app.py

import gradio as gr
import random, math
from transformers import pipeline
from PyPDF2 import PdfReader

##############################
# Helper Functions
##############################

def sigmoid(x):
    return 1 / (1 + math.exp(-x))

def extract_pdf_text(pdf_file):
    """Extract text from an uploaded PDF file using PyPDF2."""
    try:
        reader = PdfReader(pdf_file.name)  # pdf_file.name gives the filename in Colab
        text = ""
        for page in reader.pages:
            text += page.extract_text()
        return text
    except Exception as e:
        return f"Error extracting PDF text: {e}"

##############################
# Callback to update visibility of "Other" fields
##############################

def update_visibility(choice):
    if choice == "Other":
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)

##############################
# Prediction function for non-MRI data (with PDF extraction)
##############################

def predict_without_mri(
    # Section A: Patient Demographics
    age, gender, ethnicity, education, pincode, mobile,
    # Section B: Medical History
    family_history, family_relationship, conditions, medications,
    # Section C: Basic Cognitive Assessment
    basic_memory_changes, basic_memory_desc, basic_difficulty_recent,
    basic_trouble_words, basic_problem_solving, basic_trouble_appointments,
    basic_changes_reaction, basic_driving_concern,
    # Section IV: Lifestyle Factors
    smoking_status, alcohol_consumption, physical_activity,
    # Section E: Physical & Neurological Exam (using "Other" options)
    reflexes_choice, reflexes_other,
    muscle_tone_choice, muscle_tone_other,
    coordination_choice, coordination_other,
    balance_choice, balance_other,
    sight_hearing_choice, sight_hearing_other,
    # Section V: Detailed Cognitive & Functional Assessment
    # A. Memory
    mem_diff_recent, mem_forget_appointments, mem_repeat, mem_learning,
    # B. Language
    lang_find_words, lang_understand, lang_follow,
    # C. Executive Function
    exec_plan, exec_decide, exec_finance,
    # D. Visuospatial Skills
    visuospatial_judge, visuospatial_navigate, visuospatial_recognize,
    # E. Attention and Concentration
    attention_focus, attention_distracted, attention_instructions,
    # F. Functional Abilities
    func_ADL, func_medications, func_transport,
    # Section VI: Behavioral and Emotional Changes
    behavior_mood_change, behavior_mood_change_desc, behavior_anxiety, behavior_irritability, behavior_loss_interest,
    # Section VII: Additional Comments
    additional_comments,
    # Section G: Blood-Based Biomarkers
    p_tau217, abeta42, abeta40, miRNAs,
    # Section H: Basic Cognitive & Functional Assessment Scores
    mmse, adas_cog, faq, ravlt,
    # PDF Report Upload (new)
    pdf_report
):
    # Convert numeric strings to floats
    try:
        mmse = float(mmse)
    except:
        mmse = 0.0
    try:
        adas = float(adas_cog)
    except:
        adas = 0.0
    try:
        p_tau217 = float(p_tau217)
    except:
        p_tau217 = 0.0
    try:
        abeta42 = float(abeta42)
    except:
        abeta42 = 0.0
    try:
        abeta40 = float(abeta40)
    except:
        abeta40 = 1.0  # avoid division by zero
    try:
        abeta_ratio = abeta42 / abeta40
    except:
        abeta_ratio = 0.0

    # Dummy calculative model with illustrative coefficients
    w_age = 0.05
    w_education = -0.1
    w_mmse = -0.05
    w_adas = 0.05
    w_ptau = 0.02
    w_abeta_ratio = 0.5
    bias = -5.0

    risk_value = (w_age * age + w_education * education + w_mmse * mmse +
                  w_adas * adas + w_ptau * p_tau217 + w_abeta_ratio * abeta_ratio + bias)
    risk_probability = sigmoid(risk_value)
    
    if risk_probability < 0.33:
        risk_category = "Low Risk"
    elif risk_probability < 0.66:
        risk_category = "Moderate Risk"
    else:
        risk_category = "High Risk"
    
    # Extract text from PDF if provided
    if pdf_report is not None:
        pdf_text = extract_pdf_text(pdf_report)
    else:
        pdf_text = "No PDF report provided."

    result = f"""### Prediction Result: {risk_category}
Calculated Risk Probability: {risk_probability*100:.2f}%

