Update app.py

app.py

@@ -3,6 +3,8 @@ 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")
@@ -25,6 +27,9 @@ disease_details = {
     "diabetes": {"medication": "Metformin or insulin", "precaution": "Monitor sugar levels", "doctor": "Endocrinologist"},
 }
 
+# Passwords
+doctor_password = "doctor123"
+
 # Functions
 def register_patient(name, age, gender, password):
     patient_id = len(patients_db) + 1
@@ -62,55 +67,138 @@ def extract_pdf_report(pdf):
             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']}\n👩‍⚕️ 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"💊 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"📋 Diagnosis: {patient['Diagnosis']}\n"
+                    f"💊 Medications: {patient['Medications']}\n"
+                    f"⚠️ Precautions: {patient['Precautions']}\n"
+                    f"👩‍⚕️ 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"📋 Diagnosis: {patient['Diagnosis']}\n"
+                       f"💊 Medications: {patient['Medications']}\n"
+                       f"⚠️ Precautions: {patient['Precautions']}\n"
+                       f"👩‍⚕️ 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")
     
-    # Shared state for extracted text
-    extracted_text_state = gr.State("")
-    
     with gr.Tab("Patient Registration"):
-        name = gr.Textbox(label="Patient Name")
-        age = gr.Number(label="Age")
-        gender = gr.Radio(label="Gender", choices=["Male", "Female", "Other"])
-        password = gr.Textbox(label="Set Password", type="password")
-        register_button = gr.Button("Register")
-        register_output = gr.Textbox(label="Output")
-        
-        register_button.click(
-            fn=register_patient,
-            inputs=[name, age, gender, password],
-            outputs=register_output
-        )
+        registration_interface.render()
+    
+    with gr.Tab("Analyze Medical Report"):
+        report_analysis_interface.render()
     
     with gr.Tab("Extract PDF Report"):
-        pdf_file = gr.File(label="Upload PDF")
-        extract_button = gr.Button("Extract")
-        extract_output = gr.Textbox(label="Extracted Text")
-        
-        extract_button.click(
-            fn=extract_pdf_report,
-            inputs=pdf_file,
-            outputs=[extract_output, extracted_text_state]
-        )
+        pdf_extraction_interface.render()
     
-    with gr.Tab("Analyze Medical Report"):
-        patient_id = gr.Number(label="Patient ID")
-        report_text = gr.Textbox(label="Report Text")
-        analyze_button = gr.Button("Analyze")
-        analyze_output = gr.Textbox(label="Output")
-        
-        analyze_button.click(
-            fn=analyze_report,
-            inputs=[patient_id, report_text],
-            outputs=analyze_output
-        )
-        
-        # Autofill extracted text into Analyze Medical Report tab
-        extract_button.click(
-            fn=lambda extracted_text: extracted_text,
-            inputs=extracted_text_state,
-            outputs=report_text
-        )
+    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)