Initial commit

Dockerfile

@@ -0,0 +1,31 @@
+# Dockerfile
+FROM python:3.9-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    tesseract-ocr \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Create uploads directory
+RUN mkdir -p uploads && chmod -R 777 uploads
+
+# Copy application files
+COPY app.py .
+COPY templates/index.html templates/
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PORT=7860
+
+# Expose port
+EXPOSE 7860
+
+# Command to run the application
+CMD ["gunicorn", "--bind", "0.0.0.0:7860","--timeout", "240", "app:app"]

app.py

@@ -0,0 +1,213 @@
+import os
+import logging
+from flask import Flask, request, render_template, redirect, flash, url_for
+from werkzeug.utils import secure_filename
+import PyPDF2
+from PIL import Image
+import io
+import base64
+import google.generativeai as genai
+import pytesseract
+import markdown
+
+app = Flask(__name__)
+app.secret_key = 'your_secret_key_here'
+logging.basicConfig(level=logging.DEBUG)
+
+# Configure upload settings
+UPLOAD_FOLDER = 'uploads'
+ALLOWED_EXTENSIONS = {'pdf', 'jpg', 'jpeg', 'png'}
+MAX_FILE_SIZE = 20 * 1024 * 1024  # 20MB max file size
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.config['MAX_CONTENT_LENGTH'] = MAX_FILE_SIZE
+
+# Configure Gemini API
+GEMINI_MODEL = "gemini-2.0-flash"  # Updated to use the correct model name
+genai.configure(api_key="AIzaSyArihOGcyK5KcQR4ntIqNga6bSoq7kM7Yo")
+
+# Ensure the upload folder exists
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+def setup_gemini():
+    """Initialize Gemini API with error handling"""
+    try:
+        genai.configure(api_key="AIzaSyArihOGcyK5KcQR4ntIqNga6bSoq7kM7Yo")
+        return True
+    except Exception as e:
+        logging.error(f"Failed to configure Gemini API: {str(e)}")
+        return False
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def extract_images_from_pdf(pdf_path):
+    """Extract images from PDF file and convert to base64"""
+    images_data = []
+    try:
+        with open(pdf_path, "rb") as pdf_file:
+            pdf_reader = PyPDF2.PdfReader(pdf_file)
+            for page in pdf_reader.pages:
+                if hasattr(page, 'images'):
+                    for image_file_object in page.images:
+                        try:
+                            image = Image.open(io.BytesIO(image_file_object.data))
+                            buffered = io.BytesIO()
+                            image.save(buffered, format="PNG")
+                            img_str = base64.b64encode(buffered.getvalue()).decode()
+                            images_data.append(img_str)
+                        except Exception as e:
+                            logging.warning(f"Failed to process image: {str(e)}")
+    except Exception as e:
+        logging.error(f"Error extracting images: {str(e)}")
+    return images_data
+
+def extract_text_from_image(image_path):
+    """Extract text from image using OCR"""
+    try:
+        image = Image.open(image_path)
+        text = pytesseract.image_to_string(image)
+        return text
+    except Exception as e:
+        logging.error(f"Error extracting text from image: {str(e)}")
+        return ""
+
+def get_scan_type(text):
+    """Determine the type of scan from the text content"""
+    text_lower = text.lower()
+    scan_types = {
+        'electrocardiogram': 'ECG'
+    }
+    
+    for key, value in scan_types.items():
+        if key in text_lower:
+            return value
+    return 'Unknown Scan Type'
+
+def analyze_medical_scan(scan_type, combined_text, image_count):
+    """Generate analysis """
+    try:
+        logging.debug(f"Initializing Gemini model with type: {GEMINI_MODEL}")
+        model = genai.GenerativeModel(GEMINI_MODEL)
+        
+        prompt = f"""
+        As a professional medical imaging specialist, analyze this {scan_type} report.
+        Please provide a clear analysis of the following points:
+        1. Key findings and observations
+        2. Any significant abnormalities or concerns
+        3. Technical quality of the scan
+        4. Recommendations for follow-up (if any)
+
+        Report details:
+        - Number of images: {image_count}
+        - Text content: {combined_text}
+
+        Note any limitations if image quality or content is unclear.
+        """
+        
+        logging.debug("Sending request to Gemini API")
+        response = model.generate_content(prompt)
+        logging.debug("Received response from Gemini API")
+        
+        if not response or not hasattr(response, 'text'):
