Adding Arabic Dates Classification using DL #329

.gitignore

@@ -21,3 +21,4 @@ myenv
 *.log
 
 .env
+.vscode

src/apps/pages/models/ImageProcessing/ArabicDatesClassifierModel.py

@@ -0,0 +1,102 @@
+import io
+import pickle
+import sys
+import os
+import numpy as np
+from PIL import Image
+
+import streamlit as st
+import torch
+from torch import nn
+from torchvision import models, transforms
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../..")))
+
+from src.helpers.kaggle import downloadNotebookOutput
+
+# APP HEADER
+st.title("Arabic Dates Classification 🍂")
+st.markdown(
+  "This pretrained model classifies an image of **Arabic Dates** into one of the 9 varieties commonly found in the Arabian region."
+)
+
+# LOAD MODEL
+@st.cache_resource
+def load_model_data():
+  try:
+    downloadNotebookOutput("supratikbhowal", "arabic-dates-classification", "notebook")
+
+    PICKLE_SAVE_PATH = "notebook/arabic_dates_classnames.pkl"
+    with open(PICKLE_SAVE_PATH, "rb") as f:
+      class_names = pickle.load(f)
+
+    model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+    model.fc = nn.Sequential(
+      nn.Linear(model.fc.in_features, 512),
+      nn.ReLU(),
+      nn.Dropout(0.4),
+      nn.Linear(512, len(class_names))
+    )
+
+    MODEL_SAVE_PATH = "notebook/arabic_dates_model.pth"
+    checkpoint = torch.load(MODEL_SAVE_PATH, map_location=torch.device("cpu"))
+    model.load_state_dict(checkpoint["model_state_dict"], strict=False)
+    model.eval()
+
+    return model, class_names
+
+  except Exception as e:
+    st.error(f"🚨 Failed to load model or data: {e}")
+    st.stop()
+
+model, CLASS_NAMES = load_model_data()
+st.success("✅ Model and class names loaded successfully!")
+
+# IMAGE PREPROCESSING
+transform = transforms.Compose([
+  transforms.Resize((224, 224)),
+  transforms.ToTensor(),
+  transforms.Normalize([0.485, 0.456, 0.406],
+                       [0.229, 0.224, 0.225])
+])
+
+def preprocess_image(image: Image.Image):
+  image = image.convert("RGB")
+  return transform(image).unsqueeze(0)
+
+def predict(model, image_tensor, class_names):
+  with torch.no_grad():
+    outputs = model(image_tensor)
+    probs = torch.nn.functional.softmax(outputs[0], dim=0)
+  probs = probs.numpy()
+  sorted_indices = np.argsort(probs)[::-1]
+  sorted_probs = probs[sorted_indices]
+  sorted_labels = [class_names[i] for i in sorted_indices]
+  return sorted_labels, sorted_probs
+
+# FILE UPLOAD
+uploaded_file = st.file_uploader("📸 Upload an image of Arabic Dates", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+  image_data = uploaded_file.read()
+  image = Image.open(io.BytesIO(image_data))
+  st.image(image, caption="Uploaded Image", width=400)
+  st.write("🔍 Analyzing...")
+
+  image_tensor = preprocess_image(image)
+  labels, probs = predict(model, image_tensor, CLASS_NAMES)
+
+  top_class = labels[0]
+  top_prob = probs[0] * 100
+
+  st.markdown(f"### ✅ Predicted Class: **{top_class}** ({top_prob:.2f}% confidence)")
+
+  prob_dict = {labels[i]: float(probs[i] * 100) for i in range(len(labels))}
+  st.write("#### 📊 Probability Distribution:")
+  st.bar_chart(prob_dict)
+
+else:
+  st.info("👆 Upload a clear image of dates to classify.")
+
+# FOOTER
+st.caption("Trained with ResNet50 on the Arabian Dates Dataset — 9 classes")