Update app.py

app.py

@@ -1,493 +1,558 @@
 import os
-os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
-from flask import Flask, render_template, request, redirect, url_for, session, send_file
-from flask_sqlalchemy import SQLAlchemy
-from flask_migrate import Migrate
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import pickle
-import io
-import os
-import matplotlib.pyplot as plt
-from reportlab.lib.pagesizes import A4
-from reportlab.lib import colors
-from reportlab.pdfgen import canvas
-from reportlab.lib.units import inch
-from datetime import datetime
-import logging
-from flask_mail import Mail, Message
-from flask import jsonify, url_for
-
-app = Flask(__name__)
-app.secret_key = "e3f6f40bb8b2471b9f07c4025d845be9"
-
-# Database configuration
-app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/snapsin.db'
-app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
-db = SQLAlchemy(app)
-migrate = Migrate(app, db)
-
-# Mail configuration
-app.config['MAIL_SERVER'] = 'smtp.gmail.com'
-app.config['MAIL_PORT'] = 465
-app.config['MAIL_USERNAME'] = os.environ.get('MAIL_USERNAME')
-app.config['MAIL_PASSWORD'] = os.environ.get('MAIL_PASSWORD')
-app.config['MAIL_USE_TLS'] = False
-app.config['MAIL_USE_SSL'] = True
-mail = Mail(app)
-
-MODEL_PATH = "skin_lesion_model.h5"
-HISTORY_PATH = "training_history.pkl"
-PLOT_PATH = "/tmp/static/training_plot.png"
-LOGO_PATH = "static/logo.jpg"
-IMG_SIZE = (224, 224)
-CONFIDENCE_THRESHOLD = 0.30
-
-label_map = {
-    0: "Melanoma",
-    1: "Melanocytic nevus",
-    2: "Basal cell carcinoma",
-    3: "Actinic keratosis",
-    4: "Benign keratosis",
-    5: "Dermatofibroma",
-    6: "Vascular lesion",
-    7: "Squamous cell carcinoma"
-}
-
-recommendations = {
-    "Melanoma": {
-        "solutions": [
-            "Consult a dermatologist immediately.",
-            "Surgical removal is typically required.",
-            "Regular follow-up and screening for metastasis."
-        ],
-        "medications": ["Interferon alfa-2b", "Vemurafenib", "Dacarbazine"]
-    },
-    "Melanocytic nevus": {
-        "solutions": [
-            "Usually benign and requires no treatment.",
-            "Monitor for any change in shape or color."
-        ],
-        "medications": ["No medication necessary unless changes occur."]
-    },
-    "Basal cell carcinoma": {
-        "solutions": [
-            "Surgical excision or Mohs surgery.",
-            "Topical treatments if superficial.",
-            "Radiation in select cases."
-        ],
-        "medications": ["Imiquimod cream", "Fluorouracil cream", "Vismodegib"]
-    },
-    "Actinic keratosis": {
-        "solutions": [
-            "Cryotherapy or topical treatments.",
-            "Avoid prolonged sun exposure.",
-            "Use of sunscreen regularly."
-        ],
-        "medications": ["Fluorouracil", "Imiquimod", "Diclofenac gel"]
-    },
-    "Benign keratosis": {
-        "solutions": [
-            "Generally harmless and often left untreated.",
-            "Can be removed for cosmetic reasons."
-        ],
-        "medications": ["No medication required unless infected."]
-    },
-    "Dermatofibroma": {
-        "solutions": [
-            "Benign skin growth, no treatment needed.",
-            "Surgical removal if painful or for cosmetic reasons."
-        ],
-        "medications": ["No medication needed."]
-    },
-    "Vascular lesion": {
-        "solutions": [
-            "Treatment depends on type (e.g., hemangioma).",
-            "Laser therapy is commonly used.",
-            "Observation if no complications."
-        ],
-        "medications": ["Beta-blockers (e.g., propranolol for hemangioma)"]
-    },
-    "Squamous cell carcinoma": {
-        "solutions": [
-            "Surgical removal is standard.",
-            "Follow-up for recurrence or metastasis.",
-            "Avoid sun exposure and use sunscreen."
-        ],
-        "medications": ["Fluorouracil", "Cisplatin", "Imiquimod"]
-    },
-    "Low confidence": {
-        "solutions": [
-            "The image is not confidently classified.",
-            "Please upload a clearer image or consult a doctor."
-        ],
-        "medications": ["Not available due to low confidence."]
