Adding Arabic Dates Classification using DL (#339)

* Arabic Dates Classification using DL #329

* Updating ArabicDatesClassifierModel.py

* Update

* Update for Frontend of ArabicDatesClassifierModel

* reform for 'Lint and Format / lint-format'

* Modify model

* 2-space indentation formatting

* new edit for ArabicDatesClassifierModel

* Modified ArabicDatesClassifierModel (with kaggle model output)

* Final edit for ArabicDatesClassifierModel

Author: SupratikB23

.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
+
+def arabicDatesClassifierModel():
+  # 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")