Update app.py

app.py

@@ -1,11 +1,21 @@
 import gradio as gr
 import tensorflow as tf
-import pandas as pd
+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 = []
 
@@ -50,6 +60,23 @@ def analyze_report(patient_id, report_text):
             patient.update(Diagnosis=diagnosis, Medications=medication, Precautions=precaution, Doctor=doctor)
     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:
@@ -104,6 +131,18 @@ report_analysis_interface = gr.Interface(
     outputs="text",
 )
 
+pdf_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",
+)
+
 doctor_space_interface = gr.Interface(
     fn=doctor_space,
     inputs=gr.Number(label="Patient ID"),
@@ -133,11 +172,15 @@ doctor_dashboard_interface = gr.Interface(
 
 # Gradio App Layout
 with gr.Blocks() as app:
-    gr.Markdown("# Medical System")
-    with gr.Tab("Register Patient"):
+    gr.Markdown("# Medical Analyzer and Eye Disease Detection")
+    with gr.Tab("Patient Registration"):
         registration_interface.render()
-    with gr.Tab("Analyze Report"):
+    with gr.Tab("Analyze Medical Report"):
         report_analysis_interface.render()
+    with gr.Tab("Extract PDF Report"):
+        pdf_extraction_interface.render()
+    with gr.Tab("Detect Eye Disease"):
+        eye_disease_interface.render()
     with gr.Tab("Doctor Space"):
         doctor_space_interface.render()
     with gr.Tab("Pharmacist Space"):