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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import os
+import glob
+import zipfile
+import time
+
+# --- PAGE CONFIG ---
+st.set_page_config(page_title="Cloud Inventory System", layout="wide")
+
+# --- 1. SETUP & UNZIP LOGIC ---
+# We check if the folder exists. If not, we unzip the uploaded file.
+IMAGES_DIR = "sample_fruits_50"
+ZIP_FILE = "sample_fruits_50.zip"
+
+if not os.path.exists(IMAGES_DIR):
+    if os.path.exists(ZIP_FILE):
+        with st.spinner("Unpacking image database..."):
+            with zipfile.ZipFile(ZIP_FILE, 'r') as zip_ref:
+                zip_ref.extractall(".") # Extract to current directory
+            st.success("Database loaded!")
+    else:
+        st.warning(f"⚠️ Please upload '{ZIP_FILE}' to the Files tab!")
+
+# --- 2. LOAD MODEL ---
+@st.cache_resource
+def load_model():
+    model_path = "fruit_classifier.h5"
+    if not os.path.exists(model_path):
+        return None
+    return tf.keras.models.load_model(model_path)
+
+model = load_model()
+
+# --- 3. HELPER FUNCTIONS ---
+CLASS_NAMES = [
+    'fresh_apple', 'fresh_banana', 'fresh_grape', 'fresh_orange', 'fresh_pomegranate',
+    'rotten_apple', 'rotten_banana', 'rotten_grape', 'rotten_orange', 'rotten_pomegranate'
+]
+
+def get_initial_db():
+    fruits = ['Apple', 'Banana', 'Grape', 'Orange', 'Pomegranate']
+    data = {fruit: {'Fresh Qty': 0, 'Rotten Qty': 0} for fruit in fruits}
+    return data
+
+# --- 4. MAIN APP UI ---
+st.title("🏭 Cloud AI Inventory Scan")
+st.markdown("This system will scan the **50 test images** uploaded to the cloud.")
+
+if model is None:
+    st.error("Model file not found. Please upload 'fruit_classifier_final.h5'.")
+    st.stop()
+
+if st.button("🔍 Start Cloud Scan"):
+    
+    # Get list of images
+    if os.path.exists(IMAGES_DIR):
+        all_images = glob.glob(os.path.join(IMAGES_DIR, "**", "*.*"), recursive=True)
+        # Filter for valid images only
+        files_to_scan = [f for f in all_images if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
+        # Pick 15 random ones
+        import random
+        if len(files_to_scan) > 15:
+            files_to_scan = random.sample(files_to_scan, 15)
+    else:
+        st.error("Image folder not found! Did the zip file unzip?")
+        st.stop()
+
+    # Layout
+    col1, col2 = st.columns([1.5, 1])
+    with col1:
+        st.subheader("📦 Live Inventory")
+        table_placeholder = st.empty()
+    with col2:
+        st.subheader("📷 Feed")
+        image_placeholder = st.empty()
+
+    # Init Data
+    db_data = get_initial_db()
+    current_df = pd.DataFrame.from_dict(db_data, orient='index')
+    table_placeholder.table(current_df)
+    progress_bar = st.progress(0)
+
+    # LOOP
+    for i, filepath in enumerate(files_to_scan):
+        # 1. Display Image
+        image_placeholder.image(filepath, caption=f"Item #{i+1}", width=400)
+        
+        # 2. Predict
+        try:
+            # Preprocess
+            img = Image.open(filepath)
+            img = img.resize((224, 224))
+            img_arr = np.array(img)
+            if img_arr.shape[-1] == 4: img_arr = img_arr[..., :3]
+            img_arr = np.expand_dims(img_arr, axis=0) / 255.0
+            
+            # Inference
+            preds = model.predict(img_arr, verbose=0)
+            idx = np.argmax(preds[0])
+            label = CLASS_NAMES[idx]
+            
+            # Parse
+            parts = label.split('_')
+            quality = parts[0]
+            fruit = parts[1].title()
+
+            # Update DB
+            if quality == 'fresh':
+                db_data[fruit]['Fresh Qty'] += 1
+            else:
+                db_data[fruit]['Rotten Qty'] += 1
+            
+            # Update Table
+            current_df = pd.DataFrame.from_dict(db_data, orient='index')
+            table_placeholder.table(current_df)
+            
+            time.sleep(0.2) # Visual delay
+
+        except Exception as e:
+            st.error(f"Error: {e}")
+
+        progress_bar.progress((i + 1) / len(files_to_scan))
+
+    st.success("Scan Complete!")
+    
+    # Graph
+    st.divider()
+    st.subheader("📊 Final Cloud Report")
+    st.bar_chart(current_df, color=["#4CAF50", "#FF5252"])

fruit_classifier.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bef1973c85d090d7afcc41b9ed470e4d7fb102907b4a24ee5dd73cdeb92d2af3
+size 40457400

requirements.txt

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+streamlit
+tensorflow
+numpy
+pandas
+pillow

sample_fruits_50.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7432d38dfcff2e9ad3ab61baea11bb1b0834571e78a06e5ac9bbf68f918208ec
+size 19822959