**Section A: Patient Demographics**
- Age: {age}
- Gender: {gender}
- Ethnicity: {ethnicity}
- Years of Education: {education}
- Pincode: {pincode}
- Mobile Number: {mobile}

**Section B: Medical History**
- Family history of Alzheimer's/dementia: {family_history}
- Relationship: {family_relationship if family_history == "Yes" else "N/A"}
- Conditions: {', '.join(conditions) if conditions else 'None'}
- Current Medications: {medications}

**Section C: Basic Cognitive Assessment**
- Recent changes in memory: {basic_memory_changes}
- Description: {basic_memory_desc}
- Difficulty remembering recent events: {basic_difficulty_recent}
- Trouble finding the right words: {basic_trouble_words}
- Difficulty with problem-solving: {basic_problem_solving}
- Trouble with appointments/medications: {basic_trouble_appointments}
- Changes in reactions: {basic_changes_reaction}
- Concerns about driving: {basic_driving_concern}

**Section IV: Lifestyle Factors**
- Smoking status: {smoking_status}
- Alcohol consumption: {alcohol_consumption}
- Physical activity level: {physical_activity}

**Section E: Physical & Neurological Exam**
- Reflexes: {reflexes_choice if reflexes_choice != "Other" else reflexes_other}
- Muscle tone and strength: {muscle_tone_choice if muscle_tone_choice != "Other" else muscle_tone_other}
- Coordination: {coordination_choice if coordination_choice != "Other" else coordination_other}
- Balance: {balance_choice if balance_choice != "Other" else balance_other}
- Sense of sight and hearing: {sight_hearing_choice if sight_hearing_choice != "Other" else sight_hearing_other}

**Section V: Detailed Cognitive & Functional Assessment**
*A. Memory*
- Difficulty remembering recent events: {mem_diff_recent}
- Forget appointments or important dates: {mem_forget_appointments}
- Repeat questions or statements: {mem_repeat}
- Trouble learning new information: {mem_learning}

*B. Language*
- Trouble finding the right words: {lang_find_words}
- Difficulty understanding what people say: {lang_understand}
- Trouble following conversations: {lang_follow}

*C. Executive Function*
- Difficulty planning/organizing tasks: {exec_plan}
- Trouble making decisions/solving problems: {exec_decide}
- Hard to manage finances: {exec_finance}

*D. Visuospatial Skills*
- Difficulty judging distances: {visuospatial_judge}
- Trouble finding your way around familiar places: {visuospatial_navigate}
- Difficulty recognizing faces: {visuospatial_recognize}

*E. Attention and Concentration*
- Difficulty focusing: {attention_focus}
- Easily distracted: {attention_distracted}
- Trouble following instructions: {attention_instructions}

*F. Functional Abilities*
- Need assistance with ADLs: {func_ADL}
- Difficulty managing medications: {func_medications}
- Difficulty driving/using public transport: {func_transport}

**Section VI: Behavioral and Emotional Changes**
- Changes in mood or personality: {behavior_mood_change}
- Description of mood changes: {behavior_mood_change_desc if behavior_mood_change == "Yes" else "N/A"}
- More anxious or depressed: {behavior_anxiety}
- More irritable or agitated: {behavior_irritability}
- Lost interest in activities: {behavior_loss_interest}

**Section VII: Additional Comments**
{additional_comments}

**Section G: Blood-Based Biomarkers**
- p-tau217: {p_tau217}
- Aβ42: {abeta42}
- Aβ40: {abeta40}
- miRNAs: {miRNAs}

**Section H: Basic Cognitive & Functional Assessment Scores**
- MMSE: {mmse}
- ADAS-Cog: {adas_cog}
- FAQ: {faq}
- RAVLT: {ravlt}

**Uploaded PDF Report Details:**
{pdf_text}
"""
    return result

##############################
# Prediction function for MRI using a Hugging Face pipeline
##############################
pipe = pipeline("image-classification", model="evanrsl/resnet-Alzheimer")
#pipe = pipeline("image-classification", model="evanrsl/resnet-Alzheimer")
#def predict_with_mri(image):
def predict_alzheimer(image, age, gender, ethnicity):
    """
    Predict Alzheimer’s status from an uploaded image using the pretrained pipeline,
    and include demographic information (age, gender, ethnicity) in the output.
    """
    # Run the pipeline on the input image
    results = pipe(image)
    # Assume the pipeline returns a list of dictionaries; we'll take the top result.
    print(results)
    label = results[0]['label']
    confidence = results[0]['score'] * 100  # convert to percentage

