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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,68 @@
+import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import tokenizer_from_json
+import pandas as pd
+import re
+from nltk.stem import WordNetLemmatizer
+import json 
+import numpy as np
+import streamlit  as st
+
+
+# Load your TensorFlow model
+model = tf.keras.models.load_model("model/my_model.h5")
+lemmatizer=WordNetLemmatizer()
+maxlen = 41
+with open('data/tokenizer.json', 'r', encoding='utf-8') as f:
+    tokenizer = tokenizer_from_json(json.load(f))
+
+def preprocessing(text):
+    # Ensure the input is a string, otherwise return an empty string
+    if not isinstance(text, str):
+        return ''
+
+    cleaned_text = re.sub(r'(http|https|www)\S+', '', text)  # Remove URLs
+    cleaned_text = re.sub(r'[@#]\w+', '', cleaned_text) # Remove mentions (like @username) and hashtgs
+
+    cleaned_text = re.sub(r'[^a-zA-Z\s]', '', text)
+    cleaned_text = cleaned_text.replace('\n', ' ')
+    cleaned_text = re.sub(r'\s+', ' ', cleaned_text)
+
+    cleaned_text = cleaned_text.split()
+    filtered_words = [lemmatizer.lemmatize(word, pos='v') for word in cleaned_text]
+    text = ' '.join(filtered_words)
+    return text
+
+def getPrediction(input):
+    input = pd.DataFrame(input, columns=['text'])
+    input['text'] = input['text'].apply(preprocessing)
+    print(input['text'][0], end=", ")
+    input = tokenizer.texts_to_sequences(input['text'])
+    input = pad_sequences(input, maxlen = maxlen,  padding = 'post', truncating = 'post')
+    prediction = model.predict(input, verbose=0)
+    # calculate confidence score
+    confidence_score = np.max(prediction, axis=1)/np.sum(prediction, axis=1)
+    result = np.argmax(prediction, axis=1)
+    for i in range(len(confidence_score)):
+        if confidence_score[i] < 0.7:
+            result[i] = 2
+    print(prediction, confidence_score)
+    return result, confidence_score
+
+def getSentiment(idx):
+   match idx:
+        case 0:
+            return "Negative"
+        case 1:
+            return "Positive"
+        case default:
+            return "Neutral"
+       
+text = st.text_area("Enter Text...")
+
+if text:
+    prediction, confidence_score = getPrediction([text])  # Modify if preprocessing is needed
+    # Convert prediction to a human-readable format
+    response = {"prediction": getSentiment(prediction[0]) + " Statement",
+                "confidence": "{:.2f}".format(float(confidence_score[0] * 100)) + "%"}  # Adjust as necessary for output formatting
+    st.json(response)

my_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92a22638d088facf637db442e78c890b362c06a05f742799b570bf7ce9e109d3
+size 314911896

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bce784012044cfa9504c1d5f0f4155e6e2a387335e72f992aa5331b498014c82
+size 100485313