-    },
-    "Unknown": {
-        "solutions": ["No specific guidance available."],
-        "medications": ["N/A"]
-    }
-}
-
-# Logger
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Database Models
-class User(db.Model):
-    id = db.Column(db.Integer, primary_key=True)
-    name = db.Column(db.String(100), nullable=False)
-    email = db.Column(db.String(120), unique=True, nullable=False)
-    scans = db.relationship('Scan', backref='user', lazy=True)
-
-class Scan(db.Model):
-    id = db.Column(db.Integer, primary_key=True)
-    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
-    patient_name = db.Column(db.String(100), nullable=False)
-    patient_gender = db.Column(db.String(20), nullable=False)
-    patient_age = db.Column(db.Integer, nullable=False)
-    prediction = db.Column(db.String(100), nullable=False)
-    confidence = db.Column(db.String(20), nullable=False)
-    timestamp = db.Column(db.DateTime, default=datetime.utcnow)
-    image_filename = db.Column(db.String(100), nullable=False)
-
-# Load Model
-try:
-    logger.info("Loading model from %s", MODEL_PATH)
-    model = tf.keras.models.load_model(MODEL_PATH)
-except Exception as e:
-    logger.error("Failed to load model: %s", str(e))
-    raise
-
-# Plot training history
-if os.path.exists(HISTORY_PATH):
-    try:
-        with open(HISTORY_PATH, "rb") as f:
-            history_dict = pickle.load(f)
-        if "accuracy" in history_dict and "val_accuracy" in history_dict:
-            os.makedirs("/tmp/static", exist_ok=True)
-            plt.plot(history_dict['accuracy'], label='Train Accuracy')
-            plt.plot(history_dict['val_accuracy'], label='Val Accuracy')
-            plt.xlabel('Epochs')
-            plt.ylabel('Accuracy')
-            plt.title('Training History')
-            plt.legend()
-            plt.grid(True)
-            plt.savefig(PLOT_PATH)
-            plt.close()
-            logger.info("Training plot saved at %s", PLOT_PATH)
-    except Exception as e:
-        logger.error("Training history load error: %s", str(e))
-
-def preprocess_image(image_bytes):
-    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    image = image.resize(IMG_SIZE)
-    image_array = tf.keras.utils.img_to_array(image)
-    return np.expand_dims(image_array, axis=0) / 255.0
-
-def generate_pdf(report, filepath):
-    c = canvas.Canvas(filepath, pagesize=A4)
-    width, height = A4
-    y = height - 60
-
-    # Background
-    c.setFillColor(colors.Color(0.98, 0.98, 0.99, alpha=1))
-    c.rect(0, 0, width, height, fill=1, stroke=0)
-    
-    # Header background
-    c.setFillColor(colors.Color(0.94, 0.96, 0.98, alpha=1))
-    c.rect(0, height-120, width, 120, fill=1, stroke=0)
-
-    # Logo from root directory - square JPG format
-    try:
-        logo_path = "./logo.jpg"
-        if os.path.exists(logo_path):
-            c.setFillColor(colors.white)
-            c.rect(65, y-25, 50, 50, fill=1, stroke=1)
-            c.setStrokeColor(colors.Color(0.7, 0.7, 0.7, alpha=1))
-            c.setLineWidth(1)
-            c.rect(65, y-25, 50, 50, fill=0, stroke=1)
-            c.drawImage(logo_path, 67, y-23, width=46, height=46, preserveAspectRatio=True, mask='auto')
-    except Exception as e:
-        logger.warning("Logo error: %s", str(e))
-
-    # Professional title
-    c.setFont("Helvetica-Bold", 22)
-    c.setFillColor(colors.Color(0.2, 0.2, 0.2, alpha=1))
-    c.drawCentredString(width / 2, y + 5, "Medical Diagnosis Report")
-    
-    # Subtitle
-    c.setFont("Helvetica", 11)
-    c.setFillColor(colors.Color(0.5, 0.5, 0.5, alpha=1))
-    c.drawCentredString(width / 2, y - 15, "Dermatological Analysis")
-    
-    # Professional line
-    c.setStrokeColor(colors.Color(0.8, 0.8, 0.8, alpha=1))
-    c.setLineWidth(1)
-    c.line(80, y - 35, width - 80, y - 35)
-    
-    y -= 80
-
-    def professional_section_box(title, fields, extra_gap=20):
-        nonlocal y
-        
-        box_height = len(fields) * 20 + 40
-        c.setFillColor(colors.Color(0.96, 0.96, 0.96, alpha=0.3))
-        c.rect(42, y - box_height - 2, width - 84, box_height, fill=1, stroke=0)
-        
-        c.setFillColor(colors.white)
-        c.rect(40, y - box_height, width - 80, box_height, fill=1, stroke=1)
-        c.setStrokeColor(colors.Color(0.9, 0.9, 0.9, alpha=1))
-        
-        c.setFillColor(colors.Color(0.95, 0.95, 0.95, alpha=1))
-        c.rect(40, y - 30, width - 80, 30, fill=1, stroke=0)
-        
-        c.setFont("Helvetica-Bold", 12)
-        c.setFillColor(colors.Color(0.3, 0.3, 0.3, alpha=1))
-        c.drawString(55, y - 20, title)
-        
-        y -= 45
-        c.setFont("Helvetica", 10)
-        c.setFillColor(colors.Color(0.2, 0.2, 0.2, alpha=1))
-        
-        for label, val in fields.items():
-            c.setFont("Helvetica-Bold", 9)
-            c.setFillColor(colors.Color(0.4, 0.4, 0.4, alpha=1))