+            raise ValueError("Invalid response from Gemini API")
+            
+        return response.text
+    except Exception as e:
+        logging.error(f"Error generating analysis: {str(e)}")
+        raise
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if not setup_gemini():
+        flash('Failed to initialize AI service. Please try again later.', 'error')
+        return render_template("index.html", result=None)
+
+    result = None
+    if request.method == 'POST':
+        logging.debug("Received a POST request.")
+        
+        # Check if the post request has the file part
+        if 'file' not in request.files:
+            logging.debug("No file part in request")
+            flash('No file selected', 'error')
+            return redirect(request.url)
+            
+        file = request.files['file']
+        
+        # If user does not select file, browser also
+        # submit an empty part without filename
+        if file.filename == '':
+            logging.debug("No selected file")
+            flash('No file selected', 'error')
+            return redirect(request.url)
+            
+        # Check file size
+        if request.content_length > MAX_FILE_SIZE:
+            logging.debug("File too large")
+            flash(f'File size exceeds {MAX_FILE_SIZE // (1024 * 1024)}MB limit', 'error')
+            return redirect(request.url)
+            
+        if file and allowed_file(file.filename):
+            file_path = None
+            try:
+                filename = secure_filename(file.filename)
+                file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+                logging.debug(f"Saving file to: {file_path}")
+                file.save(file_path)
+                
+                # Process text and images
+                combined_text = ""
+                if filename.lower().endswith('.pdf'):
+                    logging.debug("Processing PDF file")
+                    with open(file_path, "rb") as pdf_file:
+                        pdf_reader = PyPDF2.PdfReader(pdf_file)
+                        for page_num, page in enumerate(pdf_reader.pages):
+                            page_text = page.extract_text()
+                            if page_text:
+                                combined_text += page_text
+                            logging.debug(f"Extracted text from page {page_num}")
+                    images_data = extract_images_from_pdf(file_path)
+                else:  # Image file
+                    logging.debug("Processing image file")
+                    combined_text = extract_text_from_image(file_path)
+                    with open(file_path, "rb") as img_file:
+                        img_data = base64.b64encode(img_file.read()).decode()
+                        images_data = [img_data]
+                
+                logging.debug(f"Extracted text length: {len(combined_text)}")
+                scan_type = get_scan_type(combined_text)
+                image_count = len(images_data)
+                logging.debug(f"Detected scan type: {scan_type}, Image count: {image_count}")
+                
+                # Generate analysis
+                logging.debug("Generating analysis")
+                analysis_text = analyze_medical_scan(scan_type, combined_text, image_count)
+                analysis_html = markdown.markdown(analysis_text)
+                
+                result = {
+                    'analysis': analysis_text,
+                    'analysis_html': analysis_html,
+                    'scan_type': scan_type,
+                    'image_count': image_count,
+                    'images': images_data
+                }
+                logging.debug("Analysis complete")
+                
+            except Exception as e:
+                logging.exception("Error processing file:")
+                flash(f"Error processing file: {str(e)}", 'error')
+                return redirect(request.url)
+            finally:
+                # Clean up uploaded file
+                if file_path and os.path.exists(file_path):
+                    os.remove(file_path)
+                    logging.debug("Temporary file removed after processing.")
+        else:
+            logging.debug("Invalid file type")
+            flash('Only PDF and image files are allowed', 'error')
+            return redirect(request.url)
+            
+    return render_template("index.html", result=result)
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

