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README.md

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+---
+title: Scribbled Docs Notes
+emoji: 🐨
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.36.2
+app_file: app.py
+pinned: false
+license: mit
+short_description: An app to convert doc notes to SOAP
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# -*- coding: utf-8 -*-
+"""gemma_3n_colab.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1U5pbaYG8qD7HFANwU7PI1jbLGOPvArnP
+
+# 🏥 Gemma 3N SOAP Note Generator
+## Interactive Medical Documentation Assistant
+
+This notebook provides a complete interface for generating SOAP notes from medical text using the Gemma 3N model.
+"""
+
+# Install required packages
+!pip install -q transformers torch torchvision torchaudio timm accelerate
+!pip install -q ipywidgets gradio
+!pip install -q --upgrade huggingface_hub
+!pip install GPUtil
+
+# Enable widgets
+from IPython.display import display, HTML
+display(HTML("<script src='https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js'></script>"))
+
+# Import libraries and authenticate
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import gradio as gr
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+import io
+import base64
+from datetime import datetime
+from huggingface_hub import login
+import getpass
+
+# Authenticate with HuggingFace
+print("🔐 HuggingFace Authentication Required")
+print("Please enter your HuggingFace token (it will be hidden):")
+hf_token = getpass.getpass("HF Token: ")
+
+try:
+    login(token=hf_token)
+    print("✅ Successfully authenticated with HuggingFace!")
+except Exception as e:
+    print(f"❌ Authentication failed: {e}")
+    print("Please check your token and try again.")
+
+# Check GPU availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"🖥️  Using device: {device}")
+if torch.cuda.is_available():
+    print(f"🚀 GPU: {torch.cuda.get_device_name(0)}")
+    print(f"💾 GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
+else:
+    print("⚠️  Running on CPU - this will be slower")
+
+# Load Gemma 3N model
+print("📡 Loading Gemma 3N model...")
+model_id = "google/gemma-3n-e2b-it"
+
+print("🔧 Loading processor...")
+processor = AutoProcessor.from_pretrained(model_id)
+
+print("🤖 Loading Gemma 3N model (this may take a few minutes)...")
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    low_cpu_mem_usage=True,
+).to(device)
+
+print("✅ Gemma 3N model loaded successfully!")
+print(f"📊 Model size: ~2.9GB")
+print(f"🎯 Ready for SOAP note generation!")
+
+# SOAP Note Generation Function
+def generate_soap_note(doctor_notes, include_timestamp=True):
+    """
+    Generate a SOAP note from unstructured doctor's notes
+    """
+    if not doctor_notes.strip():
+        return "❌ Please enter some medical notes to process."
+
+    prompt = f"""You are a medical AI assistant. Convert the following unstructured doctor's notes into a professional SOAP note format.
+
+Doctor's Notes:
+{doctor_notes}
+
+Please generate a structured SOAP note with the following sections:
+- SUBJECTIVE: Patient's reported symptoms and history
+- OBJECTIVE: Physical examination findings, vital signs, and test results
+- ASSESSMENT: Clinical diagnosis and reasoning
+- PLAN: Treatment plan, medications, and follow-up
+
+Format your response as a proper medical SOAP note with specific details extracted from the notes."""
+
+    try:
+        # Process input
+        inputs = processor(text=prompt, return_tensors="pt").to(device)
+
+        # Generate response
+        print("🔄 Generating SOAP note with Gemma 3N...")
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                temperature=0.3,  # Lower temperature for medical precision
+                do_sample=True,
+                pad_token_id=processor.tokenizer.eos_token_id
+            )
+
+        # Decode response
+        generated_text = processor.decode(outputs[0], skip_special_tokens=True)
+
+        # Extract only the generated part (remove the prompt)
+        soap_response = generated_text[len(prompt):].strip()
+
+        # Add header if requested
+        if include_timestamp:
+            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            header = f"""📋 SOAP NOTE - Generated by Gemma 3N
+🕐 Timestamp: {timestamp}
+🤖 Model: google/gemma-3n-e2b-it
+🔒 Processed locally on device: {device.upper()}
+
+{'='*60}
+"""
+            return header + soap_response
+
+        return soap_response
+
+    except Exception as e:
+        return f"❌ Error generating SOAP note: {str(e)}"
+
+print("✅ SOAP generation function ready!")
