Delete flask_app.py

flask_app.py

@@ -1,78 +0,0 @@
-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