Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+import pickle
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+import easyocr
+
+# === Load Model and Label Encoder ===
+model_path = "MobileNetBest_Model.h5"
+label_path = "MobileNet_Label_Encoder.pkl"
+
+model = load_model(model_path)
+print("Model loaded.")
+
+# Load label encoder
+try:
+    with open(label_path, 'rb') as f:
+        label_map = pickle.load(f)
+        index_to_label = {v: k for k, v in label_map.items()}
+    print("Label encoder loaded:", index_to_label)
+except:
+    index_to_label = {0: "Handwritten", 1: "Computerized"}
+    print("Label encoder not found. Using default:", index_to_label)
+
+# === Initialize EasyOCR Reader Once (with GPU) ===
+reader = easyocr.Reader(['en'], gpu=True)
+print("EasyOCR Reader initialized with GPU.")
+
+# === Classify Region ===
+def classify_text_region(region_img):
+    try:
+        region_img = cv2.resize(region_img, (224, 224))
+        region_img = region_img.astype("float32") / 255.0
+        region_img = img_to_array(region_img)
+        region_img = np.expand_dims(region_img, axis=0)
+
+        preds = model.predict(region_img)
+
+        if preds.shape[-1] == 1:
+            return "Computerized" if preds[0][0] > 0.5 else "Handwritten"
+        else:
+            class_idx = np.argmax(preds[0])
+            return index_to_label.get(class_idx, "Unknown")
+    except Exception as e:
+        print("Classification error:", e)
+        return "Unknown"
+
+# === OCR + Annotation ===
+def AnnotatedTextDetection_EasyOCR_from_array(img):
+    results = reader.readtext(img)
+    annotated_results = []
+
+    for (bbox, text, conf) in results[:20]:  # Limit to top 20 boxes
+        if conf < 0.3 or text.strip() == "":
+            continue
+
+        x1, y1 = map(int, bbox[0])
+        x2, y2 = map(int, bbox[2])
+        crop = img[y1:y2, x1:x2]
+        if crop.size == 0:
+            continue
+
+        label = classify_text_region(crop)
+        annotated_results.append(f"{text.strip()} → {label}")
+
+        color = (0, 255, 0) if label == "Computerized" else (255, 0, 0)
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+        cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1)
+
+    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), "\n".join(annotated_results)
+
+# === Gradio Wrapper ===
+def infer(image):
+    img = np.array(image)
+
+    # Resize if image is too large
+    max_dim = 1000
+    if img.shape[0] > max_dim or img.shape[1] > max_dim:
+        scale = max_dim / max(img.shape[0], img.shape[1])
+        img = cv2.resize(img, (int(img.shape[1]*scale), int(img.shape[0]*scale)))
+
+    annotated_img, result_text = AnnotatedTextDetection_EasyOCR_from_array(img)
+    return Image.fromarray(annotated_img), result_text
+
+# === Custom CSS ===
+custom_css = """
+body {
+    background-color: #e6f2ff;
+}
+.gradio-container {
+    border-radius: 12px;
+    padding: 20px;
+    border: 2px solid #007acc;
+}
+.gr-input, .gr-output {
+    border: 1px solid #007acc;
+    border-radius: 10px;
+}
+"""
+
+# === Launch Interface ===
+demo = gr.Interface(
+    fn=infer,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=[
+        gr.Image(type="pil", label="Annotated Image"),
+        gr.Textbox(label="Detected Text and Classification")
+    ],
+    title="Text Detection and Classification",
+    description="This application detects text using EasyOCR and classifies each text region as Handwritten or Computerized using a MobileNet model.",
+    theme="soft",
+    css=custom_css
+)
+demo.launch()