+
+"""## 📝 Interactive SOAP Note Generator
+### Enter medical notes below and generate professional SOAP documentation
+"""
+
+# Create interactive widgets
+print("🎨 Creating interactive interface...")
+
+# Text input area
+notes_input = widgets.Textarea(
+    value='',
+    placeholder='Enter unstructured doctor notes here...\n\nExample:\nPatient John Smith, 45yo male, came in complaining of chest pain for 2 days. Pain is sharp, 7/10 intensity, worse with movement. Vital signs: BP 140/90, HR 88, Temp 98.6F...',
+    description='Medical Notes:',
+    layout=widgets.Layout(width='100%', height='200px')
+)
+
+# File upload widget
+file_upload = widgets.FileUpload(
+    accept='.txt,.doc,.docx,.pdf',
+    multiple=False,
+    description='Or upload file:',
+    layout=widgets.Layout(width='300px')
+)
+
+# Generate button
+generate_btn = widgets.Button(
+    description='🤖 Generate SOAP Note',
+    button_style='primary',
+    layout=widgets.Layout(width='200px', height='40px')
+)
+
+# Output area
+output_area = widgets.HTML(
+    value='<p style="color: #666;">📋 Ready to generate SOAP notes! Enter medical notes above or upload a file.</p>',
+    layout=widgets.Layout(width='100%', height='400px', overflow='auto', border='1px solid #ddd', padding='10px')
+)
+
+# Example buttons
+example1_btn = widgets.Button(description='📋 Chest Pain Example', button_style='info', layout=widgets.Layout(width='180px'))
+example2_btn = widgets.Button(description='🩺 Diabetes Follow-up', button_style='info', layout=widgets.Layout(width='180px'))
+example3_btn = widgets.Button(description='👶 Pediatric Visit', button_style='info', layout=widgets.Layout(width='180px'))
+
+# Clear button
+clear_btn = widgets.Button(description='🗑️ Clear', button_style='warning', layout=widgets.Layout(width='100px'))
+
+print("✅ Interface widgets created!")
+
+# Example medical notes
+examples = {
+    'chest_pain': """Patient John Smith, 45yo male, came in complaining of chest pain for 2 days. Pain is sharp, 7/10 intensity, worse with movement. No radiation to arms. Vital signs: BP 140/90, HR 88, Temp 98.6F, RR 16, O2 sat 98%. Physical exam shows tenderness over left chest wall, no murmurs. EKG normal sinus rhythm. Chest X-ray clear. Diagnosed with costochondritis. Prescribed ibuprofen 600mg TID and advised rest. Follow up in 1 week if symptoms persist.""",
+
+    'diabetes': """Sarah Johnson, 62yo female with Type 2 diabetes, here for routine follow-up. Says blood sugars have been running high lately, 180-220 mg/dL. Taking metformin 1000mg BID. Diet has been poor due to holiday stress. Weight increased 5 lbs since last visit. BP 150/85, BMI 32. HbA1c 8.2% (was 7.1% 3 months ago). Feet exam normal, no neuropathy. Plan to increase metformin to 1000mg TID, refer to nutritionist, recheck labs in 3 months.""",
+
+    'pediatric': """Tommy Rodriguez, 8yo male, brought by mother for fever and cough x3 days. Fever up to 102F, productive cough with yellow sputum. Decreased appetite, no vomiting or diarrhea. Vital signs: Temp 101.2F, HR 110, RR 24, BP 95/60. Exam shows bilateral crackles in lower lobes, no wheeze. Throat clear. Diagnosed with bacterial pneumonia. Prescribed amoxicillin 500mg BID x10 days. Return if fever persists >48 hours on antibiotics."""
