Delete app.py

app.py

@@ -1,108 +0,0 @@
-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)
-
-# === 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 and Annotate ===
-def AnnotatedTextDetection_EasyOCR_from_array(img):
-    reader = easyocr.Reader(['en'], gpu=False)
-    results = reader.readtext(img)
-    annotated_results = []
-
-    for (bbox, text, conf) in results:
-        if conf < 0.3 or text.strip() == "":
-            continue
-
-        x1, y1 = map(int, bbox[0])
-        x2, y2 = map(int, bbox[2])
-        w, h = x2 - x1, y2 - y1
-
-        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)
-    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 Clssification",
-    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()