Update app.py

app.py

@@ -1,203 +1,462 @@
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Force TensorFlow to use CPU
-
-import gradio as gr
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import HTMLResponse
+from transformers import pipeline
+from PIL import Image, ImageDraw
 import numpy as np
-from PIL import Image
-from reportlab.lib.pagesizes import letter
-from reportlab.pdfgen import canvas
-from reportlab.lib import colors
-from reportlab.platypus import Table, TableStyle
-import requests
-import smtplib
-from email.mime.multipart import MIMEMultipart
-from email.mime.text import MIMEText
-from email.mime.base import MIMEBase
-from email import encoders
 import io
+import uvicorn
 import base64
-
-# FastAPI server URL
-FASTAPI_URL = "http://localhost:8000/analyze"  # Updated to the default FastAPI port
-
-# Email credentials
-SENDER_EMAIL = "[email protected]"
-SENDER_PASSWORD = "1w3r5y7i9pW$"
-
-# Function to process X-ray and generate a PDF report
-def generate_report(name, age, gender, weight, height, allergies, cause, xray, email):
-    image_size = (224, 224)
-
-    # Send X-ray to FastAPI for analysis
-    try:
-        with open(xray, 'rb') as f:
-            files = {'file': (os.path.basename(xray), f)}
-            response = requests.post(FASTAPI_URL, files=files)
-            response.raise_for_status()  # Raise an exception for bad status codes
-            fastapi_results_html = response.text
-    except requests.exceptions.RequestException as e:
-        return f"Error connecting to FastAPI server: {e}"
-
-    # Extract prediction from FastAPI response (you might need to adjust this based on the exact HTML structure)
-    diagnosed_class = "normal"
-    severity = "Not Available"
+import random
+
+app = FastAPI()
+
+# Loading the models
+def load_models():
+    return {
+        "BoneEye": pipeline("object-detection", model="D3STRON/bone-fracture-detr"),
+        "BoneGuard": pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray"),
+        "XRayMaster": pipeline("image-classification",
+            model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388")
+    }
+
+models = load_models()
+
+def translate_label(label):
+    translations = {
+        "fracture": "Fracture",
+        "no fracture": "No Fracture",
+        "normal": "Normal",
+        "abnormal": "Abnormal",
+        "F1": "Fracture",
+        "NF": "No Fracture"
+    }
+    return translations.get(label.lower(), label)
+
+def create_heatmap_overlay(image, box, score):
+    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
+    draw = ImageDraw.Draw(overlay)
+
+    x1, y1 = box['xmin'], box['ymin']
+    x2, y2 = box['xmax'], box['ymax']
+
+    if score > 0.8:
+        fill_color = (255, 0, 0, 100)
+        border_color = (255, 0, 0, 255)
+    elif score > 0.6:
+        fill_color = (255, 165, 0, 100)
+        border_color = (255, 165, 0, 255)
+    else:
+        fill_color = (255, 255, 0, 100)
+        border_color = (255, 255, 0, 255)
+
+    draw.rectangle([x1, y1, x2, y2], fill=fill_color)
+    draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2)
+
+    return overlay
+
+def draw_boxes(image, predictions):
+    result_image = image.copy().convert('RGBA')
+
+    for pred in predictions:
+        box = pred['box']
+        score = pred['score']
+
+        overlay = create_heatmap_overlay(image, box, score)
+        result_image = Image.alpha_composite(result_image, overlay)
+
+        draw = ImageDraw.Draw(result_image)
+        temp = 36.5 + (score * 2.5)
+        label = f"{translate_label(pred['label'])} ({score:.1%} • {temp:.1f}°C)"
+
+        text_bbox = draw.textbbox((box['xmin'], box['ymin']-20), label)
+        draw.rectangle(text_bbox, fill=(0, 0, 0, 180))
+
+        draw.text(
+            (box['xmin'], box['ymin']-20),
+            label,
+            fill=(255, 255, 255, 255)
+        )
+
+    return result_image
+
+def image_to_base64(image):
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    return f"data:image/png;base64,{img_str}"
+
+COMMON_STYLES = """
+    body {
+        font-family: system-ui, -apple-system, sans-serif;
+        background: #f0f2f5;
+        margin: 0;
+        padding: 20px;
+        color: #1a1a1a;
