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Yash goyal commited on Sep 21, 2025

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70996b2

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1 Parent(s): 82107dd

Update app.py

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app.py +490 -192

app.py CHANGED Viewed

@@ -1,195 +1,493 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
-  <title>SnapSkin</title>
-  <link rel="stylesheet" href="/static/form-styles.css" />
-  <link rel="shortcut icon" href="/static/logo.png" />
-  <link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500;700&display=swap" rel="stylesheet"/>
-  <script src="/static/preloader.js"></script>
-  <script src="/static/cursor-effect.js"></script>
-  <script defer>
-    document.addEventListener("DOMContentLoaded", () => {
-      const fileInput = document.getElementById("image");
-      const fileMessage = document.getElementById("upload-message");
-      const submitBtn = document.getElementById("submit-btn");
-      const emailBtn = document.getElementById("email-report-btn");
-      fileInput.addEventListener("change", () => {
-        const file = fileInput.files[0];
-        fileMessage.style.display = "block";
-        if (!file) {
-          fileMessage.textContent = "No file selected.";
-          fileMessage.style.color = "orange";
-          submitBtn.disabled = true;
-          return;
         }
-        const allowedTypes = ["image/jpeg", "image/png"];
-        const maxSize = 20 * 1024 * 1024; // 20MB
-        if (!allowedTypes.includes(file.type)) {
-          fileMessage.textContent = "Invalid file format. Use JPG or PNG.";
-          fileMessage.style.color = "red";
-          submitBtn.disabled = true;
-        } else if (file.size > maxSize) {
-          fileMessage.textContent = "File too large. Max 20MB allowed.";
-          fileMessage.style.color = "red";
-          submitBtn.disabled = true;
-        } else {
-          fileMessage.textContent = "✅ File ready for upload.";
-          fileMessage.style.color = "limegreen";
-          submitBtn.disabled = false;
         }
-      });
-      if (emailBtn) {
-        emailBtn.addEventListener("click", async () => {
-          const scanId = emailBtn.dataset.scanId;
-          const emailStatus = document.getElementById("email-status");
-          try {
-            const response = await fetch(`/api/email-report/${scanId}`);
-            const data = await response.json();
-            emailStatus.textContent = data.message;
-            emailStatus.style.color = data.success ? "limegreen" : "red";
-          } catch (error) {
-            emailStatus.textContent = "Error sending email.";
-            emailStatus.style.color = "red";
-          }
-        });
-      }
-    });
-  </script>
-</head>
-<body>
-  <div class="preloader">
-    <div class="preloader-particles"></div>
-    <div class="dna-loader">
-      <div class="dna-loader-strand"></div>
-      <div class="dna-loader-strand"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="dna-loader-rung"></div>
-      <div class="loader-text">LOADING...</div>
-    </div>
-  </div>
-  <header>
-    <nav>
-      <div class="logo" style="color: beige;">SNAP<span style="color: #00ffff;">SKIN</span></div>
-      <ul>
-        <li><a href="https://snapskin.vercel.app/">Home</a></li>
-      </ul>
-    </nav>
-  </header>
-  <section class="form-section">
-    <div class="container">
-      <div class="form-container">
-        <h1 style="color: white;">ENTER PATIENT DETAILS</h1>
-        <p class="intro-text">
-          Fill out the form below for the diagnosis of your skin disease.
-          Our AI will process your image and generate a predicted report for you.
-        </p>
-        <div class="form-wrapper">
-          <form id="patient-form" action="/predict" method="POST" enctype="multipart/form-data">
-            <div class="form-group">
-              <label for="name">Full Name</label>
-              <input type="text" id="name" name="name" placeholder="Enter your name" required />
-            </div>
-            <div class="form-group">
-              <label for="email">Email Address</label>
-              <input type="email" id="email" name="email" placeholder="Enter your email address" required />
-            </div>
-            <div class="form-row">
-              <div class="form-group half">
-                <label for="gender">Gender</label>
-                <select id="gender" name="gender" required>
-                  <option value="" disabled selected>Select gender</option>
-                  <option value="male">Male</option>
-                  <option value="female">Female</option>
-                  <option value="other">Other</option>
-                </select>
-              </div>
-              <div class="form-group half">
-                <label for="age">Age</label>
-                <input type="number" id="age" name="age" placeholder="Enter your age" min="1" max="120" required />
-              </div>
-            </div>
-            <div class="form-group file-upload-group">
-              <label for="image">Upload Image</label>
-              <div class="file-upload-wrapper">
-                <input type="file" id="image" name="image" accept=".jpg,.jpeg,.png" required />
-                <span class="file-upload-text">Choose Image</span>
-              </div>
-              <small class="warning">Allowed: JPG, PNG | Max size: 20MB</small>
-              <div class="upload-message" id="upload-message" style="display: none;"></div>
-            </div>
-            <div class="form-group consent-group">
-              <input type="checkbox" id="consent" name="consent" required />
-              <label for="consent">I agree to the <a href="#">Terms</a> and <a href="#">Privacy Policy</a></label>
-            </div>
-            <div class="form-actions">
-              <button type="submit" class="submit-button" id="submit-btn">Submit Details</button>
-            </div>
-          </form>
-          <!-- Result Section -->
-          <div class="result-section" id="result-section" style="margin-top: 30px; {% if result %}display: block;{% else %}display: none;{% endif %}">
-            <h2>Diagnosis Result</h2>
-            <div id="result-content">
-              {% if result %}
-                <p><strong>Prediction:</strong> {{ result.prediction }}</p>
-                <p><strong>Confidence:</strong> {{ result.confidence }}</p>
-                {% if result.message %}
-                  <p class="warning-message">{{ result.message }}</p>
-                {% endif %}
-                <p id="email-status" style="color: {% if 'Failed' in result.email_status %}red{% else %}limegreen{% endif %}">{{ result.email_status }}</p>
-                <button id="email-report-btn" class="submit-button" data-scan-id="{{ result.scan_id }}">Resend Report to Email</button>
-              {% endif %}
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  </section>
-  <footer>
-    <div class="container">
-      <div class="footer-content">
-        <div class="footer-logo">SNAP<span>SKIN</span></div>
-        <div class="footer-links">
-          <h3>Quick Links</h3>
-          <ul>
-            <li><a href="/form">Home</a></li>
-            <li><a href="#">About</a></li>
-            <li><a href="#">Features</a></li>
-          </ul>
-        </div>
-        <div class="footer-contact">
-          <h3>Support</h3>
-          <p>Email: info.snapskin@gmail.com</p>
-          <p>Phone: 7817833974 / 7983595318</p>
-        </div>
-      </div>
-      <div class="copyright">
-        <p>© 2025 SnapSkin. All rights reserved.</p>
-      </div>
-    </div>
-  </footer>
-</body>
-</html>

+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)