Upload 2 files

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from tensorflow import keras
+import pandas as pd
+import tensorflow as tf
+import nltk
+import spacy
+import re
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+import spacy.cli
+spacy.cli.download("en_core_web_sm")
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+stop_words = set(stopwords.words('english'))
+nlp = spacy.load('en_core_web_sm')
+
+# Available backend options are: "jax", "torch", "tensorflow".
+import os
+os.environ["KERAS_BACKEND"] = "jax"
+	
+import keras
+
+model = keras.saving.load_model("hf://ARI-HIPA-AI-Team/keras_model")
+
+def preprocess_text(text):
+    text = re.sub(r'[^a-zA-Z0-9\s]', '', text)  # Only remove non-alphanumeric characters except spaces
+
+    # Tokenize and remove stopwords
+    tokens = word_tokenize(text.lower())
+    tokens = [word for word in tokens if word not in stop_words]
+
+    # Lemmatize
+    doc = nlp(' '.join(tokens))
+    lemmas = [token.lemma_ for token in doc]
+    return ' '.join(lemmas)
+
+def predict(text):
+    inputs = preprocess_text(text)
+    outputs = model(inputs)
+    return "This text is a violation = " + outputs
+
+demo = gr.Interface(fn=predict, inputs="text", outputs="text")
+demo.launch()   

requirements.txt

@@ -0,0 +1,5 @@
+huggingface_hub==0.25.2
+transformers
+tensorflow
+nltk
+spacy