Update app.py

app.py

@@ -1,22 +1,18 @@
 import gradio as gr
-import pickle
 import tensorflow as tf
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 import numpy as np
-
-
 import keras
-
-
-# Load tokenizer and model
-
-#model_path = r"models/toxic_classifier.keras"
 from tensorflow.keras.preprocessing.text import tokenizer_from_json
 import json
 
-with open("tokenizer.json", "r") as f:
-    tokenizer = tokenizer_from_json(json.load(f))
+# Load tokenizer
+tokenizer_path = "tokenizer.json"
+with open(tokenizer_path, "r", encoding="utf-8") as f:
+    tokenizer_json = f.read()
+    tokenizer = tokenizer_from_json(tokenizer_json)
 
+# Load model from Hugging Face Hub
 model = keras.saving.load_model("hf://NightPrince/Toxic-Predict")
 
 # Label map
@@ -32,6 +28,7 @@ label_map = {
     8: "Unsafe"
 }
 
+# Inference function
 def classify_toxic(query, image_desc):
     max_len = 150
     text = query + " " + image_desc
@@ -41,6 +38,7 @@ def classify_toxic(query, image_desc):
     pred_label = np.argmax(pred, axis=1)[0]
     return label_map.get(pred_label, "Unknown")
 
+# Gradio interface
 iface = gr.Interface(
     fn=classify_toxic,
     inputs=[
@@ -49,8 +47,4 @@ iface = gr.Interface(
     ],
     outputs=gr.Textbox(label="Predicted Toxic Category"),
     title="Toxic Category Classifier",
-    description="Enter a query and image description to classify the prompt into one of the toxic categories"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+    description="Enter a query and image description