Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+
+from utils import get_image_arrays, get_image_predictions, show_image
+
+st.title('Hateful Memes Classification')
+image_path = './'
+demo_data_file = 'demo_data.csv'
+demo_data = pd.read_csv('demo_data.csv')
+TFLITE_FILE_PATH = 'image_model.tflite'
+
+demo_data = demo_data.sample(1)
+y_true = demo_data['label']
+image_id = demo_data['image_id']
+text = demo_data['text']
+
+image_id_dict = dict(image_id).values()
+image_id_string = list(image_id_dict)[0]
+st.write('Meme:')
+st.image(image_path+image_id_string)
+
+# Image Unimodel
+image_array = get_image_arrays(image_id, image_path)
+image_prediction = get_image_predictions(image_array, TFLITE_FILE_PATH)
+y_pred_image = np.argmax(image_prediction, axis=1)
+print('Image Prediction Probabilities:')
+print(image_prediction)
+
+# TFIDF Model
+model = 'tfidf_model.pickle'
+vectorizer = 'tfidf_vectorizer.pickle'
+tfidf_model = pickle.load(open(model, 'rb'))
+tfidf_vectorizer = pickle.load(open(vectorizer, 'rb'))
+transformed_text = tfidf_vectorizer.transform(text)
+text_prediction = tfidf_model.predict_proba(transformed_text)
+y_pred_text = np.argmax(text_prediction, axis=1)
+print('Text Prediction Probabilities:')
+print(text_prediction)
+
+# Ensemble Probabilities
+ensemble_prediction = np.mean(np.array([image_prediction, text_prediction]), axis=0)
+y_pred_ensemble = np.argmax(ensemble_prediction, axis=1)
+print(ensemble_prediction)
+
+# StreamLit Display
+st.write('Image Model Predictions:')
+st.write(np.round(np.array(image_prediction), 4))
+
+st.write('Text Model Predictions:')
+st.write(np.round(np.array(text_prediction), 4))
+
+st.write('Ensemble Model Predictions:')
+st.write(np.round(np.array(ensemble_prediction), 4))
+
+true_label = list(dict(y_true).values())[0]
+predicted_label = y_pred_ensemble[0]
+
+st.write('True Label', true_label)
+st.write('Predicted Label', predicted_label)
+st.write('0: non-hateful, 1: hateful')
+
+st.button('Random Meme')