-            c.drawString(55, y, f"{label}:")
-            c.setFont("Helvetica", 9)
-            c.setFillColor(colors.Color(0.2, 0.2, 0.2, alpha=1))
-            c.drawString(150, y, str(val))
-            y -= 20
-        
-        y -= extra_gap
-
-    professional_section_box("Patient Information", {
-        "Name": report["name"],
-        "Email": report["email"],
-        "Gender": report["gender"],
-        "Age": f"{report['age']} years"
-    })
-
-    confidence_val = float(report["confidence"].replace('%', ''))
-    confidence_text = f"{report['confidence']} ({'High' if confidence_val > 85 else 'Moderate' if confidence_val > 70 else 'Low'} Confidence)"
-    
-    professional_section_box("Diagnostic Results", {
-        "Condition": report["prediction"],
-        "Confidence": confidence_text,
-        "Notes": report["message"] if report["message"] else "No additional notes"
-    })
-
-    disease = report["prediction"]
-    treatment = recommendations.get(disease, recommendations["Unknown"])
-
-    professional_section_box("Treatment Recommendations", {
-        f"{i+1}. {line}": "" for i, line in enumerate(treatment["solutions"])
-    })
-
-    professional_section_box("Medication Guidelines", {
-        f"{i+1}. {line}": "" for i, line in enumerate(treatment["medications"])
-    })
-
-    c.setFillColor(colors.Color(0.98, 0.98, 0.98, alpha=1))
-    c.rect(40, 40, width - 80, 70, fill=1, stroke=1)
-    c.setStrokeColor(colors.Color(0.9, 0.9, 0.9, alpha=1))
-    
-    c.setFont("Helvetica-Bold", 10)
-    c.setFillColor(colors.Color(0.4, 0.4, 0.4, alpha=1))
-    c.drawString(50, 95, "Medical Disclaimer")
-    
-    c.setFont("Helvetica", 8)
-    c.setFillColor(colors.Color(0.3, 0.3, 0.3, alpha=1))
-    disclaimer_lines = [
-        "This report is generated using AI technology for preliminary assessment purposes only.",
-        "Results should not replace professional medical consultation and diagnosis.",
-        "Please consult a qualified healthcare provider for comprehensive medical evaluation."
-    ]
-    
-    for i, line in enumerate(disclaimer_lines):
-        c.drawString(50, 80 - (i * 10), line)
-
-    c.save()
-
-@app.route("/")
-def home():
-    return redirect(url_for("form"))
-
-@app.route("/form")
-def form():
-    return render_template("form.html", history_plot="/training_plot.png")
-
-@app.route("/training_plot.png")
-def training_plot():
-    return send_file(PLOT_PATH, mimetype="image/png")
-
-@app.route("/api/history")
-def api_history():
-    user_email = request.args.get('email')
-    if not user_email:
-        return jsonify({"error": "Email parameter is required"}), 400
-
-    user = User.query.filter_by(email=user_email).first()
-    if not user:
-        return jsonify([])
-
-    scans = Scan.query.filter_by(user_id=user.id).order_by(Scan.timestamp.desc()).all()
-    
-    history_data = []
-    for scan in scans:
-        image_url = url_for('uploaded_file', filename=scan.image_filename, _external=True)
-        history_data.append({
-            "id": scan.id,
-            "prediction": scan.prediction,
-            "confidence": scan.confidence,
-            "timestamp": scan.timestamp.strftime("%B %d, %Y at %I:%M %p"),
-            "patient_name": scan.patient_name,
-            "image_url": image_url
-        })
-        
-    return jsonify(history_data)
-
-@app.route("/api/email-report/<int:scan_id>")
-def email_report(scan_id):
-    scan = Scan.query.get(scan_id)
-    if not scan:
-        return jsonify({"error": "Report not found"}), 404
-
-    try:
-        report_data = {
-            "name": scan.user.name,
-            "email": scan.user.email,
-            "gender": scan.patient_gender,
-            "age": scan.patient_age,
-            "prediction": scan.prediction,
-            "confidence": scan.confidence,
-            "message": ""
-        }
-        pdf_path = f"/tmp/report_{scan_id}.pdf"
-        generate_pdf(report_data, pdf_path)
-
-        msg = Message(
-            'Your SnapSkin Diagnostic Report',
-            sender=app.config['MAIL_USERNAME'],
-            recipients=[scan.user.email]
-        )
-        msg.body = f"Dear {scan.user.name},\n\nPlease find your requested diagnostic report attached.\n\nThank you for using SnapSkin."
-        with app.open_resource(pdf_path) as fp:
-            msg.attach(f"SnapSkin_Report_{scan_id}.pdf", "application/pdf", fp.read())
-        
-        mail.send(msg)
-        os.remove(pdf_path)
-
-        return jsonify({"success": True, "message": f"Report sent to {scan.user.email}"})
-
-    except Exception as e:
-        logger.error(f"Failed to send email for scan {scan_id}: {e}")
-        return jsonify({"success": False, "message": "Failed to send email."}), 500
-
-@app.route("/predict", methods=["POST"])
-def predict():
-    try:
-        if "image" not in request.files:
-            raise ValueError("No image uploaded.")
