MED-GPT / app.py
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import gradio as gr
import tensorflow as tf
import pdfplumber
from transformers import pipeline
import timm
import torch
import pandas as pd
# Load pre-trained zero-shot model for text classification
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
# Pre-trained ResNet50 model for X-ray or image analysis
image_model = timm.create_model('resnet50', pretrained=True)
image_model.eval()
# Load saved TensorFlow eye disease detection model
#eye_model = tf.keras.models.load_model('model.h5')
# Patient database
patients_db = []
# Disease details for medical report analyzer
disease_details = {
"anemia": {
"medication": (
"Iron supplements (e.g., ferrous sulfate), "
"Vitamin B12 injections (for pernicious anemia), "
"Folic acid supplements."
),
"precaution": (
"Consume iron-rich foods like spinach, red meat, and lentils. "
"Pair iron-rich foods with vitamin C to enhance absorption. "
"Avoid tea or coffee with meals as they inhibit iron absorption."
),
"doctor": "Hematologist",
},
"viral infection": {
"medication": (
"Antiviral drugs (e.g., oseltamivir for flu, acyclovir for herpes). "
"Over-the-counter medications for symptom relief, such as ibuprofen for fever and body aches."
),
"precaution": (
"Stay hydrated by drinking plenty of fluids. "
"Isolate to prevent spreading the infection. "
"Rest adequately to support recovery. Maintain proper hygiene."
),
"doctor": "Infectious Disease Specialist",
},
"liver disease": {
"medication": (
"Hepatoprotective drugs (e.g., ursodeoxycholic acid, silymarin). "
"Antiviral therapy for viral hepatitis. "
"Diuretics for managing fluid retention (e.g., spironolactone)."
),
"precaution": (
"Avoid alcohol and hepatotoxic drugs. "
"Follow a low-fat diet and avoid processed foods. "
"Regularly monitor liver function tests."
),
"doctor": "Hepatologist",
},
"diabetes": {
"medication": (
"Oral hypoglycemics (e.g., metformin). "
"Insulin therapy for Type 1 diabetes or advanced Type 2 diabetes. "
"GLP-1 receptor agonists (e.g., liraglutide) for improving blood sugar control."
),
"precaution": (
"Monitor blood glucose levels daily. "
"Follow a low-carb, high-fiber diet. "
"Engage in regular physical activity. "
"Avoid sugary foods and beverages."
),
"doctor": "Endocrinologist",
},
"hypertension": {
"medication": (
"ACE inhibitors (e.g., lisinopril). "
"Beta-blockers (e.g., metoprolol). "
"Calcium channel blockers (e.g., amlodipine). "
"Diuretics (e.g., hydrochlorothiazide)."
),
"precaution": (
"Reduce salt intake to less than 2g per day. "
"Engage in at least 150 minutes of moderate exercise weekly. "
"Avoid smoking and excessive alcohol consumption. "
"Manage stress through relaxation techniques like yoga or meditation."
),
"doctor": "Cardiologist",
},
"pneumonia": {
"medication": (
"Antibiotics (e.g., amoxicillin or azithromycin for bacterial pneumonia). "
"Antiviral therapy if caused by viruses like influenza. "
"Supplemental oxygen in severe cases."
),
"precaution": (
"Get plenty of rest and stay hydrated. "
"Use a humidifier to ease breathing. "
"Avoid smoking or exposure to pollutants. "
"Ensure vaccination against influenza and pneumococcus."
),
"doctor": "Pulmonologist",
},
"asthma": {
"medication": (
"Short-acting bronchodilators (e.g., albuterol) for quick relief. "
"Inhaled corticosteroids (e.g., fluticasone) for long-term control. "
"Leukotriene receptor antagonists (e.g., montelukast) for reducing inflammation."
),
"precaution": (
"Avoid known allergens like pollen, dust, and pet dander. "
"Carry a rescue inhaler at all times. "
"Practice breathing exercises to strengthen lungs. "
"Avoid cold air or strenuous exercise without a warm-up."
