Update app.py

app.py

@@ -6,15 +6,15 @@ 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")
+# Load pre-trained zero-shot model for text classification (using PyTorch for compatibility)
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli", framework="pt")
 
-# Pre-trained ResNet50 model for X-ray or image analysis
+# Pre-trained ResNet50 model for X-ray or image analysis using Timm
 image_model = timm.create_model('resnet50', pretrained=True)
 image_model.eval()
 
 # Load saved TensorFlow eye disease detection model (TensorFlow Model without Keras)
-eye_model = tf.saved_model.load('model')
+eye_model = tf.keras.models.load_model('/content/model.h5')
 
 # Patient database
 patients_db = []
@@ -30,10 +30,9 @@ disease_details = {
 # Passwords
 doctor_password = "doctor123"
 
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
+# Loading the custom model for consultation with the doctor
 try:
-    # Force using the slow tokenizer
+    # Force using the slow tokenizer for compatibility
     tokenizer = AutoTokenizer.from_pretrained("harishussain12/PastelMed", use_fast=False)
 except Exception as e:
     print(f"Tokenizer error: {e}")
@@ -47,7 +46,8 @@ def consult_doctor(prompt):
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return response
 
-# Functions
+# Functions for the app
+
 def register_patient(name, age, gender, password):
     patient_id = len(patients_db) + 1
     patients_db.append({