-        image = request.files["image"]
-        image_bytes = image.read()
-        img_array = preprocess_image(image_bytes)
-        prediction = model.predict(img_array)[0]
-        predicted_index = int(np.argmax(prediction))
-        confidence = float(prediction[predicted_index])
-        label = label_map.get(predicted_index, "Unknown") if confidence >= CONFIDENCE_THRESHOLD else "Low confidence"
-        msg = "⚠ This image is not confidently recognized. Please upload a clearer image." if confidence < CONFIDENCE_THRESHOLD else ""
-
-        # Save user and scan data
-        email = request.form.get("email")
-        user = User.query.filter_by(email=email).first()
-        if not user:
-            user = User(name=request.form.get("name"), email=email)
-            db.session.add(user)
-            db.session.commit()
-
-        # Save image
-        timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
-        image_filename = f"scan_{timestamp}.jpg"
-        image_path = os.path.join("static/uploads", image_filename)
-        os.makedirs("static/uploads", exist_ok=True)
-        image.seek(0)
-        image.save(image_path)
-
-        scan = Scan(
-            user_id=user.id,
-            patient_name=request.form.get("name"),
-            patient_gender=request.form.get("gender"),
-            patient_age=int(request.form.get("age")),
-            prediction=label,
-            confidence=f"{confidence * 100:.2f}%",
-            image_filename=image_filename
-        )
-        db.session.add(scan)
-        db.session.commit()
-
-        report = {
-            "name": request.form.get("name"),
-            "email": email,
-            "gender": request.form.get("gender"),
-            "age": request.form.get("age"),
-            "prediction": label,
-            "confidence": f"{confidence * 100:.2f}%",
-            "message": msg,
-            "scan_id": scan.id
-        }
-        session["report"] = report
-
-        # Send email automatically
-        try:
-            pdf_path = f"/tmp/report_{scan.id}.pdf"
-            generate_pdf(report, pdf_path)
-            msg = Message(
-                'Your SnapSkin Diagnostic Report',
-                sender=app.config['MAIL_USERNAME'],
-                recipients=[email]
-            )
-            msg.body = f"Dear {report['name']},\n\nPlease find your diagnostic report attached.\n\nThank you for using SnapSkin."
-            with app.open_resource(pdf_path) as fp:
-                msg.attach(f"SnapSkin_Report_{scan.id}.pdf", "application/pdf", fp.read())
-            mail.send(msg)
-            os.remove(pdf_path)
-            report["email_status"] = "Report sent to your email."
-        except Exception as e:
-            logger.error(f"Failed to send email: {e}")
-            report["email_status"] = "Failed to send report to email."
-
-        return redirect(url_for("result"))
-    except Exception as e:
-        return render_template("form.html", history_plot="/training_plot.png", result={
-            "prediction": "Error",
-            "confidence": "N/A",
-            "message": str(e),
-            "email_status": "Error occurred, no email sent."
-        })
-
-@app.route("/result")
-def result():
-    report = session.get("report", {})
-    return render_template("result.html", **report)
-
-@app.route("/download-report")
-def download_report():
-    report = session.get("report", {})
-    if not report:
-        return redirect(url_for("form"))
-    os.makedirs("/tmp/reports", exist_ok=True)
-    timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
-    filepath = f"/tmp/reports/report_{timestamp}.pdf"
-    generate_pdf(report, filepath)
-    return send_file(filepath, as_attachment=True)
-
-@app.route("/uploads/<filename>")
-def uploaded_file(filename):
-    return send_file(os.path.join("static/uploads", filename))
-
-if __name__ == "__main__":
-    with app.app_context():
-        db.create_all()
-    app.run(host="0.0.0.0", port=7860)
+   os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
+   from flask import Flask, render_template, request, redirect, url_for, session, send_file
+   from flask_sqlalchemy import SQLAlchemy
+   from flask_migrate import Migrate
+   import tensorflow as tf
+   import numpy as np
+   from PIL import Image
+   import pickle
+   import io
+   import os
+   import matplotlib.pyplot as plt
+   from reportlab.lib.pagesizes import A4
+   from reportlab.lib import colors
+   from reportlab.pdfgen import canvas
+   from reportlab.lib.units import inch
+   from datetime import datetime
+   import logging
+   from flask_mail import Mail, Message
+   from flask import jsonify, url_for
+
+   app = Flask(__name__)
+   app.secret_key = "e3f6f40bb8b2471b9f07c4025d845be9"
+
+   # Database configuration
+   app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/snapsin.db'
+   app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
+   db = SQLAlchemy(app)
+   migrate = Migrate(app, db)
+
+   # Mail configuration
+   app.config['MAIL_SERVER'] = 'smtp.gmail.com'
+   app.config['MAIL_PORT'] = 465
+   app.config['MAIL_USERNAME'] = os.environ.get('MAIL_USERNAME')
+   app.config['MAIL_PASSWORD'] = os.environ.get('MAIL_PASSWORD')
+   app.config['MAIL_USE_TLS'] = False
+   app.config['MAIL_USE_SSL'] = True
+   mail = Mail(app)
+
+   MODEL_PATH = "skin_lesion_model.h5"
+   HISTORY_PATH = "training_history.pkl"
+   PLOT_PATH = "/tmp/static/training_plot.png"
+   LOGO_PATH = "static/logo.jpg"
+   IMG_SIZE = (224, 224)
+   CONFIDENCE_THRESHOLD = 0.30
+
+   label_map = {
+       0: "Melanoma",
+       1: "Melanocytic nevus",
+       2: "Basal cell carcinoma",
+       3: "Actinic keratosis",
+       4: "Benign keratosis",
+       5: "Dermatofibroma",
+       6: "Vascular lesion",
+       7: "Squamous cell carcinoma"
+   }
+
+   recommendations = {
+       "Melanoma": {
+           "solutions": [
+               "Consult a dermatologist immediately.",
+               "Surgical removal is typically required.",
+               "Regular follow-up and screening for metastasis."
