Update app.py

app.py

@@ -1,23 +1,103 @@
-import streamlit as st
-from shadow_map import analyze_uv_exposure
-import tempfile
+import cv2
+import numpy as np
+from paddleocr import PaddleOCR
+import matplotlib.pyplot as plt
+from fpdf import FPDF
+import os
 
-st.title("UV Sterilization Verifier")
+# Initialize PaddleOCR
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
-img = st.file_uploader("Upload Post-Sterilization Image", type=["jpg", "png"])
+def run_ocr(image_path):
+    result = ocr.ocr(image_path, cls=True)
+    texts = []
+    for line in result:
+        for word_info in line:
+            texts.append(word_info[1][0])
+    return texts
 
-if img is not None:
-    # Save uploaded file temporarily
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as f:
-        f.write(img.read())
-        temp_file_path = f.name
+def analyze_uv_coverage(image_path, brightness_threshold=150):
+    # Load image
+    img = cv2.imread(image_path)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
-    # Analyze the image
-    coverage, preview_path = analyze_uv_exposure(temp_file_path)
+    # Threshold image to segment sterilized zones
+    _, binary_mask = cv2.threshold(gray, brightness_threshold, 255, cv2.THRESH_BINARY)
 
-    # Display results
-    st.image(preview_path, caption=f"Coverage: {coverage}%")
-    if coverage >= 95:
-        st.success("Compliant")
-    else:
-        st.error("Fail - Insufficient Exposure")
+    # Calculate coverage
+    total_pixels = binary_mask.size
+    sterilized_pixels = cv2.countNonZero(binary_mask)
+    coverage_percent = (sterilized_pixels / total_pixels) * 100
+
+    # Create color overlay: sterilized in green, unsterilized in red
+    overlay = img.copy()
+    overlay[binary_mask == 255] = [0, 255, 0]        # Green zones: sterilized
+    overlay[binary_mask == 0] = [0, 0, 255]          # Red zones: unsterilized
+
+    # Blend overlay with original image
+    annotated_img = cv2.addWeighted(img, 0.6, overlay, 0.4, 0)
+
+    return annotated_img, coverage_percent
+
+def save_report(image_path, coverage_percent, extracted_texts, output_folder='output'):
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+
+    report_file = os.path.join(output_folder, 'UV_Sterilization_Report.pdf')
+    annotated_image_file = os.path.join(output_folder, 'annotated_image.jpg')
+
+    # Generate annotated image and save
+    annotated_img, coverage = analyze_uv_coverage(image_path)
+    cv2.imwrite(annotated_image_file, annotated_img)
+
+    # Create PDF report
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=14)
+    pdf.cell(200, 10, txt="UV Sterilization Report", ln=True, align='C')
+
+    pdf.ln(10)
+    pdf.set_font("Arial", size=12)
+    pdf.cell(0, 10, f"Sterilization Coverage: {coverage_percent:.2f}%", ln=True)
+    pdf.ln(5)
+
+    pdf.cell(0, 10, "Extracted Texts from Image (OCR):", ln=True)
+    pdf.set_font("Arial", size=10)
+    for text in extracted_texts:
+        pdf.multi_cell(0, 8, f"- {text}")
+
+    pdf.ln(10)
+    pdf.cell(0, 10, "Annotated Image is saved alongside this report.", ln=True)
+
+    # Insert annotated image (resize for PDF)
+    pdf.image(annotated_image_file, x=10, y=pdf.get_y(), w=pdf.w - 20)
+
+    pdf.output(report_file)
+    print(f"Report saved at: {report_file}")
+    print(f"Annotated image saved at: {annotated_image_file}")
+
+def main(image_path):
+    print("Running OCR...")
+    extracted_texts = run_ocr(image_path)
+    print("Extracted Texts:", extracted_texts)
+
+    print("Analyzing UV sterilization coverage...")
+    annotated_img, coverage_percent = analyze_uv_coverage(image_path)
+
+    # Show annotated image using matplotlib
+    plt.imshow(cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB))
+    plt.title(f"UV Sterilization Coverage: {coverage_percent:.2f}%")
+    plt.axis('off')
+    plt.show()
+
+    # Save report and annotated image
+    save_report(image_path, coverage_percent, extracted_texts)
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description="UV Sterilization Analysis App")
+    parser.add_argument('image_path', type=str, help='Path to post-UV sterilization image')
+    args = parser.parse_args()
+
+    main(args.image_path)