Create app.py

app.py

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+import os
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+from transformers import pipeline
+import pdfplumber
+from PIL import Image
+import timm
+import torch
+
+# Load pre-trained zero-shot model for text classification
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# Pre-trained model for X-ray 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 325mg)",
+        "precaution": "Increase intake of iron-rich foods like spinach, red meat, and beans.",
+        "doctor": "Hematologist"
+    },
+    "viral infection": {
+        "medication": "Antiviral drugs (e.g., Oseltamivir 75mg for flu)",
+        "precaution": "Rest, stay hydrated, avoid close contact with others, and wash hands frequently.",
+        "doctor": "Infectious Disease Specialist"
+    },
+    "liver disease": {
+        "medication": "Hepatoprotective drugs (e.g., Ursodeoxycholic acid 300mg)",
+        "precaution": "Avoid alcohol and maintain a balanced diet, avoid fatty foods.",
+        "doctor": "Hepatologist"
+    },
+    "kidney disease": {
+        "medication": "Angiotensin-converting enzyme inhibitors (e.g., Lisinopril 10mg)",
+        "precaution": "Monitor salt intake, stay hydrated, and avoid NSAIDs.",
+        "doctor": "Nephrologist"
+    },
+    "diabetes": {
+        "medication": "Metformin (e.g., 500mg) or insulin therapy",
+        "precaution": "Follow a low-sugar diet, monitor blood sugar levels, and exercise regularly.",
+        "doctor": "Endocrinologist"
+    },
+    "hypertension": {
+        "medication": "Antihypertensive drugs (e.g., Amlodipine 5mg)",
+        "precaution": "Reduce salt intake, manage stress, and avoid smoking.",
+        "doctor": "Cardiologist"
+    },
+    "COVID-19": {
+        "medication": "Supportive care, antiviral drugs (e.g., Remdesivir 200mg in severe cases)",
+        "precaution": "Follow isolation protocols, wear a mask, stay hydrated, and rest.",
+        "doctor": "Infectious Disease Specialist"
+    },
+    "pneumonia": {
+        "medication": "Antibiotics (e.g., Amoxicillin 500mg or Azithromycin 250mg)",
+        "precaution": "Rest, avoid smoking, stay hydrated, and get proper ventilation.",
+        "doctor": "Pulmonologist"
+    }
+}
+
+# Functions
+def register_patient(name, age, gender):
+    patient_id = len(patients_db) + 1
+    patients_db.append({
+        "ID": patient_id,
+        "Name": name,
+        "Age": age,
+        "Gender": gender,
+        "Diagnosis": "",
+        "Medications": "",
+        "Precautions": ""
+    })
+    return f"✅ 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']
+    for patient in patients_db:
+        if patient['ID'] == patient_id:
+            patient.update(Diagnosis=diagnosis, Medications=medication, Precautions=precaution)
+    return f"🔍 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:
+            diagnosis = patient["Diagnosis"]
+            medication = patient["Medications"]
+            precaution = patient["Precautions"]
+            doctor = disease_details.get(diagnosis, {}).get("doctor", "No doctor available")
+            return (f"🩺 Patient Name: {patient['Name']}\n"
+                    f"📋 Diagnosis: {diagnosis}\n"
+                    f"💊 Medications: {medication}\n"
+                    f"⚠️ Precautions: {precaution}\n"
+                    f"👩‍⚕️ Recommended Doctor: {doctor}")
+    return "Patient not found. Please check the ID."
+
+def pharmacist_space(patient_id):
+    for patient in patients_db:
+        if patient["ID"] == patient_id:
+            diagnosis = patient["Diagnosis"]
+            medication = patient["Medications"]
+            return f"💊 Patient Name: {patient['Name']}\n📋 Prescribed Medications: {medication}"
+    return "Patient not found. Please check the ID."
+
+# 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"])], outputs="text")
+report_analysis_interface = gr.Interface(fn=analyze_report, inputs=[gr.Number(label="Patient ID"), gr.Textbox(label="Report Text")], outputs="text")
+pdf_report_extraction_interface = gr.Interface(fn=extract_pdf_report, inputs=gr.File(label="Upload PDF Report"), outputs="text")
+eye_disease_interface = gr.Interface(fn=predict_eye_disease, inputs=gr.Image(label="Upload an Eye Image", type="numpy"), outputs="text")
+dashboard_interface = gr.Interface(fn=lambda: pd.DataFrame(patients_db), inputs=None, outputs="dataframe")
+doctor_interface = gr.Interface(fn=doctor_space, inputs=gr.Number(label="Patient ID"), outputs="text")
+pharmacist_interface = gr.Interface(fn=pharmacist_space, inputs=gr.Number(label="Patient ID"), outputs="text")
+
+# Gradio App Layout
+with gr.Blocks() as app:
+    gr.Markdown("# Medical Analyzer and Eye Disease Detection")
+    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_report_extraction_interface.render()
+    with gr.Tab("Detect Eye Disease"):
+        eye_disease_interface.render()
+    with gr.Tab("Doctor Space"):
+        doctor_interface.render()
+    with gr.Tab("Pharmacist Space"):
+        pharmacist_interface.render()
+    with gr.Tab("Patient Dashboard"):
+        dashboard_interface.render()
+
+app.launch(share=True)