),
"doctor": "Pulmonologist",
},
"kidney disease": {
"medication": (
"ACE inhibitors or ARBs (e.g., losartan) for controlling blood pressure. "
"Erythropoietin-stimulating agents for anemia management. "
"Phosphate binders (e.g., sevelamer) to manage high phosphate levels."
),
"precaution": (
"Limit salt, potassium, and phosphorus in the diet. "
"Stay hydrated but avoid overhydration. "
"Avoid NSAIDs and other nephrotoxic drugs. "
"Monitor kidney function and blood pressure regularly."
),
"doctor": "Nephrologist",
},
"thyroid disorder": {
"medication": (
"Levothyroxine for hypothyroidism. "
"Antithyroid medications (e.g., methimazole) for hyperthyroidism. "
"Beta-blockers for symptomatic relief in hyperthyroidism."
),
"precaution": (
"Ensure regular thyroid function tests. "
"Avoid foods that interfere with thyroid hormone absorption (e.g., soy, certain vegetables). "
"Follow medication schedules precisely without skipping doses."
),
"doctor": "Endocrinologist",
},
"arthritis": {
"medication": (
"Nonsteroidal anti-inflammatory drugs (NSAIDs) for pain relief. "
"Disease-modifying antirheumatic drugs (DMARDs) for rheumatoid arthritis. "
"Biologics (e.g., adalimumab) in severe cases."
),
"precaution": (
"Engage in low-impact exercises like swimming or yoga. "
"Use ergonomic furniture to reduce joint strain. "
"Maintain a healthy weight to reduce joint stress. "
"Apply heat or cold therapy for symptom relief."
),
"doctor": "Rheumatologist",
},
"depression": {
"medication": (
"Selective serotonin reuptake inhibitors (SSRIs, e.g., sertraline). "
"Serotonin-norepinephrine reuptake inhibitors (SNRIs, e.g., venlafaxine). "
"Tricyclic antidepressants (e.g., amitriptyline) in specific cases."
),
"precaution": (
"Engage in regular physical exercise. "
"Maintain a routine and avoid isolation. "
"Consider therapy (e.g., CBT or psychotherapy). "
"Avoid alcohol and recreational drugs."
),
"doctor": "Psychiatrist",
},
}
# Passwords
doctor_password = "doctor123"
# Functions
def register_patient(name, age, gender, password):
patient_id = len(patients_db) + 1
patients_db.append({
"ID": patient_id,
"Name": name,
"Age": age,
"Gender": gender,
"Password": password,
"Diagnosis": "",
"Medications": "",
"Precautions": "",
"Doctor": ""
})
return f"βœ… Patient {name} registered successfully. Patient ID: {patient_id}"
def analyze_report(patient_id, report_text):
candidate_labels = list(disease_details.keys())
result = classifier(report_text, candidate_labels)
diagnosis = result['labels'][0]
# Update patient's record
medication = disease_details[diagnosis]['medication']
precaution = disease_details[diagnosis]['precaution']
doctor = disease_details[diagnosis]['doctor']
for patient in patients_db:
if patient['ID'] == patient_id:
patient.update(Diagnosis=diagnosis, Medications=medication, Precautions=precaution, Doctor=doctor)
return f"πŸ” Diagnosis: {diagnosis}"
def extract_pdf_report(pdf):
text = ""
with pdfplumber.open(pdf.name) as pdf_file:
for page in pdf_file.pages:
text += page.extract_text()
return text
'''def predict_eye_disease(input_image):
input_image = tf.image.resize(input_image, [224, 224]) / 255.0
input_image = tf.expand_dims(input_image, 0)
predictions = eye_model.predict(input_image)
labels = ['Cataract', 'Conjunctivitis', 'Glaucoma', 'Normal']
confidence_scores = {labels[i]: round(predictions[0][i] * 100, 2) for i in range(len(labels))}
if confidence_scores['Normal'] > 50:
return f"Congrats! No disease detected. Confidence: {confidence_scores['Normal']}%"
return "\n".join([f"{label}: {confidence}%" for label, confidence in confidence_scores.items()])
'''
def doctor_space(patient_id):
for patient in patients_db:
if patient["ID"] == patient_id:
return f"⚠ Precautions: {patient['Precautions']}\nπŸ‘©β€βš• Recommended Doctor: {patient['Doctor']}"
return "❌ Patient not found. Please check the ID."