+    }
+    ::-webkit-scrollbar {
+        width: 8px;
+        height: 8px;
+    }
+
+    ::-webkit-scrollbar-track {
+        background: transparent;
+    }
+
+    ::-webkit-scrollbar-thumb {
+        background-color: rgba(156, 163, 175, 0.5);
+        border-radius: 4px;
+    }
+
+    .container {
+        max-width: 1200px;
+        margin: 0 auto;
+        background: white;
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .button {
+        background: #2d2d2d;
+        color: white;
+        border: none;
+        padding: 12px 30px;
+        border-radius: 8px;
+        cursor: pointer;
+        font-size: 1.1em;
+        transition: all 0.3s ease;
+        position: relative;
+    }
+    .button:hover {
+        background: #404040;
+    }
+    @keyframes progress {
+        0% { width: 0; }
+        100% { width: 100%; }
+    }
+    .button-progress {
+        position: absolute;
+        bottom: 0;
+        left: 0;
+        height: 4px;
+        background: rgba(255, 255, 255, 0.5);
+        width: 0;
+    }
+    .button:active .button-progress {
+        animation: progress 2s linear forwards;
+    }
+    img {
+        max-width: 100%;
+        height: auto;
+        border-radius: 8px;
+    }
+    @keyframes blink {
+        0% { opacity: 1; }
+        50% { opacity: 0; }
+        100% { opacity: 1; }
+    }
+    #loading {
+        display: none;
+        color: white;
+        margin-top: 10px;
+        animation: blink 1s infinite;
+        text-align: center;
+    }
+"""
+
+SAMPLE_IMAGES = [
+    {"id": "sample1", "filename": "sample1.png", "label": "Fracture"},
+    {"id": "sample2", "filename": "sample2.png", "label": "No Fracture"},
+    {"id": "sample3", "filename": "sample3.png", "label": "Fracture"},
+    {"id": "sample4", "filename": "sample4.png", "label": "No Fracture"},
+    {"id": "sample5", "filename": "sample5.png", "label": "Fracture"},
+    {"id": "sample6", "filename": "sample6.png", "label": "No Fracture"},
+    {"id": "sample7", "filename": "sample7.png", "label": "Fracture"},
+    {"id": "sample8", "filename": "sample8.png", "label": "No Fracture"},
+    {"id": "sample9", "filename": "sample9.png", "label": "Fracture"},
+    {"id": "sample10", "filename": "sample10.png", "label": "No Fracture"},
+]
+
+@app.get("/", response_class=HTMLResponse)
+async def main():
+    image_options = "".join(
+        f'<option value="{img["filename"]}">{img["id"]}</option>' for img in SAMPLE_IMAGES
+    )
+    content = f"""
+    <!DOCTYPE html>
+    <html>
+    <head>
+        <title>Fracture Detection</title>
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <style>
+            {COMMON_STYLES}
+
+            .upload-section {{
+                background: #2d2d2d;
+                padding: 40px;
+                border-radius: 12px;
+                margin: 20px 0;
+                text-align: center;
+                border: 2px dashed #404040;
+                transition: all 0.3s ease;
+                color: white;
+            }}
+            .upload-section:hover {{
+                border-color: #555;
+            }}
+            .image-selection {{
+                font-size: 1.1em;
+                margin: 20px 0;
+                color: white;
+            }}
+            select {{
+                padding: 10px;
+                border-radius: 8px;
+                border: 1px solid #404040;
+                background: #2d2d2d;
+                color: white;
+                transition: all 0.3s ease;
+                cursor: pointer;
+                font-size: 1em;
+            }}
+            select:hover {{
+                background: #404040;
+            }}
+            .confidence-slider {{
+                width: 100%;
+                max-width: 300px;
+                margin: 20px auto;
+            }}
+            input[type="range"] {{
+                width: 100%;
+                height: 8px;
+                border-radius: 4px;
+                background: #404040;
+                outline: none;
+                transition: all 0.3s ease;
+                -webkit-appearance: none;
+            }}
+            input[type="range"]::-webkit-slider-thumb {{
+                -webkit-appearance: none;