+           ],
+           "medications": ["Interferon alfa-2b", "Vemurafenib", "Dacarbazine"]
+       },
+       "Melanocytic nevus": {
+           "solutions": [
+               "Usually benign and requires no treatment.",
+               "Monitor for any change in shape or color."
+           ],
+           "medications": ["No medication necessary unless changes occur."]
+       },
+       "Basal cell carcinoma": {
+           "solutions": [
+               "Surgical excision or Mohs surgery.",
+               "Topical treatments if superficial.",
+               "Radiation in select cases."
+           ],
+           "medications": ["Imiquimod cream", "Fluorouracil cream", "Vismodegib"]
+       },
+       "Actinic keratosis": {
+           "solutions": [
+               "Cryotherapy or topical treatments.",
+               "Avoid prolonged sun exposure.",
+               "Use of sunscreen regularly."
+           ],
+           "medications": ["Fluorouracil", "Imiquimod", "Diclofenac gel"]
+       },
+       "Benign keratosis": {
+           "solutions": [
+               "Generally harmless and often left untreated.",
+               "Can be removed for cosmetic reasons."
+           ],
+           "medications": ["No medication required unless infected."]
+       },
+       "Dermatofibroma": {
+           "solutions": [
+               "Benign skin growth, no treatment needed.",
+               "Surgical removal if painful or for cosmetic reasons."
+           ],
+           "medications": ["No medication needed."]
+       },
+       "Vascular lesion": {
+           "solutions": [
+               "Treatment depends on type (e.g., hemangioma).",
+               "Laser therapy is commonly used.",
+               "Observation if no complications."
+           ],
+           "medications": ["Beta-blockers (e.g., propranolol for hemangioma)"]
+       },
+       "Squamous cell carcinoma": {
+           "solutions": [
+               "Surgical removal is standard.",
+               "Follow-up for recurrence or metastasis.",
+               "Avoid sun exposure and use sunscreen."
+           ],
+           "medications": ["Fluorouracil", "Cisplatin", "Imiquimod"]
+       },
+       "Low confidence": {
+           "solutions": [
+               "The image is not confidently classified.",
+               "Please upload a clearer image or consult a doctor."
+           ],
+           "medications": ["Not available due to low confidence."]
+       },
+       "Unknown": {
+           "solutions": ["No specific guidance available."],
+           "medications": ["N/A"]
+       }
+   }
+
+   # Logger
+   logging.basicConfig(level=logging.INFO)
+   logger = logging.getLogger(__name__)
+
+   # Database Models
+   class User(db.Model):
+       id = db.Column(db.Integer, primary_key=True)
+       name = db.Column(db.String(100), nullable=False)
+       email = db.Column(db.String(120), unique=True, nullable=False)
+       scans = db.relationship('Scan', backref='user', lazy=True)
+
+   class Scan(db.Model):
+       id = db.Column(db.Integer, primary_key=True)
+       user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
+       patient_name = db.Column(db.String(100), nullable=False)
+       patient_gender = db.Column(db.String(20), nullable=False)
+       patient_age = db.Column(db.Integer, nullable=False)
+       prediction = db.Column(db.String(100), nullable=False)
+       confidence = db.Column(db.String(20), nullable=False)
+       timestamp = db.Column(db.DateTime, default=datetime.utcnow)
+       image_filename = db.Column(db.String(100), nullable=False)
+
+   # Load Model
+   model = None
+   try:
+       if os.path.exists(MODEL_PATH):
+           logger.info("Loading model from %s", MODEL_PATH)
+           model = tf.keras.models.load_model(MODEL_PATH)
+       else:
+           logger.error("Model file %s not found", MODEL_PATH)
+           raise FileNotFoundError(f"Model file {MODEL_PATH} not found")
+   except Exception as e:
+       logger.error("Failed to load model: %s", str(e))
+       raise
+
+   # Plot training history
+   if os.path.exists(HISTORY_PATH):
+       try:
+           with open(HISTORY_PATH, "rb") as f:
+               history_dict = pickle.load(f)
+           if "accuracy" in history_dict and "val_accuracy" in history_dict:
+               os.makedirs("/tmp/static", exist_ok=True)
+               plt.plot(history_dict['accuracy'], label='Train Accuracy')
+               plt.plot(history_dict['val_accuracy'], label='Val Accuracy')
+               plt.xlabel('Epochs')
+               plt.ylabel('Accuracy')
+               plt.title('Training History')
+               plt.legend()
+               plt.grid(True)
+               plt.savefig(PLOT_PATH)
+               plt.close()
+               logger.info("Training plot saved at %s", PLOT_PATH)