+}
+
+# Event handlers
+def on_generate_click(b):
+    with output_area:
+        output_area.value = '<p style="color: #007bff;">🔄 Processing with Gemma 3N... Please wait...</p>'
+
+        # Get text from input or uploaded file
+        text_to_process = notes_input.value
+
+        # Check if file was uploaded
+        if file_upload.value and len(file_upload.value) > 0:
+            try:
+                uploaded_file = list(file_upload.value.values())[0]
+                file_content = uploaded_file['content'].decode('utf-8')
+                text_to_process = file_content
+                notes_input.value = file_content  # Show in text area
+            except Exception as e:
+                output_area.value = f'<p style="color: #dc3545;">❌ Error reading file: {str(e)}</p>'
+                return
+
+        if not text_to_process.strip():
+            output_area.value = '<p style="color: #dc3545;">❌ Please enter medical notes or upload a file!</p>'
+            return
+
+        # Generate SOAP note
+        soap_note = generate_soap_note(text_to_process)
+
+        # Format output as HTML
+        formatted_output = f'<pre style="font-family: monospace; font-size: 12px; line-height: 1.4; white-space: pre-wrap;">{soap_note}</pre>'
+        output_area.value = formatted_output
+
+def on_example1_click(b):
+    notes_input.value = examples['chest_pain']
+    output_area.value = '<p style="color: #28a745;">✅ Chest pain example loaded! Click "Generate SOAP Note" to process.</p>'
+
+def on_example2_click(b):
+    notes_input.value = examples['diabetes']
+    output_area.value = '<p style="color: #28a745;">✅ Diabetes follow-up example loaded! Click "Generate SOAP Note" to process.</p>'
+
+def on_example3_click(b):
+    notes_input.value = examples['pediatric']
+    output_area.value = '<p style="color: #28a745;">✅ Pediatric example loaded! Click "Generate SOAP Note" to process.</p>'
+
+def on_clear_click(b):
+    notes_input.value = ''
+    file_upload.value = ()
+    output_area.value = '<p style="color: #666;">📋 Ready to generate SOAP notes! Enter medical notes above or upload a file.</p>'
+
+# Bind event handlers
+generate_btn.on_click(on_generate_click)
+example1_btn.on_click(on_example1_click)
+example2_btn.on_click(on_example2_click)
+example3_btn.on_click(on_example3_click)
+clear_btn.on_click(on_clear_click)
+
+print("✅ Event handlers configured!")
+
+# Define example medical notes first
+example_notes_1 = """
+Patient: John Smith, 45-year-old male
+Chief Complaint: Chest pain for 2 hours
+History: Patient reports sudden onset of sharp chest pain while at work. Pain is 7/10 intensity, located substernal, radiating to left arm. Associated with shortness of breath and diaphoresis. No previous cardiac history. Denies nausea or vomiting.
+Physical Exam: VS: BP 150/90, HR 110, RR 22, O2 Sat 96% on RA. Patient appears anxious and diaphoretic. Heart: Regular rhythm, no murmurs. Lungs: Clear bilaterally. Extremities: No edema.
+Assessment: Acute chest pain, rule out myocardial infarction
+Plan: EKG, cardiac enzymes, chest X-ray, aspirin 325mg, continuous cardiac monitoring
+"""
+
+example_notes_2 = """
+Patient: Sarah Johnson, 28-year-old female
+Chief Complaint: Severe headache and fever
+History: 3-day history of progressive headache, fever up to 101.5°F, photophobia, and neck stiffness. Patient reports this is the worst headache of her life. No recent travel or sick contacts. No rash noted.
+Physical Exam: VS: T 101.2°F, BP 130/80, HR 95, RR 18. Patient appears ill and photophobic. HEENT: Pupils equal and reactive. Neck: Stiff with positive Kernig's sign. Neurologic: Alert and oriented x3, no focal deficits.
+Assessment: Suspected meningitis
+Plan: Lumbar puncture, blood cultures, empiric antibiotics, supportive care
+"""
+
+example_notes_3 = """
+Patient: Robert Davis, 62-year-old male
+Chief Complaint: Shortness of breath and leg swelling
+History: 2-week history of progressive dyspnea on exertion, orthopnea, and bilateral lower extremity edema. Patient has history of hypertension and diabetes. Reports sleeping on 3 pillows due to breathing difficulty.
+Physical Exam: VS: BP 140/85, HR 88, RR 24, O2 Sat 92% on RA. Heart: S3 gallop present, JVD elevated. Lungs: Bilateral rales in lower fields. Extremities: 2+ pitting edema bilaterally.