+                width: 20px;
+                height: 20px;
+                border-radius: 50%;
+                background: white;
+                cursor: pointer;
+                border: none;
+            }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <div class="upload-section">
+                <form action="/analyze" method="post" enctype="multipart/form-data" onsubmit="document.getElementById('loading').style.display = 'block';">
+                    <div class="image-selection">
+                        <label for="sample_image">Select a Sample X-ray:</label>
+                        <select name="sample_image" id="sample_image">
+                            {image_options}
+                        </select>
+                    </div>
+                    <div class="confidence-slider">
+                        <label for="threshold">Confidence Threshold: <span id="thresholdValue">0.60</span></label>
+                        <input type="range" id="threshold" name="threshold"
+                               min="0" max="1" step="0.05" value="0.60"
+                               oninput="document.getElementById('thresholdValue').textContent = parseFloat(this.value).toFixed(2)">
+                    </div>
+                    <button type="submit" class="button">
+                        Analyze
+                        <div class="button-progress"></div>
+                    </button>
+                    <div id="loading">Loading...</div>
+                </form>
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    return content
+
+@app.post("/analyze", response_class=HTMLResponse)
+async def analyze_file(sample_image: str, threshold: float = 0.60):
     try:
-        # Simple string matching for now - improve this if the HTML structure is complex
-        if "KnochenWächter" in fastapi_results_html:
-            if "Kein Knochenbruch" in fastapi_results_html:
-                diagnosed_class = "normal"
-            elif "Knochenbruch" in fastapi_results_html or "Auffällig" in fastapi_results_html:
-                diagnosed_class = "Fractured"
-
-        if diagnosed_class == "Fractured":
-            if "score-high" in fastapi_results_html:
-                severity = "Severe"
-            elif "score-medium" in fastapi_results_html:
-                severity = "Moderate"
-            else:
-                severity = "Mild"
+        # For now, let's just pick a random sample image for demonstration.
+        # Replace this with actual loading of the selected image.
+        # You will need to place the sample images in a directory (e.g., 'sample_images')
+        image_path = f"sample_images/{sample_image}"
+        try:
+            image = Image.open(image_path)
+        except FileNotFoundError:
+            return f"""
+            <!DOCTYPE html>
+            <html>
+            <head>
+                <title>Error</title>
+                <meta name="viewport" content="width=device-width, initial-scale=1.0">
+                <style>
+                    {COMMON_STYLES}
+                    .error-box {{
+                        background: #fee2e2;
+                        border: 1px solid #ef4444;
+                        padding: 20px;
+                        border-radius: 8px;
+                        margin: 20px 0;
+                    }}
+                </style>
+            </head>
+            <body>
+                <div class="container">
+                    <div class="error-box">
+                        <h3>Error</h3>
+                        <p>Sample image '{sample_image}' not found. Please ensure the image exists in the 'sample_images' directory.</p>
+                    </div>
+                    <a href="/" class="button back-button">
+                        ← Back
+                        <div class="button-progress"></div>
+                    </a>
+                </div>
+            </body>
+            </html>
+            """
+
+        predictions_watcher = models["BoneGuard"](image)
+        predictions_master = models["XRayMaster"](image)
+        predictions_locator = models["BoneEye"](image)
+
+        filtered_preds = [p for p in predictions_locator if p['score'] >= threshold]
