Update app.py

app.py

@@ -1,38 +1,26 @@
 import streamlit as st
 import tensorflow as tf
+from tf.keras.models import load_model
 from PIL import Image
 import os
-from huggingface_hub import hf_hub_url, HfApi  # Import Hugging Face utilities
+from huggingface_hub import notebook_login
+from huggingface_hub import hf_hub_download
 
 # Title of the Streamlit app
 st.title("Yellow Rust Severity Prediction")
 
-# Function to get Hugging Face API token securely
-def get_huggingface_api_token():
-    if "HUGGINGFACE_API_TOKEN" not in st.session_state:
-        st.session_state.HUGGINGFACE_API_TOKEN = st.text_input(
-            "Enter your Hugging Face API Token",
-            type="password",
-            label_visibility="collapsed"
-        )
-    return st.session_state.HUGGINGFACE_API_TOKEN
 
-# Set Hugging Face token for authentication
-huggingface_api_token = get_huggingface_api_token()
-if not huggingface_api_token:
-    st.warning("Please enter your Hugging Face API Token.")
-    st.stop()
 
-HfApi().set_access_token(huggingface_api_token)
+# Downloading Model from hugging face url
+st.write("Downloading model from Hugging Face repo:", model_repo_id)
+# Download the model file from Hugging Face
+model_path = hf_hub_download(repo_id="shaheer-data/Yellow-Rust-Prediction", filename="final_meta_model.keras")
 
-# Model repository details
-model_repo_id = "shaheer-data/Yellow-Rust-Prediction"
-model_file_path = "final_meta_model.keras"
 
 # Construct the model URL
 st.write("Loading model from Hugging Face repo:", model_repo_id)
-model_url = hf_hub_url(model_repo_id, model_file_path)
-loaded_model = tf.keras.models.load_model(model_url)  # Load model using tf.keras directly
+
+loaded_model = load_model(model_path)  # Load model using tf.keras directly
 
 # Function to make predictions
 def predict_image(image):