXED: Added average='weighted' to handle all 10 classes correctly
+accuracy = accuracy_score(y_true, y_pred_classes)
+precision = precision_score(y_true, y_pred_classes, average='weighted', zero_division=0)
+recall = recall_score(y_true, y_pred_classes, average='weighted', zero_division=0)
+f1 = f1_score(y_true, y_pred_classes, average='weighted', zero_division=0)
+
+print(f"✅ Accuracy:  {accuracy * 100:.2f}%")
+print(f"🎯 Precision: {precision * 100:.2f}%")
+print(f"🔍 Recall:    {recall * 100:.2f}%")
+print(f"⚖️ F1-Score:  {f1 * 100:.2f}%")
+print("="*50)
+
+# Make the confusion matrix chart larger to fit 10 classes
+cm = confusion_matrix(y_true, y_pred_classes)
+plt.figure(figsize=(10, 8))
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
+plt.title('AI Confusion Matrix (10 Classes)')
+plt.ylabel('Actual Image Class')
+plt.xlabel('AI Prediction')
+plt.savefig(os.path.join(BASE_DIR, '..', '..', 'outputs', 'confusion_matrix.png'))
 print("\n📊 Saved 'confusion_matrix.png' to your outputs folder. Put this in your presentation!")