Update app.py

app.py

@@ -1,149 +1,18 @@
+from flask import Flask, send_file
 import gradio as gr
-import tensorflow as tf
-import numpy as np
-from tensorflow.keras.preprocessing import image
-from PIL import Image, ImageDraw
-from reportlab.lib.pagesizes import letter
-from reportlab.pdfgen import canvas
-import os
 
-# Load the trained model
-model = tf.keras.models.load_model("my_keras_model.h5")
+app = Flask(__name__)
 
-# Define image size
-image_size = (224, 224)
+# Route to serve HTML file
+@app.route("/")
+def serve_html():
+    return send_file("re.html")
 
-# Function to detect fracture
-def detect_fracture(xray):
-    img = Image.open(xray).convert("RGB").resize(image_size)
-    img_array = image.img_to_array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
+# Run Gradio separately
+def run_gradio():
+    gr.Interface(lambda: "Gradio App Running!", inputs=[], outputs="text").launch(share=True)
 
-    prediction = model.predict(img_array)[0][0]
-    bbox = (50, 50, 150, 150) if prediction > 0.5 else None  # Placeholder
-    return prediction, bbox, img
-
-# Function to analyze severity
-def analyze_injury(prediction):
-    if prediction < 0.3:
-        severity = "Mild"
-        treatment = "Rest, pain relievers, and follow-up X-ray."
-        gov_cost = "₹2,000 - ₹5,000"
-        private_cost = "₹10,000 - ₹20,000"
-    elif 0.3 <= prediction < 0.7:
-        severity = "Moderate"
-        treatment = "Plaster cast or splint; possible minor surgery."
-        gov_cost = "₹8,000 - ₹15,000"
-        private_cost = "₹30,000 - ₹60,000"
-    else:
-        severity = "Severe"
-        treatment = "Major surgery with metal implants, extensive physiotherapy."
-        gov_cost = "₹20,000 - ₹50,000"
-        private_cost = "₹1,00,000+"
-
-    return severity, treatment, gov_cost, private_cost
-
-# Function to generate report
-def generate_report(name, age, gender, xray1, xray2):
-    prediction1, bbox1, img1 = detect_fracture(xray1)
-    prediction2, bbox2, img2 = detect_fracture(xray2)
-
-    avg_prediction = (prediction1 + prediction2) / 2
-    diagnosed_class = "Fractured" if avg_prediction > 0.5 else "Normal"
-    severity, treatment, gov_cost, private_cost = analyze_injury(avg_prediction)
-
-    # Draw bounding box if fracture detected
-    if bbox1:
-        draw = ImageDraw.Draw(img1)
-        draw.rectangle(bbox1, outline="red", width=5)
-
-    if bbox2:
-        draw = ImageDraw.Draw(img2)
-        draw.rectangle(bbox2, outline="red", width=5)
-
-    # Save images
-    img1_path = f"{name}_xray1.png"
-    img2_path = f"{name}_xray2.png"
-    img1.save(img1_path)
-    img2.save(img2_path)
-
-    # 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, img1_path, img2_path, diagnosed_class, severity, treatment, gov_cost, private_cost
-
-# UI Components
-with gr.Blocks() as app:
-    gr.Markdown("# 🏥 Bone Fracture Detection & Medical Report")
-    gr.Markdown(
-        "### A radiologist is a doctor who specializes in reading medical images like X-rays, MRIs, and CT scans to diagnose diseases and injuries."
-    )
-    gr.Image("x.jpg", label="X-Ray Example")
-    gr.Markdown(
-        """
-        ## **Understanding Bone Fractures**
-        - **Closed (Simple):** Bone doesn't pierce skin.
-        - **Open (Compound):** Bone breaks skin.
-        - **Hairline:** Small stress fracture.
-        - **Comminuted:** Bone shatters into pieces.
-        - **Avulsion:** Tendon pulls bone fragment.
-        - **Compression:** Bones forced together.
-        """
-    )
-    gr.Markdown(
-        """
-        ## **First Aid**
-        - Immobilize the injured area.
-        - Control bleeding, cover wounds.
-        - Don't straighten broken bones.
-        - Use splints, slings for support.
-        - Apply cold packs.
-        - Seek emergency help.
-        """
-    )
-
-    with gr.Row():
-        gr.Markdown("## 📝 Patient Information Form")
-
-    with gr.Column():
-        name = gr.Textbox(label="Patient Name")
-        age = gr.Number(label="Age")
-        gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
-
-    with gr.Column():
-        xray1 = gr.Image(type="file", label="Upload X-ray Image 1")
-        xray2 = gr.Image(type="file", label="Upload X-ray Image 2")
-
-    submit_button = gr.Button("Generate Report")
-
-    output_file = gr.File(label="Download Report")
-    xray1_output = gr.Image(label="X-ray 1 with Fracture Highlight")
-    xray2_output = gr.Image(label="X-ray 2 with Fracture Highlight")
-    diagnosis_output = gr.Textbox(label="Fracture Detected")
-    severity_output = gr.Textbox(label="Injury Severity")
-    treatment_output = gr.Textbox(label="Recommended Treatment")
-    gov_cost_output = gr.Textbox(label="Estimated Cost (Govt Hospital)")
-    private_cost_output = gr.Textbox(label="Estimated Cost (Private Hospital)")
-
-    submit_button.click(
-        generate_report,
-        inputs=[name, age, gender, xray1, xray2],
-        outputs=[output_file, xray1_output, xray2_output, diagnosis_output, severity_output, treatment_output, gov_cost_output, private_cost_output],
-    )
-
-# Run app
 if __name__ == "__main__":
-    app.launch()
+    from threading import Thread
+    Thread(target=run_gradio).start()
+    app.run(host="0.0.0.0", port=7860)