+        if filtered_preds:
+            result_image = draw_boxes(image, filtered_preds)
         else:
-            severity = "Mild" # Assuming normal is mild
-    except Exception as e:
-        print(f"Error parsing FastAPI response: {e}")
-
-    # Treatment details table
-    treatment_data = [
-        ["Severity Level", "Recommended Treatment", "Recovery Duration"],
-        ["Mild", "Rest, pain relievers, and follow-up X-ray", "4-6 weeks"],
-        ["Moderate", "Plaster cast, minor surgery if needed", "6-10 weeks"],
-        ["Severe", "Major surgery, metal implants, physiotherapy", "Several months"]
-    ]
-
-    # Estimated cost & duration table
-    cost_duration_data = [
-        ["Hospital Type", "Estimated Cost", "Recovery Time"],
-        ["Government Hospital", f"₹{2000 if severity == 'Mild' else 8000 if severity == 'Moderate' else 20000} - ₹{5000 if severity == 'Mild' else 15000 if severity == 'Moderate' else 50000}", "4-12 weeks"],
-        ["Private Hospital", f"₹{10000 if severity == 'Mild' else 30000 if severity == 'Moderate' else 100000}+", "6 weeks - Several months"]
-    ]
-
-    # Save X-ray image for report
-    img = Image.open(xray).resize((300, 300))
-    img_path = f"{name}_xray.png"
-    img.save(img_path)
-
-    # Generate PDF report
-    report_path = f"{name}_fracture_report.pdf"
-    c = canvas.Canvas(report_path, pagesize=letter)
-
-    # Report title
-    c.setFont("Helvetica-Bold", 16)
-    c.drawString(200, 770, "Bone Fracture Detection Report")
-
-    # Patient details table
-    patient_data = [
-        ["Patient Name", name],
-        ["Age", age],
-        ["Gender", gender],
-        ["Weight", f"{weight} kg"],
-        ["Height", f"{height} cm"],
-        ["Allergies", allergies if allergies else "None"],
-        ["Cause of Injury", cause if cause else "Not Provided"],
-        ["Diagnosis", diagnosed_class],
-        ["Injury Severity", severity]
-    ]
-
-    # Format and align tables
-    def format_table(data):
-        table = Table(data, colWidths=[270, 270])  # Set 90% width
-        table.setStyle(TableStyle([
-            ('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
-            ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
-            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
-            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
-            ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
-            ('GRID', (0, 0), (-1, -1), 1, colors.black),
-            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')
-        ]))
-        return table
-
-    # Draw patient details table
-    patient_table = format_table(patient_data)
-    patient_table.wrapOn(c, 480, 500)
-    patient_table.drawOn(c, 50, 620)
-
-    # Load and insert X-ray image
-    c.drawInlineImage(img_path, 50, 320, width=250, height=250)
-    c.setFont("Helvetica-Bold", 12)
-    c.drawString(120, 290, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}")
-
-    # Draw treatment and cost tables
-    treatment_table = format_table(treatment_data)
-    treatment_table.wrapOn(c, 480, 200)
-    treatment_table.drawOn(c, 50, 200)
-
-    cost_table = format_table(cost_duration_data)
-    cost_table.wrapOn(c, 480, 150)
-    cost_table.drawOn(c, 50, 80)
-
-    c.save()
-
-    # Send email with the report
-    subject = "Bone Fracture Detection Report"
-    body = f"Dear {name},\n\nPlease find attached your bone fracture detection report.\n\nSincerely,\nYour Bone Fracture Detection System"
-
-    msg = MIMEMultipart()
-    msg['From'] = SENDER_EMAIL
-    msg['To'] = email
-    msg['Subject'] = subject
-    msg.attach(MIMEText(body))
-
-    with open(report_path, "rb") as attachment:
-        part = MIMEBase('application', "octet-stream")
-        part.set_payload(attachment.read())
-
-    encoders.encode_base64(part)
-    part.add_header('Content-Disposition', f"attachment; filename= {os.path.basename(report_path)}")
-    msg.attach(part)
+            result_image = image
+
+        # Logic to make fractured area black will be implemented here once images and fracture data are available.
+        # For demonstration purposes, let's just mark the detected areas.