+Assessment: Congestive heart failure exacerbation
+Plan: Chest X-ray, BNP, echocardiogram, furosemide, ACE inhibitor, daily weights
+"""
+
+# Event handlers
+def on_generate_click(b):
+    try:
+        # Update the HTML widget directly
+        output_area.value = '<p style="color: #007bff;">🔄 Processing with Gemma 3N... Please wait...</p>'
+
+        # Get input text
+        input_text = notes_input.value.strip()
+
+        # Check if file was uploaded
+        if file_upload.value:
+            try:
+                # Process uploaded file
+                uploaded_file = list(file_upload.value.values())[0]
+                file_content = uploaded_file['content'].decode('utf-8')
+                input_text = file_content
+            except Exception as upload_error:
+                output_area.value = f'<p style="color: #ff6b6b;">❌ File upload error: {str(upload_error)}</p>'
+                return
+
+        if not input_text:
+            output_area.value = '<p style="color: #ff6b6b;">⚠️ Please enter medical notes or upload a file first!</p>'
+            return
+
+        # Check if generate_soap_note function exists
+        if 'generate_soap_note' not in globals():
+            output_area.value = '<p style="color: #ff6b6b;">❌ Error: generate_soap_note function not found. Please define it first.</p>'
+            return
+
+        # Generate SOAP note using Gemma
+        soap_note = generate_soap_note(input_text)
+
+        # Escape HTML in soap_note to prevent rendering issues
+        import html
+        escaped_soap_note = html.escape(soap_note)
+
+        # Display result
+        output_area.value = f'''
+        <div style="background: #f8f9fa; padding: 15px; border-radius: 8px; border-left: 4px solid #28a745;">
+            <h4 style="color: #28a745; margin-top: 0;">✅ Generated SOAP Note:</h4>
+            <pre style="white-space: pre-wrap; font-family: 'Courier New', monospace; background: white; padding: 15px; border-radius: 5px; border: 1px solid #ddd;">{escaped_soap_note}</pre>
+        </div>
+        '''
+
+    except Exception as e:
+        import traceback
+        error_details = traceback.format_exc()
+        output_area.value = f'''
+        <div style="color: #ff6b6b; background: #ffe6e6; padding: 15px; border-radius: 5px;">
+            <h4>❌ Error Details:</h4>
+            <p><strong>Error:</strong> {str(e)}</p>
+            <details>
+                <summary>Click for full traceback</summary>
+                <pre style="font-size: 12px; background: #fff; padding: 10px; border-radius: 3px; margin-top: 10px;">{error_details}</pre>
+            </details>
+        </div>
+        '''
+
+def on_clear_click(b):
+    try:
+        notes_input.value = ""
+        file_upload.value = ()
+        output_area.value = '<p>📋 Ready to generate SOAP notes! Enter medical notes above or upload a file.</p>'
+    except Exception as e:
+        output_area.value = f'<p style="color: #ff6b6b;">❌ Clear error: {str(e)}</p>'
+
+def on_example_click(example_text):
+    def handler(b):
+        try:
+            notes_input.value = example_text
+            output_area.value = '<p style="color: #28a745;">📋 Example loaded! Click "Generate SOAP Note" to process.</p>'
+        except Exception as e:
+            output_area.value = f'<p style="color: #ff6b6b;">❌ Example load error: {str(e)}</p>'
+    return handler
+
+# Connect event handlers to buttons
+try:
+    generate_btn.on_click(on_generate_click)
+    clear_btn.on_click(on_clear_click)
+    example1_btn.on_click(on_example_click(example_notes_1))
+    example2_btn.on_click(on_example_click(example_notes_2))
+    example3_btn.on_click(on_example_click(example_notes_3))
+
+    print("✅ Event handlers connected successfully!")
+    print("📋 Example notes loaded:")
+    print("   - Example 1: Chest pain case")
+    print("   - Example 2: Suspected meningitis")
+    print("   - Example 3: Heart failure")
+
+except Exception as e:
+    print(f"❌ Error connecting event handlers: {str(e)}")
+    import traceback
+    traceback.print_exc()
+
+"""## 🌐 Alternative: Gradio Web Interface
+### Run this cell for a shareable web interface
+"""
+
+# Install required packages for image processing and OCR
+!pip install -q pytesseract opencv-python pillow easyocr
+!apt-get install -q tesseract-ocr
+
+import gradio as gr
+import torch
+from PIL import Image
+import pytesseract
+import cv2
+import numpy as np
+import easyocr
+import io
+
+# First, make sure you have the examples dictionary defined
+examples = {
+    'chest_pain': """Patient: John Smith, 45-year-old male
+Chief Complaint: Chest pain for 2 hours
+History: Patient reports sudden onset of sharp chest pain while at work. Pain is 7/10 intensity, located substernal, radiating to left arm. Associated with shortness of breath and diaphoresis. No previous cardiac history. Denies nausea or vomiting.
