uploading app files

Sentiment_CNN_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:716cbb90365fdc3ac8c6f918d16b8c47bd33c9fbe0be34c62da16e9c3483c2af
+size 283407096

app.py

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+import streamlit as st
+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
+from streamlit_lottie import st_lottie 
+import requests
+import pickle
+
+col1, col2 = st.columns([1, 1])
+
+with col1:
+
+    # Load the saved tokenizer
+    with open('tokenizer.pickle', 'rb') as handle:
+        tokenizer = pickle.load(handle)
+
+    # Load the saved MAX_SEQUENCE_LENGTH
+    with open('max_sequence_length.txt', 'r') as f:
+        MAX_SEQUENCE_LENGTH = int(f.read())
+
+    # Load the saved model
+    model = load_model('Sentiment_CNN_model.h5')
+
+    # Define the tokenizer function
+    def tokenize_text(text):
+        # Tokenize text
+        x_test = pad_sequences(tokenizer.texts_to_sequences([text]), maxlen=MAX_SEQUENCE_LENGTH)
+        return x_test
+
+    # Streamlit app
+    st.title('Twitter Sentiment Analysis')
+
+    user_input = st.text_input("Enter a tweet for sentiment analysis")
+
+    if st.button('Submit'):
+        processed_input = tokenize_text(user_input)
+        prediction = model.predict(processed_input)
+        if prediction >= 0.7:
+            st.success(f"Positive sentiment 😊")
+            st.slider('Sentiment Score', 0,10, int (prediction*10))
+        elif 0.3 < prediction < 0.7:  # Adjusted the condition to check for values between 0.4 and 0.7
+            st.info(f"Neutral sentiment 🙂")
+            st.slider('Sentiment Score', 0,10, int(prediction*10))
+        else:
+            st.warning(f"Negative sentiment 😖")
+            st.slider('Sentiment Score', 0,10, int(prediction*10))
+with col2:
+    def load_lottieurl(url: str):
+        r = requests.get(url)
+        if r.status_code != 200:
+            return None
+        return r.json()
+    lottie_hello = load_lottieurl("https://lottie.host/22856a7c-7ef0-453d-81ea-dc5842ab763a/p0C048YfUz.json")
+    
+    st_lottie(
+        lottie_hello,
+        speed=1,
+        reverse=False,
+        loop=True,
+        quality="high", 
+        width=300,
+        height=300,
+        key=None,
+    )
+

max_sequence_length.txt

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+300

requirements.txt

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+
+Keras-Preprocessing==1.1.2
+streamlit==1.31.0
+streamlit-lottie==0.0.5
+tensorflow==2.15.0

tokenizer.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2d65057a02c4577c625d24a893556a8cee46e78353d8e6ef09494bade586cfe
+size 10438740