    # Create output text combining prediction and demographic info
    output_text = (f"**Prediction:** {label}\n"
                   f"**Confidence:** {confidence:.2f}%\n\n"
                   f"**Demographics:**\n"
                   f"- Age: {age}\n"
                   f"- Gender: {gender}\n"
                   f"- Ethnicity: {ethnicity}\n"
                   f"Note: Prediction may be Non-Demented to High-Demented\n Confidence suggests that how the model is sure of this prediction")
    return output_text


##############################
# Gradio App Interface Configuration
##############################
with gr.Blocks() as demo:
    gr.Markdown("# ADAP System : Alzheimer Detection, Assessment & Prediction System")
    
    with gr.Tabs():
        ###########################
        # Tab: Without MRI Data   #
        ###########################
        with gr.Tab("Without MRI Data"):
            gr.Markdown("### Please fill out the following form:")
            # Section A: Patient Demographics
            with gr.Column():
                gr.Markdown("#### Section A: Patient Demographics")
                age = gr.Number(label="Age (years)", value=60)
                gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"])
                ethnicity = gr.Textbox(label="Ethnicity", placeholder="Enter ethnicity")
                education = gr.Number(label="Years of Education", value=12)
                pincode = gr.Textbox(label="Pincode", placeholder="Enter your pincode")
                mobile = gr.Textbox(label="Mobile Number", placeholder="Enter your mobile number")
            # Section B: Medical History
            with gr.Column():
                gr.Markdown("#### Section B: Medical History")
                family_history = gr.Radio(label="Do you have a family history of Alzheimer's disease or dementia?", choices=["Yes", "No"])
                family_relationship = gr.Textbox(label="If yes, please specify the relationship", placeholder="e.g., Mother, Father, Grandparent")
                conditions = gr.CheckboxGroup(label="Do you have any of the following conditions?", choices=["High blood pressure", "Heart disease", "Stroke", "Diabetes", "High cholesterol", "Head injuries"])
                medications = gr.Textbox(label="List any current medications", lines=3, placeholder="Enter current medications")
            # Section C: Basic Cognitive Assessment
            with gr.Column():
                gr.Markdown("#### Section C: Basic Cognitive Assessment")
                basic_memory_changes = gr.Radio(label="Have you noticed any recent changes in your memory?", choices=["Yes", "No"])
                basic_memory_desc = gr.Textbox(label="If yes, please describe", lines=3, placeholder="Describe any changes")
                basic_difficulty_recent = gr.Radio(label="Do you have difficulty remembering recent events?", choices=["Not at all", "Mild", "Moderate", "Severe"])
                basic_trouble_words = gr.Radio(label="Do you have trouble finding the right words?", choices=["Not at all", "Mild", "Moderate", "Severe"])
                basic_problem_solving = gr.Radio(label="Do you have difficulty with problem-solving or decision-making?", choices=["Not at all", "Mild", "Moderate", "Severe"])
                basic_trouble_appointments = gr.Radio(label="Are you having trouble remembering healthcare appointments or when to take your medicines?", choices=["Yes", "No"])
                basic_changes_reaction = gr.Radio(label="Have you noticed any changes in the way you tend to react to people or events?", choices=["Yes", "No"])
                basic_driving_concern = gr.Radio(label="Does anyone express unusual concern about your driving?", choices=["Yes", "No"])
            # Section IV: Lifestyle Factors
            with gr.Column():
                gr.Markdown("#### Section IV: Lifestyle Factors")
                smoking_status = gr.Radio(label="Smoking status", choices=["Yes", "No"])
                alcohol_consumption = gr.Dropdown(label="Alcohol consumption", choices=["None", "Occasional", "Regular", "Heavy"])
                physical_activity = gr.Dropdown(label="Physical activity level", choices=["Sedentary", "Moderate", "Active"])
            # Section E: Physical & Neurological Exam with "Other" options
            with gr.Column():
                gr.Markdown("#### Section E: Physical & Neurological Exam")
                reflexes_choice = gr.Radio(label="Reflexes", choices=["Normal", "Diminished", "Hyperactive", "Other"], value="Normal")
                reflexes_other = gr.Textbox(label="If Other, please describe", placeholder="Describe reflexes", visible=False)
                reflexes_choice.change(fn=update_visibility, inputs=reflexes_choice, outputs=reflexes_other)

