Update app.py

app.py

@@ -3,7 +3,6 @@ import tensorflow as tf
 import pdfplumber
 from transformers import pipeline
 import timm
-import torch
 
 # Load pre-trained zero-shot model for text classification
 classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
@@ -26,9 +25,6 @@ disease_details = {
     "diabetes": {"medication": "Metformin or insulin", "precaution": "Monitor sugar levels", "doctor": "Endocrinologist"},
 }
 
-# Password for doctor dashboard
-doctor_password = "doctor123"
-
 # Functions
 def register_patient(name, age, gender, password):
     patient_id = len(patients_db) + 1
@@ -66,45 +62,13 @@ 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."
-
 # 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")
@@ -144,10 +108,9 @@ with gr.Blocks() as app:
         
         # Autofill extracted text into Analyze Medical Report tab
         extract_button.click(
-            fn=None,
-            inputs=None,
-            outputs=[report_text],
-            _js="(x) => x"
+            fn=lambda extracted_text: extracted_text,
+            inputs=extracted_text_state,
+            outputs=report_text
         )
 
 app.launch(share=True)