Create flask_app.py

flask_app.py

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+from flask import Flask, render_template, request, send_file
+import os
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+
+# Load the trained model
+model = tf.keras.models.load_model("my_keras_model.h5")
+
+app = Flask(__name__, template_folder="templates", static_folder="static")
+
+# Function to process X-rays and generate a PDF report
+def generate_report(name, age, gender, xray1, xray2):
+    image_size = (224, 224)
+
+    def predict_fracture(xray):
+        img = Image.open(xray).resize(image_size)
+        img_array = image.img_to_array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        prediction = model.predict(img_array)[0][0]
+        return prediction
+
+    # Predict on both X-rays
+    prediction1 = predict_fracture(xray1)
+    prediction2 = predict_fracture(xray2)
+    avg_prediction = (prediction1 + prediction2) / 2
+    diagnosed_class = "Fractured" if avg_prediction > 0.5 else "Normal"
+
+    # Injury severity classification
+    severity = "Mild" if avg_prediction < 0.3 else "Moderate" if avg_prediction < 0.7 else "Severe"
+    treatment = {
+        "Mild": "Rest, pain relievers, follow-up X-ray.",
+        "Moderate": "Plaster cast, possible minor surgery.",
+        "Severe": "Major surgery, metal implants, physiotherapy."
+    }[severity]
+    gov_cost = {"Mild": "₹2,000 - ₹5,000", "Moderate": "₹8,000 - ₹15,000", "Severe": "₹20,000 - ₹50,000"}[severity]
+    private_cost = {"Mild": "₹10,000 - ₹20,000", "Moderate": "₹30,000 - ₹60,000", "Severe": "₹1,00,000+"}[severity]
+
+    # Generate PDF report
+    report_path = f"{name}_fracture_report.pdf"
+    c = canvas.Canvas(report_path, pagesize=letter)
+    c.setFont("Helvetica", 12)
+    c.drawString(100, 750, f"Patient Name: {name}")
+    c.drawString(100, 730, f"Age: {age}")
+    c.drawString(100, 710, f"Gender: {gender}")
+    c.drawString(100, 690, f"Diagnosis: {diagnosed_class}")
+    c.drawString(100, 670, f"Injury Severity: {severity}")
+    c.drawString(100, 650, f"Recommended Treatment: {treatment}")
+    c.drawString(100, 630, f"Estimated Cost (Govt Hospital): {gov_cost}")
+    c.drawString(100, 610, f"Estimated Cost (Private Hospital): {private_cost}")
+    c.save()
+
+    return report_path  # Return path for auto-download
+
+# Flask Route: Serve HTML Page
+@app.route("/")
+def home():
+    return render_template("re.html")
+
+# Flask Route: Handle Form Submission
+@app.route("/submit_report", methods=["POST"])
+def submit_report():
+    name = request.form["first_name"] + " " + request.form["surname"]
+    age = request.form["age"]
+    gender = request.form["gender"]
+    xray1 = request.files["xray_side"]
+    xray2 = request.files["xray_top"]
+
+    # Generate PDF report
+    pdf_path = generate_report(name, age, gender, xray1, xray2)
+
+    return send_file(pdf_path, as_attachment=True)  # Auto-download report
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=False)  # Run Flask on 7860