+Physical Exam: VS: BP 150/90, HR 110, RR 22, O2 Sat 96% on RA. Patient appears anxious and diaphoretic. Heart: Regular rhythm, no murmurs. Lungs: Clear bilaterally. Extremities: No edema.
+Assessment: Acute chest pain, rule out myocardial infarction
+Plan: EKG, cardiac enzymes, chest X-ray, aspirin 325mg, continuous cardiac monitoring""",
+
+    'diabetes': """Patient: Maria Garcia, 52-year-old female
+Chief Complaint: Increased thirst and frequent urination for 3 weeks
+History: Patient reports polyuria, polydipsia, and unintentional weight loss of 10 lbs over past month. Family history of diabetes. Denies fever, abdominal pain, or vision changes.
+Physical Exam: VS: BP 140/85, HR 88, RR 16, BMI 28. Patient appears well but slightly dehydrated. HEENT: Dry mucous membranes. Cardiovascular: Regular rate and rhythm. Extremities: No diabetic foot changes noted.
+Assessment: New onset diabetes mellitus, likely Type 2
+Plan: HbA1c, fasting glucose, comprehensive metabolic panel, diabetic education, metformin initiation""",
+
+    'pediatric': """Patient: Emma Thompson, 8-year-old female
+Chief Complaint: Fever and sore throat for 2 days
+History: Mother reports fever up to 102°F, sore throat, difficulty swallowing, and decreased appetite. No cough or runny nose. Several classmates have been sick with similar symptoms.
+Physical Exam: VS: T 101.8°F, HR 110, RR 20, O2 Sat 99%. Patient appears mildly ill but alert. HEENT: Throat erythematous with tonsillar exudate, anterior cervical lymphadenopathy. Heart and lungs: Normal.
+Assessment: Streptococcal pharyngitis (probable)
+Plan: Rapid strep test, throat culture, amoxicillin if positive, supportive care, return if worsening"""
+}
+
+# Initialize EasyOCR reader (better for handwritten text)
+try:
+    ocr_reader = easyocr.Reader(['en'])
+    print("✅ EasyOCR initialized successfully")
+except:
+    ocr_reader = None
+    print("⚠️ EasyOCR not available, using Tesseract only")
+
+def preprocess_image_for_ocr(image):
+    """
+    Preprocess image to improve OCR accuracy
+    """
+    # Convert PIL Image to numpy array
+    img_array = np.array(image)
+
+    # Convert to grayscale if needed
+    if len(img_array.shape) == 3:
+        gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+    else:
+        gray = img_array
+
+    # Apply image preprocessing for better OCR
+    # 1. Resize image if too small
+    height, width = gray.shape
+    if height < 300 or width < 300:
+        scale_factor = max(300/height, 300/width)
+        new_width = int(width * scale_factor)
+        new_height = int(height * scale_factor)
+        gray = cv2.resize(gray, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
+
+    # 2. Noise removal
+    denoised = cv2.medianBlur(gray, 3)
+
+    # 3. Contrast enhancement
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    enhanced = clahe.apply(denoised)
+
+    # 4. Thresholding
+    _, thresh = cv2.threshold(enhanced, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+    return thresh
+
+def extract_text_from_image(image):
+    """
+    Extract text from image using multiple OCR methods
+    """
+    if image is None:
+        return "❌ No image provided"
+
+    try:
+        # Preprocess image
+        processed_img = preprocess_image_for_ocr(image)
+
+        # Method 1: Try EasyOCR (better for handwritten text)
+        if ocr_reader is not None:
+            try:
+                # Convert back to PIL Image for EasyOCR
+                pil_img = Image.fromarray(processed_img)
+                results = ocr_reader.readtext(np.array(pil_img))
+
+                # Extract text from EasyOCR results
+                easyocr_text = ' '.join([result[1] for result in results])
+
+                if len(easyocr_text.strip()) > 20:  # If we got good results
+                    return clean_extracted_text(easyocr_text)
+
+            except Exception as e:
+                print(f"EasyOCR failed: {e}")
+
+        # Method 2: Tesseract OCR (fallback)
+        try:
+            # Configure Tesseract for medical text
+            custom_config = r'--oem 3 --psm 6'
+            tesseract_text = pytesseract.image_to_string(processed_img, config=custom_config)
+
+            if len(tesseract_text.strip()) > 10:
+                return clean_extracted_text(tesseract_text)
+
+        except Exception as e:
+            print(f"Tesseract failed: {e}")
+
+        return "❌ Could not extract text from image. Please try a clearer image or enter text manually."
