Delete pipeline.py

pipeline.py

@@ -1,51 +0,0 @@
-import numpy as np
-import tensorflow as tf
-import keras
-import json
-from keras.preprocessing.sequence import pad_sequences
-from huggingface_hub import hf_hub_download
-
-# Download model
-model_path = hf_hub_download(repo_id="NightPrince/Toxic_Classification", filename="toxic_classifier.keras")
-
-# Download tokenizer
-tokenizer_path = hf_hub_download(repo_id="NightPrince/Toxic_Classification", filename="tokenizer.json")
-
-# Load model
-model = keras.saving.load_model(model_path)
-
-# Load tokenizer
-from keras.preprocessing.text import tokenizer_from_json
-with open(tokenizer_path, "r", encoding="utf-8") as f:
-    tokenizer_json = f.read()
-    tokenizer = tokenizer_from_json(tokenizer_json)
-
-# Label map (same as config)
-label_map = {
-    0: "Child Sexual Exploitation",
-    1: "Elections",
-    2: "Non-Violent Crimes",
-    3: "Safe",
-    4: "Sex-Related Crimes",
-    5: "Suicide & Self-Harm",
-    6: "Unknown S-Type",
-    7: "Violent Crimes",
-    8: "Unsafe"
-}
-
-# Pipeline function
-def classify_toxic(query, image_description):
-    max_len = 150  # Keep it same as training
-    text = query + " " + image_description
-    seq = tokenizer.texts_to_sequences([text])
-    pad = pad_sequences(seq, maxlen=max_len, padding='post', truncating='post')
-    pred = model.predict(pad, verbose=0)
-    pred_label = np.argmax(pred, axis=1)[0]
-    return label_map.get(pred_label, "Unknown")
-
-# Example usage
-if __name__ == "__main__":
-    query = "This is a dangerous post"
-    image_desc = "Knife shown in the image"
-    result = classify_toxic(query, image_desc)
-    print("Predicted Category:", result)