Update app.py

app.py

@@ -1,12 +1,13 @@
-import streamlit as st
+import gradio as gr
 import cv2
 import numpy as np
 from fpdf import FPDF
 import tempfile
 import os
 from PIL import Image
-import io
+import pytesseract
 
+# Analyze UV coverage by brightness threshold
 def analyze_uv_coverage(img, brightness_threshold=150):
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     _, binary_mask = cv2.threshold(gray, brightness_threshold, 255, cv2.THRESH_BINARY)
@@ -22,6 +23,7 @@ def analyze_uv_coverage(img, brightness_threshold=150):
     annotated_img = cv2.addWeighted(img, 0.6, overlay, 0.4, 0)
     return annotated_img, coverage_percent
 
+# Create PDF report with coverage, OCR text, and annotated image
 def create_pdf_report(coverage_percent, extracted_texts, annotated_image_path, output_path):
     pdf = FPDF()
     pdf.add_page()
@@ -35,60 +37,66 @@ def create_pdf_report(coverage_percent, extracted_texts, annotated_image_path, o
 
     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}")
+    if extracted_texts:
+        for text in extracted_texts:
+            pdf.multi_cell(0, 8, f"- {text}")
+    else:
+        pdf.cell(0, 8, "No text detected.", ln=True)
 
     pdf.ln(10)
     pdf.cell(0, 10, "Annotated Image:", ln=True)
     pdf.image(annotated_image_path, x=10, y=pdf.get_y(), w=pdf.w - 20)
     pdf.output(output_path)
 
-def main():
-    st.title("UV Sterilization Coverage Analyzer")
+def process_image(input_img, brightness_threshold=150):
+    # Convert PIL image to OpenCV format
+    img = cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)
 
-    uploaded_file = st.file_uploader("Upload post-UV sterilization image", type=['png','jpg','jpeg'])
-    brightness_threshold = st.slider("Brightness Threshold (for sterilized zone detection)", 50, 255, 150)
+    # Run OCR using pytesseract
+    ocr_result = pytesseract.image_to_string(input_img)
+    extracted_texts = [line.strip() for line in ocr_result.split('\n') if line.strip()]
 
-    if uploaded_file is not None:
-        # Read image
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-        img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+    # Analyze UV coverage
+    annotated_img, coverage_percent = analyze_uv_coverage(img, brightness_threshold)
 
-        # Run OCR with Streamlit experimental API (uses Tesseract)
-        pil_image = Image.open(uploaded_file)
-        ocr_result = st.experimental_text_ocr(pil_image)
-        extracted_texts = [item['text'] for item in ocr_result if item['text'].strip() != ""]
+    # Save annotated image temporarily for PDF
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img_file:
+        cv2.imwrite(temp_img_file.name, annotated_img)
 
-        # Analyze UV coverage
-        annotated_img, coverage_percent = analyze_uv_coverage(img, brightness_threshold)
+        # Save PDF report temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf_file:
+            create_pdf_report(coverage_percent, extracted_texts, temp_img_file.name, temp_pdf_file.name)
 
-        # Show results
-        st.subheader("Extracted Text from Image:")
-        if extracted_texts:
-            for txt in extracted_texts:
-                st.write(f"- {txt}")
-        else:
-            st.write("No text detected.")
+            # Read PDF bytes for download
+            with open(temp_pdf_file.name, "rb") as f:
+                pdf_bytes = f.read()
 
-        st.subheader(f"UV Sterilization Coverage: {coverage_percent:.2f}%")
-        st.image(cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB), caption="Annotated Image (Green = Sterilized, Red = Unsterilized)")
+    # Convert annotated image back to RGB for display
+    annotated_img_rgb = cv2.cvtColor(annotated_img, cv2.COLOR_BGR2RGB)
 
-        # Save annotated image temporarily
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img_file:
-            cv2.imwrite(temp_img_file.name, annotated_img)
+    # Clean up temp files
+    os.unlink(temp_img_file.name)
+    os.unlink(temp_pdf_file.name)
 
-            # Create PDF report
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf_file:
-                create_pdf_report(coverage_percent, extracted_texts, temp_img_file.name, temp_pdf_file.name)
+    report_text = f"UV Sterilization Coverage: {coverage_percent:.2f}%\n\nExtracted Texts:\n"
+    report_text += "\n".join(extracted_texts) if extracted_texts else "No text detected."
 
-                # Provide download link for PDF
-                with open(temp_pdf_file.name, "rb") as f:
-                    pdf_bytes = f.read()
-                st.download_button(label="Download UV Sterilization Report (PDF)", data=pdf_bytes, file_name="UV_Sterilization_Report.pdf")
+    return annotated_img_rgb, report_text, pdf_bytes
 
-                # Cleanup temp files on app rerun
-                os.unlink(temp_img_file.name)
-                os.unlink(temp_pdf_file.name)
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Post-UV Sterilization Image"),
+        gr.Slider(50, 255, value=150, step=1, label="Brightness Threshold")
+    ],
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Image"),
+        gr.Textbox(label="UV Sterilization Report", lines=10),
+        gr.File(label="Download PDF Report")
+    ],
+    title="UV Sterilization Coverage Analyzer",
+    description="Upload a post-UV sterilization image to analyze surface coverage and generate a compliance report."
+)
 
 if __name__ == "__main__":
-    main()
+    iface.launch()