+
+    except Exception as e:
+        return f"❌ Error processing image: {str(e)}"
+
+def clean_extracted_text(text):
+    """
+    Clean up extracted text
+    """
+    # Remove excessive whitespace and empty lines
+    lines = [line.strip() for line in text.split('\n') if line.strip()]
+    cleaned_text = '\n'.join(lines)
+
+    # Remove special characters that might interfere
+    cleaned_text = cleaned_text.replace('|', '').replace('_', ' ')
+
+    return cleaned_text.strip()
+
+def gradio_generate_soap(medical_notes, uploaded_image):
+    """
+    Modified Gradio interface function for SOAP generation from images
+    """
+    text_to_process = medical_notes.strip() if medical_notes else ""
+
+    # If image is uploaded, extract text using OCR
+    if uploaded_image is not None:
+        try:
+            print("🔍 Extracting text from uploaded image...")
+            extracted_text = extract_text_from_image(uploaded_image)
+
+            # Check if OCR was successful
+            if extracted_text.startswith("❌"):
+                return extracted_text
+
+            # Use extracted text if manual text is empty or append to manual text
+            if not text_to_process:
+                text_to_process = extracted_text
+            else:
+                text_to_process = f"{text_to_process}\n\n--- Extracted from image ---\n{extracted_text}"
+
+        except Exception as e:
+            return f"❌ Error processing image: {str(e)}"
+
+    if not text_to_process:
+        return "❌ Please enter medical notes manually or upload a PNG/JPG image with medical text"
+
+    # Check if generate_soap_note function exists
+    if 'generate_soap_note' not in globals():
+        return "❌ Error: generate_soap_note function not found. Please define it first."
+
+    try:
+        return generate_soap_note(text_to_process)
+    except Exception as e:
+        return f"❌ Error generating SOAP note: {str(e)}"
+
+# Create example images (you can replace these with actual medical note images)
+def create_example_image(text, filename):
+    """
+    Create example images from text (for demonstration)
+    """
+    from PIL import Image, ImageDraw, ImageFont
+
+    # Create a white image
+    img = Image.new('RGB', (800, 600), color='white')
+    draw = ImageDraw.Draw(img)
+
+    try:
+        # Try to use a default font
+        font = ImageFont.load_default()
+    except:
+        font = None
+
+    # Add text to image
+    lines = text.split('\n')
+    y_offset = 20
+    for line in lines[:15]:  # Limit to first 15 lines
+        draw.text((20, y_offset), line, fill='black', font=font)
+        y_offset += 25
+
+    return img
+
+# Create Gradio interface
+gradio_interface = gr.Interface(
+    fn=gradio_generate_soap,
+    inputs=[
+        gr.Textbox(
+            lines=6,
+            placeholder="Enter medical notes manually (optional)...\n\nOr upload an image below and text will be extracted automatically.",
+            label="📝 Medical Notes (Manual Entry)"
+        ),
+        gr.Image(
+            type="pil",
+            label="📷 Upload Medical Image (PNG/JPG only)",
+            sources=["upload", "webcam"],  # FIXED: Changed "camera" to "webcam"
+            image_mode="RGB"
+        )
+    ],
+    outputs=[
+        gr.Textbox(
+            lines=15,
+            label="📋 Generated SOAP Note",
+            show_copy_button=True
+        )
+    ],
+    title="🏥 Medical Image SOAP Note Generator",
+    description="""
+    Transform medical images (PNG/JPG) into professional SOAP documentation using OCR + Gemma 3N model.