def pharmacist_space(patient_id):
for patient in patients_db:
if patient["ID"] == patient_id:
return f"πŸ’Š Medications: {patient['Medications']}"
return "❌ Patient not found. Please check the ID."
def patient_dashboard(patient_id, password):
for patient in patients_db:
if patient["ID"] == patient_id and patient["Password"] == password:
return (f"🩺 Name: {patient['Name']}\n"
f"πŸ“‹ Diagnosis: {patient['Diagnosis']}\n"
f"πŸ’Š Medications: {patient['Medications']}\n"
f"⚠ Precautions: {patient['Precautions']}\n"
f"πŸ‘©β€βš• Recommended Doctor: {patient['Doctor']}")
return "❌ Access Denied: Invalid ID or Password."
def doctor_dashboard(password):
if password != doctor_password:
return "❌ Access Denied: Incorrect Password"
if not patients_db:
return "No patient records available."
details = []
for patient in patients_db:
details.append(f"🩺 Name: {patient['Name']}\n"
f"πŸ“‹ Diagnosis: {patient['Diagnosis']}\n"
f"πŸ’Š Medications: {patient['Medications']}\n"
f"⚠ Precautions: {patient['Precautions']}\n"
f"πŸ‘©β€βš• Recommended Doctor: {patient['Doctor']}")
return "\n\n".join(details)
# Gradio Interfaces
registration_interface = gr.Interface(
fn=register_patient,
inputs=[
gr.Textbox(label="Patient Name"),
gr.Number(label="Age"),
gr.Radio(label="Gender", choices=["Male", "Female", "Other"]),
gr.Textbox(label="Set Password", type="password"),
],
outputs="text",
)
pdf_extraction_interface = gr.Interface(
fn=extract_pdf_report,
inputs=gr.File(label="Upload PDF Report"),
outputs="text",
)
report_analysis_interface = gr.Interface(
fn=analyze_report,
inputs=[
gr.Number(label="Patient ID"),
gr.Textbox(label="Report Text"),
],
outputs="text",
)
'''eye_disease_interface = gr.Interface(
fn=predict_eye_disease,
inputs=gr.Image(label="Upload an Eye Image", type="numpy"),
outputs="text",
)
'''
doctor_space_interface = gr.Interface(
fn=doctor_space,
inputs=gr.Number(label="Patient ID"),
outputs="text",
)
pharmacist_space_interface = gr.Interface(
fn=pharmacist_space,
inputs=gr.Number(label="Patient ID"),
outputs="text",
)
patient_dashboard_interface = gr.Interface(
fn=patient_dashboard,
inputs=[
gr.Number(label="Patient ID"),
gr.Textbox(label="Password", type="password"),
],
outputs="text",
)
doctor_dashboard_interface = gr.Interface(
fn=doctor_dashboard,
inputs=gr.Textbox(label="Doctor Password", type="password"),
outputs="text",
)
# Gradio App Layout
with gr.Blocks() as app:
gr.Markdown("# Medico GPT")
with gr.Tab("Patient Registration"):
registration_interface.render()
with gr.Tab("Analyze Medical Report"):
report_analysis_interface.render()
with gr.Tab("Extract PDF Report"):
pdf_extraction_interface.render()
''' with gr.Tab("Ophthalmologist Space"):
eye_disease_interface.render()
'''
with gr.Tab("Doctor Space"):
doctor_space_interface.render()
with gr.Tab("Pharmacist Space"):
pharmacist_space_interface.render()
with gr.Tab("Patient Dashboard"):
patient_dashboard_interface.render()
with gr.Tab("Doctor Dashboard"):
doctor_dashboard_interface.render()
app.launch(share=True)