+           else:
+               logger.warning("Invalid training history data in %s", HISTORY_PATH)
+       except Exception as e:
+           logger.warning("Training history load error: %s", str(e))
+   else:
+       logger.warning("Training history file %s not found", HISTORY_PATH)
+
+   def preprocess_image(image_bytes):
+       try:
+           image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+           image = image.resize(IMG_SIZE)
+           image_array = tf.keras.utils.img_to_array(image)
+           return np.expand_dims(image_array, axis=0) / 255.0
+       except Exception as e:
+           logger.error("Image preprocessing error: %s", str(e))
+           raise
+
+   def generate_pdf(report, filepath):
+       try:
+           c = canvas.Canvas(filepath, pagesize=A4)
+           width, height = A4
+           y = height - 60
+
+           # Background
+           c.setFillColor(colors.Color(0.98, 0.98, 0.99, alpha=1))
+           c.rect(0, 0, width, height, fill=1, stroke=0)
+           
+           # Header background
+           c.setFillColor(colors.Color(0.94, 0.96, 0.98, alpha=1))
+           c.rect(0, height-120, width, 120, fill=1, stroke=0)
+
+           # Logo from root directory - square JPG format
+           try:
+               if os.path.exists(LOGO_PATH):
+                   c.setFillColor(colors.white)
+                   c.rect(65, y-25, 50, 50, fill=1, stroke=1)
+                   c.setStrokeColor(colors.Color(0.7, 0.7, 0.7, alpha=1))
+                   c.setLineWidth(1)
+                   c.rect(65, y-25, 50, 50, fill=0, stroke=1)
+                   c.drawImage(LOGO_PATH, 67, y-23, width=46, height=46, preserveAspectRatio=True, mask='auto')
+               else:
+                   logger.warning("Logo file %s not found, skipping logo", LOGO_PATH)
+           except Exception as e:
+               logger.warning("Logo error: %s", str(e))
+
+           # Professional title
+           c.setFont("Helvetica-Bold", 22)
+           c.setFillColor(colors.Color(0.2, 0.2, 0.2, alpha=1))
+           c.drawCentredString(width / 2, y + 5, "Medical Diagnosis Report")
+           
+           # Subtitle
+           c.setFont("Helvetica", 11)
+           c.setFillColor(colors.Color(0.5, 0.5, 0.5, alpha=1))
+           c.drawCentredString(width / 2, y - 15, "Dermatological Analysis")
+           
+           # Professional line
+           c.setStrokeColor(colors.Color(0.8, 0.8, 0.8, alpha=1))
+           c.setLineWidth(1)
+           c.line(80, y - 35, width - 80, y - 35)
+           
+           y -= 80
+
+           def professional_section_box(title, fields, extra_gap=20):
+               nonlocal y
+               
+               box_height = len(fields) * 20 + 40
+               c.setFillColor(colors.Color(0.96, 0.96, 0.96, alpha=0.3))
+               c.rect(42, y - box_height - 2, width - 84, box_height, fill=1, stroke=0)
+               
+               c.setFillColor(colors.white)
+               c.rect(40, y - box_height, width - 80, box_height, fill=1, stroke=1)
+               c.setStrokeColor(colors.Color(0.9, 0.9, 0.9, alpha=1))
+               
+               c.setFillColor(colors.Color(0.95, 0.95, 0.95, alpha=1))
+               c.rect(40, y - 30, width - 80, 30, fill=1, stroke=0)
+               
+               c.setFont("Helvetica-Bold", 12)
+               c.setFillColor(colors.Color(0.3, 0.3, 0.3, alpha=1))
+               c.drawString(55, y - 20, title)
+               
+               y -= 45
+               c.setFont("Helvetica", 10)
+               c.setFillColor(colors.Color(0.2, 0.2, 0.2, alpha=1))
+               
+               for label, val in fields.items():
+                   c.setFont("Helvetica-Bold", 9)
+                   c.setFillColor(colors.Color(0.4, 0.4, 0.4, alpha=1))
+                   c.drawString(55, y, f"{label}:")
+                   c.setFont("Helvetica", 9)
+                   c.setFillColor(colors.Color(0.2, 0.2, 0.2, alpha=1))
+                   c.drawString(150, y, str(val))
+                   y -= 20
+               
+               y -= extra_gap
+
+           professional_section_box("Patient Information", {
+               "Name": report["name"],
+               "Email": report["email"],
+               "Gender": report["gender"],
+               "Age": f"{report['age']} years"
+           })
+
+           confidence_val = float(report["confidence"].replace('%', ''))
+           confidence_text = f"{report['confidence']} ({'High' if confidence_val > 85 else 'Moderate' if confidence_val > 70 else 'Low'} Confidence)"
+           
+           professional_section_box("Diagnostic Results", {
+               "Condition": report["prediction"],
+               "Confidence": confidence_text,
+               "Notes": report["message"] if report["message"] else "No additional notes"