@@ -0,0 +1,8 @@
+flask==2.0.1
+Werkzeug==2.0.1
+PyPDF2==3.0.1
+Pillow==9.5.0
+Markdown
+google-generativeai==0.3.0
+pytesseract==0.3.10
+gunicorn==20.1.0

templates/index.html

@@ -0,0 +1,174 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>ECG Scan Analyzer</title>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/tailwindcss/2.2.19/tailwind.min.css" rel="stylesheet">
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/alpinejs/2.3.0/alpine.js"></script>
+</head>
+<body class="bg-gray-50 min-h-screen">
+    <div x-data="{ 
+        isDragging: false,
+        isAnalyzing: false,
+        fileName: '',
+        selectedScan: 'ECG',
+        handleFile(event) {
+            const file = event.target.files[0];
+            if (file) {
+                this.fileName = file.name;
+                document.querySelector('form').submit();
+                this.isAnalyzing = true;
+            }
+        }
+    }" 
+    class="container mx-auto px-4 py-8 max-w-4xl">
+        
+        <!-- Header -->
+        <header class="text-center mb-12">
+            <h1 class="text-4xl font-bold text-gray-800 mb-4">ECG Scan Analyzer</h1>
+            <p class="text-gray-600 max-w-2xl mx-auto">Upload your ECG scan reports in PDF format and receive an instant professional analysis powered by advanced AI.</p>
+        </header>
+
+        {% if result %}
+<!-- Results Section -->
+<div class="bg-white rounded-lg shadow-lg p-6 mb-8">
+    <div class="flex items-center justify-between mb-4">
+        <h2 class="text-2xl font-semibold text-gray-800">Analysis Results</h2>
+        <span class="px-3 py-1 bg-blue-100 text-blue-800 rounded-full text-sm font-medium">
+            {{ result.scan_type }} Scan
+        </span>
+    </div>
+    
+    <div class="bg-gray-50 rounded-lg p-6 mb-6">
+        <!-- Render the converted HTML analysis -->
+        <div class="text-gray-700 whitespace-pre-wrap">
+            {{ result.analysis_html | safe }}
+        </div>
+    </div>
+    
+    {% if result.images %}
+    <div class="mt-6">
+        <h3 class="text-lg font-medium text-gray-800 mb-4">Scan Images</h3>
+        <div class="grid grid-cols-1 md:grid-cols-2 gap-4">
+            {% for image in result.images %}
+            <div class="border rounded-lg p-2">
+                <img src="data:image/png;base64,{{ image }}" alt="Scan image" class="w-full h-auto">
+            </div>
+            {% endfor %}
+        </div>
+    </div>
+    {% endif %}
+    
+            
+            <a href="{{ url_for('index') }}" 
+               class="inline-flex items-center px-6 py-3 border border-transparent text-base font-medium rounded-md text-white bg-blue-600 hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500 transition-colors duration-200 mt-6">
+                Analyze Another Report
+            </a>
+        </div>
+        {% else %}
+        <!-- Upload Section -->
+        <div class="bg-white rounded-lg shadow-lg p-8"
+             x-on:dragover.prevent="isDragging = true"
+             x-on:dragleave.prevent="isDragging = false"
+             x-on:drop.prevent="isDragging = false; handleFile($event)">
+            
+            <form action="{{ url_for('index') }}" method="POST" enctype="multipart/form-data"
+                  class="relative">
+                  
+                <!-- Scan Type Selection -->
+                <div class="mb-6">
+                    <label class="block text-sm font-medium text-gray-700 mb-2">Select Scan Type</label>
+                    <div class="grid grid-cols-2 gap-4">
+                        <label class="relative flex cursor-pointer">
+                            <input type="radio" name="scan_type" value="ECG" class="sr-only" x-model="selectedScan">
+                            <div class="flex items-center justify-center w-full p-4 border rounded-lg"
+                                 :class="{ 'border-blue-500 bg-blue-50': selectedScan === 'ECG', 'border-gray-200': selectedScan !== 'ECG' }">
+                                <span class="text-sm font-medium" :class="{ 'text-blue-600': selectedScan === 'ECG', 'text-gray-900': selectedScan !== 'ECG' }">
+                                    ECG Scan
+                                </span>
+                            </div>
+                        </label>
+                        <!-- MRI option removed -->
+                    </div>
+                </div>
+                
+                <div class="flex flex-col items-center justify-center space-y-6"
+                     :class="{ 'bg-blue-50 border-2 border-dashed border-blue-400': isDragging,
+                              'border-2 border-dashed border-gray-300': !isDragging }"
+                     class="rounded-lg p-8 transition-all duration-200">
