Upload app.py

app.py

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+import streamlit as st
+import numpy as np
+import joblib
+import easyocr
+from transformers import pipeline
+from keras.utils import load_img
+from keras.utils import img_to_array
+from PIL import Image
+import io
+
+from tempfile import NamedTemporaryFile
+
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+def load_image(image_file):
+	img = Image.open(image_file)
+	return img
+
+def get_img_prediction(imgpath):    
+    img = load_img(imgpath,target_size=(64,64,3))
+    img = img_to_array(img)
+    img = img/255
+
+    X_pred_image = np.array(img)
+
+    X_pred_imaged = X_pred_image.reshape(1,64*64*3)
+    y_pred_pro = loaded_lgbm.predict_proba(X_pred_imaged)
+    return y_pred_pro[0].tolist()
+
+
+def get_text_prediction(text,imgpath):
+    if text == '':
+        result = reader.readtext(imgpath,paragraph="False")
+        text = []
+        for i in result:
+            text.append(i[1])
+        text = " ".join(text)
+    t_pred = get_inference(text)[0]
+    t_pred_c = []
+    for c in t_pred:
+        for a in c.values():
+            if a not in ['NEGATIVE','POSITIVE']:
+                t_pred_c.append(a)
+    return t_pred_c[::-1]
+
+
+def pred_label_mean(i_pred,t_pred):
+    ensemble_pro = [(g + h) / 2 for g, h in zip(i_pred, t_pred)]
+    return ensemble_pro
+
+def get_inference(input_text):
+  return bert(input_text)
+
+
+loaded_lgbm = joblib.load('lgbm.sav')
+
+bert = pipeline("text-classification", return_all_scores=True)
+reader = easyocr.Reader(['en'])
+st.title('Hateful Memes Classification')
+
+image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
+temp_file = NamedTemporaryFile(delete=False)
+
+if image_file is not None:
+    # To View Uploaded Image
+    st.write('Meme Image:')
+    temp_file.write(image_file.getvalue())
+    imgu = load_img(temp_file.name)
+    st.image(imgu)
+
+    with st.spinner('Predicting Label..'):
+        i_pred = get_img_prediction(temp_file.name)
+        t_pred = get_text_prediction('',temp_file.name)
+        y_pred_both = pred_label_mean(i_pred,t_pred)
+        y_pred = y_pred_both.index(max(y_pred_both))
+
+        st.write(np.round(np.array(y_pred_both),4))
+        if y_pred == 0:
+            st.success('Predicted Label: non-hateful meme')
+        if y_pred == 1:
+            st.success('Predicted Label: hateful meme')