+           })
+
+           disease = report["prediction"]
+           treatment = recommendations.get(disease, recommendations["Unknown"])
+
+           professional_section_box("Treatment Recommendations", {
+               f"{i+1}. {line}": "" for i, line in enumerate(treatment["solutions"])
+           })
+
+           professional_section_box("Medication Guidelines", {
+               f"{i+1}. {line}": "" for i, line in enumerate(treatment["medications"])
+           })
+
+           c.setFillColor(colors.Color(0.98, 0.98, 0.98, alpha=1))
+           c.rect(40, 40, width - 80, 70, fill=1, stroke=1)
+           c.setStrokeColor(colors.Color(0.9, 0.9, 0.9, alpha=1))
+           
+           c.setFont("Helvetica-Bold", 10)
+           c.setFillColor(colors.Color(0.4, 0.4, 0.4, alpha=1))
+           c.drawString(50, 95, "Medical Disclaimer")
+           
+           c.setFont("Helvetica", 8)
+           c.setFillColor(colors.Color(0.3, 0.3, 0.3, alpha=1))
+           disclaimer_lines = [
+               "This report is generated using AI technology for preliminary assessment purposes only.",
+               "Results should not replace professional medical consultation and diagnosis.",
+               "Please consult a qualified healthcare provider for comprehensive medical evaluation."
+           ]
+           
+           for i, line in enumerate(disclaimer_lines):
+               c.drawString(50, 80 - (i * 10), line)
+
+           c.save()
+       except Exception as e:
+           logger.error("PDF generation error: %s", str(e))
+           raise
+
+   @app.route("/")
+   def home():
+       return redirect(url_for("form"))
+
+   @app.route("/form")
+   def form():
+       try:
+           return render_template("form.html", history_plot="/training_plot.png")
+       except Exception as e:
+           logger.error("Error rendering form: %s", str(e))
+           return render_template("form.html", history_plot=None, result={
+               "prediction": "Error",
+               "confidence": "N/A",
+               "message": "Failed to load form. Please try again.",
+               "email_status": "N/A"
+           })
+
+   @app.route("/training_plot.png")
+   def training_plot():
+       try:
+           if os.path.exists(PLOT_PATH):
+               return send_file(PLOT_PATH, mimetype="image/png")
+           else:
+               logger.warning("Training plot %s not found", PLOT_PATH)
+               return "", 404
+       except Exception as e:
+           logger.error("Error serving training plot: %s", str(e))
+           return "", 500
+
+   @app.route("/api/history")
+   def api_history():
+       try:
+           user_email = request.args.get('email')
+           if not user_email:
+               return jsonify({"error": "Email parameter is required"}), 400
+
+           user = User.query.filter_by(email=user_email).first()
+           if not user:
+               return jsonify([])
+
+           scans = Scan.query.filter_by(user_id=user.id).order_by(Scan.timestamp.desc()).all()
+           
+           history_data = []
+           for scan in scans:
+               image_url = url_for('uploaded_file', filename=scan.image_filename, _external=True)
+               history_data.append({
+                   "id": scan.id,
+                   "prediction": scan.prediction,
+                   "confidence": scan.confidence,
+                   "timestamp": scan.timestamp.strftime("%B %d, %Y at %I:%M %p"),
+                   "patient_name": scan.patient_name,
+                   "image_url": image_url
+               })
+           
+           return jsonify(history_data)
+       except Exception as e:
+           logger.error("Error in history API: %s", str(e))
+           return jsonify({"error": "Internal server error"}), 500
+
+   @app.route("/api/email-report/<int:scan_id>")
+   def email_report(scan_id):
+       try:
+           scan = Scan.query.get(scan_id)
+           if not scan:
+               return jsonify({"error": "Report not found"}), 404
+
+           report_data = {
+               "name": scan.user.name,
+               "email": scan.user.email,
+               "gender": scan.patient_gender,
+               "age": scan.patient_age,
+               "prediction": scan.prediction,
+               "confidence": scan.confidence,
+               "message": ""
+           }
+           pdf_path = f"/tmp/report_{scan_id}.pdf"
+           generate_pdf(report_data, pdf_path)
+
+           msg = Message(
+               'Your SnapSkin Diagnostic Report',
+               sender=app.config['MAIL_USERNAME'],
+               recipients=[scan.user.email]
+           )
+           msg.body = f"Dear {scan.user.name},\n\nPlease find your requested diagnostic report attached.\n\nThank you for using SnapSkin."
