Update app.py

app.py

@@ -1,4 +1,4 @@
-from flask import Flask, request, jsonify, render_template
+from flask import Flask, request, jsonify, render_template, Response
 import numpy as np
 from PIL import Image, ImageOps, ImageFilter
 import tensorflow as tf
@@ -9,7 +9,7 @@ from io import BytesIO
 import sqlite3
 from datetime import datetime
 
-# டேட்டாபேஸ் உருவாக்கும் கோடு (Database Setup)
+# டேட்டாபேஸ் உருவாக்கும் கோடு
 def init_db():
     conn = sqlite3.connect('reticheck_patients.db')
     c = conn.cursor()
@@ -22,7 +22,7 @@ def init_db():
     conn.commit()
     conn.close()
 
-init_db() # ஆப் ஆன் ஆனதும் டேட்டாபேஸ் ரெடி ஆகிடும்
+init_db()
 
 app = Flask(__name__)
 
@@ -41,33 +41,30 @@ cdss_data = {
     "Glaucoma": {"status": "Glaucoma Suspect", "triage": "REFERRAL", "advice": "Optic nerve damage detected.", "action": "Refer for IOP Test."}
 }
 
-# 2. XAI Heatmap Functions (ACCURATE VERSION)
+# 2. XAI Heatmap Functions
 def generate_robust_heatmap(img_array, model, class_index, patch_size=16, step=8):
     try:
         base_pred = model.predict(img_array, verbose=0)[0][class_index]
         patches, coords = [], []
         img_size = 224
         
-        # Scanning the image with a smaller, finer box
         for y in range(0, img_size, step):
             for x in range(0, img_size, step):
                 y_end, x_end = min(y + patch_size, img_size), min(x + patch_size, img_size)
                 if (y_end - y) < 4 or (x_end - x) < 4: continue
                 
                 occ_img = img_array.copy()
-                occ_img[0, y:y_end, x:x_end, :] = -1 # Blackout small region
+                occ_img[0, y:y_end, x:x_end, :] = -1 
                 patches.append(occ_img[0])
                 coords.append((y, y_end, x, x_end))
                 
         if not patches: return None
         
-        # Predict all patches
         preds = model.predict(np.array(patches), batch_size=32, verbose=0)[:, class_index]
         
         heatmap = np.zeros((img_size, img_size))
         for i, (y, y_end, x, x_end) in enumerate(coords):
             drop = base_pred - preds[i]
-            # STRICT ACCURACY: Only mark regions where hiding it drops confidence significantly (>2%)
             if drop > 0.02: 
                 heatmap[y:y_end, x:x_end] += drop
                 
@@ -76,11 +73,9 @@ def generate_robust_heatmap(img_array, model, class_index, patch_size=16, step=8
         if max_val > 0: 
             heatmap /= max_val
             
-        # Smooth the heatmap so it looks like medical imaging, not blocky pixels
         heatmap_img = Image.fromarray(np.uint8(255 * heatmap)).filter(ImageFilter.GaussianBlur(radius=6))
         return np.array(heatmap_img) / 255.0
     except Exception as e: 
-        print("Heatmap Error:", e)
         return None
 
 def overlay_heatmap(img, heatmap, alpha=0.5):
@@ -104,16 +99,35 @@ def upload(): return render_template('upload.html')
 @app.route('/result')
 def result(): return render_template('result.html')
 
-# DATABASE க்கான புது ரூட் (NEW ROUTE FOR DATABASE)
 @app.route('/database')
 def database():
     conn = sqlite3.connect('reticheck_patients.db')
     c = conn.cursor()
-    c.execute("SELECT * FROM history ORDER BY id DESC") # கடைசியாக செக் பண்ணியதை முதலில் காட்ட
+    c.execute("SELECT * FROM history ORDER BY id DESC")
     records = c.fetchall()
     conn.close()
     return render_template('database.html', records=records)
 
+# --- PUDHU CODE: EXCEL DOWNLOAD MAGIC 👇 ---
+@app.route('/export_csv')
+def export_csv():
+    conn = sqlite3.connect('reticheck_patients.db')
+    c = conn.cursor()
+    c.execute("SELECT * FROM history ORDER BY id DESC")
+    records = c.fetchall()
+    conn.close()
+
+    def generate():
+        # எக்செல் சீட்டில் மேல வரும் தலைப்புகள் (Headings)
+        yield 'Patient ID,Date & Time,Predicted Disease,AI Confidence,Triage Status\n'
+        # டேட்டாபேஸில் உள்ள ஒவ்வொரு ரிசல்ட்டும் வரியாக மாறும்
+        for row in records:
+            yield f"{row[0]},{row[1]},{row[2]},{row[3]},{row[4]}\n"
+
+    # இதை ஒரு ஃபைலாக போனுக்கு அனுப்பும் கமாண்ட்
+    return Response(generate(), mimetype='text/csv', headers={'Content-Disposition': 'attachment; filename=RetiCheck_Patients_Report.csv'})
+# --- PUDHU CODE END 👆 ---
+
 @app.route('/predict', methods=['POST'])
 def predict():
     file = request.files['image']
@@ -136,7 +150,6 @@ def predict():
 
     report = cdss_data[disease]
 
-    # --- DATABASE ல் சேமிப்பதற்கான கோடு (NEW CODE) ---
     try:
         conn = sqlite3.connect('reticheck_patients.db')
         c = conn.cursor()
@@ -147,14 +160,11 @@ def predict():
         conn.close()
     except Exception as db_error:
         print(f"Database Save Error: {db_error}")
-    # ------------------------------------------------
 
-    # Convert Original Image to Base64
     buf = BytesIO()
     image_resized.save(buf, format="JPEG")
     orig_b64 = base64.b64encode(buf.getvalue()).decode('utf-8')
 
-    # Generate Heatmap (Only if Disease)
     heat_b64 = ""
     heatmap_generated = False
     if disease != "Normal":