+
+    📸 **How to use:**
+    1. Upload a PNG or JPG image of medical notes (typed or handwritten)
+    2. Or enter text manually in the text box above
+    3. The system will extract text from images using OCR
+    4. Generate structured SOAP notes automatically
+
+    💡 **Tips for better OCR results:**
+    - Use clear, high-resolution images
+    - Ensure good lighting and contrast
+    - Keep text horizontal (not tilted)
+    - Handwritten text works best when clearly written
+    """,
+    examples=[
+        [examples['chest_pain'], None],
+        [examples['diabetes'], None],
+        [examples['pediatric'], None]
+    ],
+    theme=gr.themes.Soft(),
+    flagging_mode="never"
+)
+
+# Launch Gradio interface with flexible port selection
+print("🚀 Launching Medical Image SOAP Generator...")
+
+try:
+    # Try different ports if 7860 is busy
+    for port in [7860, 7861, 7862, 7863, 7864]:
+        try:
+            gradio_interface.launch(
+                share=True,  # Creates a public shareable link
+                server_port=port,
+                show_error=True,
+                quiet=False
+            )
+            print(f"✅ Interface launched successfully on port {port}")
+            break
+        except OSError as port_error:
+            print(f"⚠️ Port {port} is busy, trying next port...")
+            continue
+    else:
+        # If all ports are busy, let Gradio choose automatically
+        print("🔄 All preferred ports busy, letting Gradio choose automatically...")
+        gradio_interface.launch(
+            share=True,
+            show_error=True,
+            quiet=False
+        )
+
+except Exception as e:
+    print(f"❌ Error launching Gradio interface: {str(e)}")
+    print("💡 Alternative: Try running without share=True:")
+    print("gradio_interface.launch(show_error=True)")
+
+print("🎯 Medical Image SOAP Generator ready!")
+print("📸 Upload PNG/JPG images of medical notes for automatic text extraction and SOAP generation")
+
+"""## 📊 Usage Statistics & Model Info"""
+
+# Display model and system information
+import psutil
+import GPUtil
+
+def show_system_info():
+    print("🔧 SYSTEM INFORMATION")
+    print("="*50)
+    print(f"🖥️  Device: {device.upper()}")
+    print(f"🧠 CPU Usage: {psutil.cpu_percent(interval=1):.1f}%")
+    print(f"💾 RAM Usage: {psutil.virtual_memory().percent:.1f}%")
+
+    if torch.cuda.is_available():
+        try:
+            gpus = GPUtil.getGPUs()
+            if gpus:
+                gpu = gpus[0]
+                print(f"🚀 GPU: {gpu.name}")
+                print(f"📊 GPU Usage: {gpu.load*100:.1f}%")
+                print(f"🔥 GPU Memory: {gpu.memoryUsed}/{gpu.memoryTotal} MB ({gpu.memoryPercent:.1f}%)")
+                print(f"🌡️  GPU Temp: {gpu.temperature}°C")
+        except:
+            print(f"🚀 GPU Memory: {torch.cuda.memory_allocated()/1e9:.1f}GB / {torch.cuda.memory_reserved()/1e9:.1f}GB")
+
+    print("\n🤖 MODEL INFORMATION")
+    print("="*50)
+    print(f"📡 Model ID: {model_id}")
+    print(f"🎯 Model Type: Multimodal (Text, Image, Audio)")
+    print(f"📊 Model Size: ~2.9GB")
+    print(f"🔢 Parameters: ~2.9B")
+    print(f"🌍 Languages: 140 text + 35 multimodal")
+    print(f"💽 Precision: {model.dtype}")
+
+    print("\n✅ Ready for SOAP note generation!")
+
+show_system_info()
+
+"""---
+## 📋 SOAP Note Format Reference
+
+**S - SUBJECTIVE**: Patient's reported symptoms and history  
+**O - OBJECTIVE**: Observable clinical findings  
+**A - ASSESSMENT**: Clinical diagnosis/impression  
+**P - PLAN**: Treatment and follow-up plan  
+
+---
+*🤖 Powered by Google's Gemma 3N Model | 🔒 All processing performed locally*
+"""
+
+!gradio deploy