+                    
+                    <div class="text-center" x-show="!isAnalyzing">
+                        <svg class="mx-auto h-12 w-12 text-gray-400" stroke="currentColor" fill="none" viewBox="0 0 48 48">
+                            <path d="M28 8H12a4 4 0 00-4 4v20m32-12v8m0 0v8a4 4 0 01-4 4H12a4 4 0 01-4-4v-4m32-4l-3.172-3.172a4 4 0 00-5.656 0L28 28M8 32l9.172-9.172a4 4 0 015.656 0L28 28m0 0l4-4m4-4h8m-4-4v8m-12 4h.02" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" />
+                        </svg>
+                        <div class="mt-4">
+                            <label for="file" class="cursor-pointer">
+                                <span class="mt-2 block text-sm font-medium text-gray-900">
+                                    Drop your PDF here, or
+                                    <span class="text-blue-600 hover:text-blue-500">browse</span>
+                                </span>
+                            </label>
+                            <input type="file" id="file" name="file" accept=".pdf" required
+                                   class="hidden"
+                                   @change="handleFile($event)">
+                            <p class="text-xs text-gray-500 mt-2">PDF files only, up to 20MB</p>
+                        </div>
+                    </div>
+
+                    <!-- Loading State -->
+                    <div x-show="isAnalyzing" class="text-center">
+                        <div class="animate-spin rounded-full h-12 w-12 border-b-2 border-blue-600 mx-auto"></div>
+                        <p class="mt-4 text-sm text-gray-600">Analyzing your <span x-text="selectedScan"></span> report...</p>
+                      </div>
+                    </div>
+                </form>
+    
+                <!-- Error Messages -->
+                <div class="mt-4">
+                    {% with messages = get_flashed_messages(with_categories=true) %}
+                        {% if messages %}
+                            {% for category, message in messages %}
+                                <div class="rounded-md p-4 {% if category == 'error' %}bg-red-50 text-red-700{% else %}bg-green-50 text-green-700{% endif %}">
+                                    {{ message }}
+                                </div>
+                            {% endfor %}
+                        {% endif %}
+                    {% endwith %}
+                </div>
+            </div>
+            {% endif %}
+    
+            <!-- Features Section -->
+            <div class="mt-12 grid grid-cols-1 gap-8 md:grid-cols-3">
+                <div class="text-center">
+                    <div class="rounded-lg bg-blue-50 p-6">
+                        <svg class="h-8 w-8 text-blue-600 mx-auto" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M13 10V3L4 14h7v7l9-11h-7z"/>
+                        </svg>
+                        <h3 class="mt-4 text-lg font-medium text-gray-900">Instant Analysis</h3>
+                        <p class="mt-2 text-sm text-gray-500">Get quick insights from your medical scans within seconds</p>
+                    </div>
+                </div>
+                <div class="text-center">
+                    <div class="rounded-lg bg-blue-50 p-6">
+                        <svg class="h-8 w-8 text-blue-600 mx-auto" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9 12l2 2 4-4m5.618-4.016A11.955 11.955 0 0112 2.944a11.955 11.955 0 01-8.618 3.04A12.02 12.02 0 003 9c0 5.591 3.824 10.29 9 11.622 5.176-1.332 9-6.03 9-11.622 0-1.042-.133-2.052-.382-3.016z"/>
+                        </svg>
+                        <h3 class="mt-4 text-lg font-medium text-gray-900">Secure Processing</h3>
+                        <p class="mt-2 text-sm text-gray-500">Your medical data is processed securely and never stored</p>
+                    </div>
+                </div>
+                <div class="text-center">
+                    <div class="rounded-lg bg-blue-50 p-6">
+                        <svg class="h-8 w-8 text-blue-600 mx-auto" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M19.428 15.428a2 2 0 00-1.022-.547l-2.387-.477a6 6 0 00-3.86.517l-.318.158a6 6 0 01-3.86.517L6.05 15.21a2 2 0 00-1.806.547M8 4h8l-1 1v5.172a2 2 0 00.586 1.414l5 5c1.26 1.26.367 3.414-1.415 3.414H4.828c-1.782 0-2.674-2.154-1.414-3.414l5-5A2 2 0 009 10.172V5L8 4z"/>
+                        </svg>
+                        <h3 class="mt-4 text-lg font-medium text-gray-900">AI-Powered</h3>
+                        <p class="mt-2 text-sm text-gray-500">Advanced AI technology for accurate medical scan analysis</p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </body>
+    </html>