+
+        result_image_b64 = image_to_base64(result_image)
+
+        results_html = f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>Results</title>
+            <meta name="viewport" content="width=device-width, initial-scale=1.0">
+            <style>
+                {COMMON_STYLES}
+
+                .results-grid {{
+                    display: grid;
+                    grid-template-columns: 1fr 1fr;
+                    gap: 20px;
+                    margin-top: 20px;
+                }}
+                .result-box {{
+                    background: white;
+                    padding: 20px;
+                    border-radius: 12px;
+                    margin: 10px 0;
+                    border: 1px solid #e9ecef;
+                }}
+                .score-high {{
+                    color: #0066cc;
+                    font-weight: bold;
+                }}
+
+                .score-medium {{
+                    color: #ffa500;
+                    font-weight: bold;
+                }}
+                .back-button {{
+                    display: inline-block;
+                    text-decoration: none;
+                    margin-top: 20px;
+                }}
+                h3 {{
+                    color: #0066cc;
+                    margin-top: 0;
+                }}
+                @media (max-width: 768px) {{
+                    .results-grid {{
+                        grid-template-columns: 1fr;
+                    }}
+                }}
+            </style>
+        </head>
+        <body>
+            <div class="container">
+                <div class="results-grid">
+                    <div>
+                        <div class="result-box"><h3>BoneGuard</h3>
+        """
+
+        for pred in predictions_watcher:
+            confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
+            results_html += f"""
+                <div>
+                    <span class="{confidence_class}">{pred['score']:.1%}</span> -
+                    {translate_label(pred['label'])}
+                </div>
+            """
+        results_html += "</div>"
+
+        results_html += "<div class='result-box'><h3>XRayMaster</h3>"
+        for pred in predictions_master:
+            confidence_class = "score-high" if pred['score'] > 0.7 else "score-medium"
+            results_html += f"""
+                <div>
+                    <span class="{confidence_class}">{pred['score']:.1%}</span> -
+                    {translate_label(pred['label'])}
+                </div>
+            """
+        results_html += "</div></div>"
+
+        results_html += f"""
+                    <div class='result-box'>
+                        <h3>Fracture Localization</h3>
+                        <img src="{result_image_b64}" alt="Analyzed image">
+                    </div>
+                </div>
+
+                <a href="/" class="button back-button">
+                    ← Back
+                    <div class="button-progress"></div>
+                </a>
+            </div>
+        </body>
+        </html>
+        """
+
+        return results_html
 
-    try:
-        with smtplib.SMTP_SSL('smtp.gmail.com', 465) as server:
-            server.login(SENDER_EMAIL, SENDER_PASSWORD)
-            server.sendmail(SENDER_EMAIL, email, msg.as_string())
-        print(f"Report sent successfully to {email}")
-        return report_path  # Return path for auto-download
     except Exception as e:
-        return f"Error sending email: {e}"
-
-# Define Gradio Interface
-with gr.Blocks() as app:
-    # Remove the HTML content loading
-    gr.Markdown("## Bone Fracture Detection System")
-
-    with gr.Row():
-        name = gr.Textbox(label="Patient Name")
-        age = gr.Number(label="Age")
-        gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
-
-    with gr.Row():
-        weight = gr.Number(label="Weight (kg)")
-        height = gr.Number(label="Height (cm)")
-
-    with gr.Row():
-        allergies = gr.Textbox(label="Allergies (if any)")
-        cause = gr.Textbox(label="Cause of Injury")
-
-    with gr.Row():
-        xray = gr.Image(type="filepath", label="Upload X-ray Image")
-
-    # Remove the sample image selector
-    email = gr.Textbox(label="Patient Email Address")
-    submit_button = gr.Button("Generate Report and Send Email")
-    output_file = gr.File(label="Download Report")
-
-    # Remove the sample image loading functionality
-
-    submit_button.click(
-        generate_report,
-        inputs=[name, age, gender, weight, height, allergies, cause, xray, email],
-        outputs=[output_file],
-    )
+        return f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>Error</title>
+            <meta name="viewport" content="width=device-width, initial-scale=1.0">
+            <style>
+                {COMMON_STYLES}
+                .error-box {{
+                    background: #fee2e2;
+                    border: 1px solid #ef4444;
+                    padding: 20px;
+                    border-radius: 8px;
+                    margin: 20px 0;
+                }}
+            </style>
+        </head>
+        <body>
+            <div class="container">
+                <div class="error-box">
+                    <h3>Error</h3>
+                    <p>{str(e)}</p>
+                </div>
+                <a href="/" class="button back-button">
+                    ← Back
+                    <div class="button-progress"></div>
+                </a>
+            </div>
+        </body>
+        </html>
+        """
 
-# Launch the Gradio app
 if __name__ == "__main__":
-    app.launch()
+    uvicorn.run(app, host="0.0.0.0", port=7860)