+           with app.open_resource(pdf_path) as fp:
+               msg.attach(f"SnapSkin_Report_{scan_id}.pdf", "application/pdf", fp.read())
+           
+           mail.send(msg)
+           os.remove(pdf_path)
+
+           return jsonify({"success": True, "message": f"Report sent to {scan.user.email}"})
+       except Exception as e:
+           logger.error(f"Failed to send email for scan {scan_id}: {e}")
+           return jsonify({"success": False, "message": "Failed to send email."}), 500
+
+   @app.route("/predict", methods=["POST"])
+   def predict():
+       try:
+           if "image" not in request.files:
+               raise ValueError("No image uploaded.")
+           image = request.files["image"]
+           image_bytes = image.read()
+           img_array = preprocess_image(image_bytes)
+           prediction = model.predict(img_array)[0]
+           predicted_index = int(np.argmax(prediction))
+           confidence = float(prediction[predicted_index])
+           label = label_map.get(predicted_index, "Unknown") if confidence >= CONFIDENCE_THRESHOLD else "Low confidence"
+           msg = "⚠ This image is not confidently recognized. Please upload a clearer image." if confidence < CONFIDENCE_THRESHOLD else ""
+
+           # Save user and scan data
+           email = request.form.get("email")
+           user = User.query.filter_by(email=email).first()
+           if not user:
+               user = User(name=request.form.get("name"), email=email)
+               db.session.add(user)
+               db.session.commit()
+
+           # Save image
+           timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
+           image_filename = f"scan_{timestamp}.jpg"
+           image_path = os.path.join("static/uploads", image_filename)
+           os.makedirs("static/uploads", exist_ok=True)
+           image.seek(0)
+           image.save(image_path)
+
+           scan = Scan(
+               user_id=user.id,
+               patient_name=request.form.get("name"),
+               patient_gender=request.form.get("gender"),
+               patient_age=int(request.form.get("age")),
+               prediction=label,
+               confidence=f"{confidence * 100:.2f}%",
+               image_filename=image_filename
+           )
+           db.session.add(scan)
+           db.session.commit()
+
+           report = {
+               "name": request.form.get("name"),
+               "email": email,
+               "gender": request.form.get("gender"),
+               "age": request.form.get("age"),
+               "prediction": label,
+               "confidence": f"{confidence * 100:.2f}%",
+               "message": msg,
+               "scan_id": scan.id
+           }
+           session["report"] = report
+
+           # Send email automatically
+           try:
+               pdf_path = f"/tmp/report_{scan.id}.pdf"
+               generate_pdf(report, pdf_path)
+               msg = Message(
+                   'Your SnapSkin Diagnostic Report',
+                   sender=app.config['MAIL_USERNAME'],
+                   recipients=[email]
+               )
+               msg.body = f"Dear {report['name']},\n\nPlease find your diagnostic report attached.\n\nThank you for using SnapSkin."
+               with app.open_resource(pdf_path) as fp:
+                   msg.attach(f"SnapSkin_Report_{scan.id}.pdf", "application/pdf", fp.read())
+               mail.send(msg)
+               os.remove(pdf_path)
+               report["email_status"] = "Report sent to your email."
+           except Exception as e:
+               logger.error(f"Failed to send email: {e}")
+               report["email_status"] = "Failed to send report to email."
+
+           return redirect(url_for("result"))
+       except Exception as e:
+           logger.error("Prediction error: %s", str(e))
+           return render_template("form.html", history_plot="/training_plot.png", result={
+               "prediction": "Error",
+               "confidence": "N/A",
+               "message": str(e),
+               "email_status": "Error occurred, no email sent."
+           })
+
+   @app.route("/result")
+   def result():
+       try:
+           report = session.get("report", {})
+           return render_template("result.html", **report)
+       except Exception as e:
+           logger.error("Error rendering result: %s", str(e))
+           return render_template("form.html", history_plot="/training_plot.png", result={
+               "prediction": "Error",
+               "confidence": "N/A",
+               "message": "Failed to load result. Please try again.",
+               "email_status": "N/A"
+           })
+
+   @app.route("/download-report")
+   def download_report():
+       try:
+           report = session.get("report", {})
+           if not report:
+               return redirect(url_for("form"))
+           os.makedirs("/tmp/reports", exist_ok=True)
+           timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
+           filepath = f"/tmp/reports/report_{timestamp}.pdf"
+           generate_pdf(report, filepath)
+           return send_file(filepath, as_attachment=True)
+       except Exception as e:
+           logger.error("Download report error: %s", str(e))
+           return redirect(url_for("form"))
+
+   @app.route("/uploads/<filename>")
+   def uploaded_file(filename):
+       try:
+           file_path = os.path.join("static/uploads", filename)
+           if os.path.exists(file_path):
+               return send_file(file_path)
+           else:
+               logger.warning("Image file %s not found", file_path)
+               return "", 404
+       except Exception as e:
+           logger.error("Error serving uploaded file: %s", str(e))
+           return "", 500
+
+   if __name__ == "__main__":
+       try:
+           with app.app_context():
+               db.create_all()
+           app.run(host="0.0.0.0", port=7860)
+       except Exception as e:
+           logger.error("Application startup error: %s", str(e))
+           raise