                muscle_tone_choice = gr.Radio(label="Muscle tone and strength", choices=["Normal", "Reduced", "Increased", "Other"], value="Normal")
                muscle_tone_other = gr.Textbox(label="If Other, please describe", placeholder="Describe muscle tone and strength", visible=False)
                muscle_tone_choice.change(fn=update_visibility, inputs=muscle_tone_choice, outputs=muscle_tone_other)

                coordination_choice = gr.Radio(label="Coordination", choices=["Normal", "Impaired", "Other"], value="Normal")
                coordination_other = gr.Textbox(label="If Other, please describe", placeholder="Describe coordination", visible=False)
                coordination_choice.change(fn=update_visibility, inputs=coordination_choice, outputs=coordination_other)

                balance_choice = gr.Radio(label="Balance", choices=["Stable", "Impaired", "Other"], value="Stable")
                balance_other = gr.Textbox(label="If Other, please describe", placeholder="Describe balance", visible=False)
                balance_choice.change(fn=update_visibility, inputs=balance_choice, outputs=balance_other)

                sight_hearing_choice = gr.Radio(label="Sense of sight and hearing", choices=["Normal", "Impaired", "Other"], value="Normal")
                sight_hearing_other = gr.Textbox(label="If Other, please describe", placeholder="Describe any issues", visible=False)
                sight_hearing_choice.change(fn=update_visibility, inputs=sight_hearing_choice, outputs=sight_hearing_other)
            # Section V: Detailed Cognitive & Functional Assessment
            with gr.Column():
                gr.Markdown("#### Section V: Detailed Cognitive & Functional Assessment")
                gr.Markdown("**A. Memory**")
                mem_diff_recent = gr.Radio(label="Do you have difficulty remembering recent events?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                mem_forget_appointments = gr.Radio(label="Do you forget appointments or important dates more often than before?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                mem_repeat = gr.Radio(label="Do you repeat questions or statements during a conversation?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                mem_learning = gr.Radio(label="Do you have trouble learning new information?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                gr.Markdown("**B. Language**")
                lang_find_words = gr.Radio(label="Do you have trouble finding the right words to express yourself?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                lang_understand = gr.Radio(label="Do you have difficulty understanding what people are saying?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                lang_follow = gr.Radio(label="Do you have trouble following conversations?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                gr.Markdown("**C. Executive Function**")
                exec_plan = gr.Radio(label="Do you have difficulty planning and organizing tasks?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                exec_decide = gr.Radio(label="Do you have trouble making decisions or solving problems?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                exec_finance = gr.Radio(label="Do you find it hard to manage your finances?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                gr.Markdown("**D. Visuospatial Skills**")
                visuospatial_judge = gr.Radio(label="Do you have difficulty judging distances?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                visuospatial_navigate = gr.Radio(label="Do you have trouble finding your way around familiar places?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                visuospatial_recognize = gr.Radio(label="Do you have trouble recognizing faces?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                gr.Markdown("**E. Attention and Concentration**")
                attention_focus = gr.Radio(label="Do you have difficulty focusing your attention?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                attention_distracted = gr.Radio(label="Are you easily distracted?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                attention_instructions = gr.Radio(label="Do you have trouble following instructions?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                gr.Markdown("**F. Functional Abilities**")
                func_ADL = gr.Radio(label="Do you need assistance with activities of daily living (bathing, dressing, eating)?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                func_medications = gr.Radio(label="Do you have difficulty managing your medications?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
                func_transport = gr.Radio(label="Do you have difficulty driving or using public transportation?", choices=["Not at all", "Mildly", "Moderately", "Severely"])
            # Section VI: Behavioral and Emotional Changes
            with gr.Column():
                gr.Markdown("#### Section VI: Behavioral and Emotional Changes")
                behavior_mood_change = gr.Radio(label="Have you noticed any changes in your mood or personality?", choices=["Yes", "No"])
                behavior_mood_change_desc = gr.Textbox(label="If Yes, please describe", lines=3, placeholder="Describe changes")
                behavior_anxiety = gr.Radio(label="Do you feel more anxious or depressed than usual?", choices=["Yes", "No"])
                behavior_irritability = gr.Radio(label="Are you more irritable or agitated than usual?", choices=["Yes", "No"])
                behavior_loss_interest = gr.Radio(label="Have you lost interest in activities you used to enjoy?", choices=["Yes", "No"])
            # Section VII: Additional Comments
            with gr.Column():
                gr.Markdown("#### Section VII: Additional Comments")
                additional_comments = gr.Textbox(label="Please provide any additional comments", lines=3, placeholder="Enter additional comments here")
            # Section G: Blood-Based Biomarkers
            with gr.Column():
                gr.Markdown("#### Section G: Blood-Based Biomarkers")
                p_tau217 = gr.Textbox(label="p-tau217 (pg/mL)", placeholder="Enter value")
                abeta42 = gr.Textbox(label="Aβ42 (pg/mL)", placeholder="Enter value")
                abeta40 = gr.Textbox(label="Aβ40 (pg/mL)", placeholder="Enter value")
                miRNAs = gr.Textbox(label="miRNAs (List with values)", lines=2, placeholder="e.g., miR-1: 2.5, miR-2: 3.0")
            # Section H: Basic Cognitive & Functional Assessment Scores
            with gr.Column():
                gr.Markdown("#### Section H: Basic Cognitive & Functional Assessment Scores")
                mmse = gr.Textbox(label="MMSE Score", placeholder="Enter MMSE score")
                adas_cog = gr.Textbox(label="ADAS-Cog Score", placeholder="Enter ADAS-Cog score")
                faq = gr.Textbox(label="FAQ Score", placeholder="Enter FAQ score")
                ravlt = gr.Textbox(label="RAVLT Score", placeholder="Enter RAVLT score")
            # Section: Upload PDF Report for additional details
            with gr.Column():
                gr.Markdown("#### Additional Document (Optional)")
                pdf_report = gr.File(label="Upload PDF Report (optional)", file_types=['.pdf'])
            
