Delete app.py

app.py

@@ -1,71 +0,0 @@
-import streamlit as st
-import joblib
-import re
-import nltk
-from nltk.corpus import stopwords
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-nltk.download('stopwords')
-stop_words = set(stopwords.words('english'))
-
-# LOAD MODEL AND VECTORIZER
-with open("sentimentAnalysis_model.pkl", "rb") as model_file:
-    model = joblib.load(model_file)
-with open("vectorizer.pkl", "rb") as vectorizer_file:
-    vectorizer = joblib.load(vectorizer_file)
-
-
-app, model_eval = st.tabs(["Application", "Model Evaluation"])
-# STREAMLIT APP TAB 1
-with app:
-    # MAPPING RESULTS
-    sentiment_mapping = {0: "Neutral", 1: "Positive", 2: "Negative"}
-
-    # FUNCTION TO REDUCE TEXT TO ITS MOST BASIC FORM 
-    def clean_text(text):
-        text = text.lower() 
-        text = re.sub(r'[^a-zA-Z\s]', '', text)  
-        text = ' '.join([word for word in text.split() if word not in stop_words])  
-        return text
-
-    # STREAMLIT UI
-    st.title("Sentiment Analysis App")
-    st.write("Enter text below to analyze its sentiment.")
-
-    user_input = st.text_area("Enter text:")
-
-    if st.button("Analyze Sentiment"):
-        if user_input:
-            cleaned_input = clean_text(user_input)
-            transformed_input = vectorizer.transform([cleaned_input])
-            
-            prediction = model.predict(transformed_input)[0]
-            sentiment = sentiment_mapping[prediction]
-            
-            st.write(f"Predicted Sentiment: **{sentiment}**")
-        else:
-            st.write("Please enter some text to analyze.")
-
-with model_eval:
-
-    st.header("Model Evaluation")
-    st.write("The Sentiment Analysis model was trained in order to detect if a text is positive, negative, or neutral. The dataset was taken from kaggle.")
-    st.write("dataset by Dataset by Ismiel Hossen Abir. Link: https://www.kaggle.com/datasets/mdismielhossenabir/sentiment-analysis")
-    
-    # SENTIMENT DISTRIBUTION
-    st.header("Sentiment Distribution")
-    st.write("The model was trained using a dataset with the total amount of text equivalent to the following labels")
-    st.image("sentiment_distribution.png")
-
-    # CONFUSION MATRIX
-    st.title("Confusion Matrix")
-    st.write("The confusion matrix displays the actual values or true labels with the predicted values from the model. With this, we can identify the margin of error the model has.")
-    st.image("confusion_matrix.png")
-
-    # EVALUATION MATRICS
-    st.title("Evaluation Metrics")
-    st.write("The image below represents the Accuracy, F1 score and the classification report of the model")
-    st.image("classification_report.png")
-    st.write("The model can be improved by using other algorithms, but logistic regression was used for project purposes")
-
-