            predict_btn_no_mri = gr.Button("Predict Alzheimer's Risk")
            output_no_mri = gr.Textbox(label="Prediction Result", lines=35)
            predict_btn_no_mri.click(
                fn=predict_without_mri,
                inputs=[
                    age, gender, ethnicity, education, pincode, mobile,
                    family_history, family_relationship, conditions, medications,
                    basic_memory_changes, basic_memory_desc, basic_difficulty_recent,
                    basic_trouble_words, basic_problem_solving, basic_trouble_appointments,
                    basic_changes_reaction, basic_driving_concern,
                    smoking_status, alcohol_consumption, physical_activity,
                    reflexes_choice, reflexes_other,
                    muscle_tone_choice, muscle_tone_other,
                    coordination_choice, coordination_other,
                    balance_choice, balance_other,
                    sight_hearing_choice, sight_hearing_other,
                    mem_diff_recent, mem_forget_appointments, mem_repeat, mem_learning,
                    lang_find_words, lang_understand, lang_follow,
                    exec_plan, exec_decide, exec_finance,
                    visuospatial_judge, visuospatial_navigate, visuospatial_recognize,
                    attention_focus, attention_distracted, attention_instructions,
                    func_ADL, func_medications, func_transport,
                    behavior_mood_change, behavior_mood_change_desc, behavior_anxiety, behavior_irritability, behavior_loss_interest,
                    additional_comments,
                    p_tau217, abeta42, abeta40, miRNAs,
                    mmse, adas_cog, faq, ravlt,
                    pdf_report
                ],
                outputs=output_no_mri
            )
        
        ###########################
        # Tab: With MRI Image      #
        ###########################
        with gr.Tab("With MRI Image"):
            gr.Markdown("# ADAP System : Alzheimer Detection, Assessment & Prediction System")
            gr.Markdown("### With MRI Data: Upload an MRI scan and provide your demographic details")

            with gr.Row():
                image_input = gr.Image(label="Upload MRI Scan", type="pil")
                age_input = gr.Number(label="Age (years)", value=65)

            gender_input = gr.Radio(label="Gender", choices=["Male", "Female", "Other"], value="Male")
            ethnicity_input = gr.Textbox(label="Ethnicity", placeholder="Enter your ethnicity")

            predict_button = gr.Button("Predict Alzheimer's")
            output_box = gr.Textbox(label="Prediction Result", lines=8)

            predict_button.click(fn=predict_alzheimer, inputs=[image_input, age_input, gender_input, ethnicity_input], outputs=output_box)

# Launch the Gradio app
demo.launch()