Delete app.py

app.py

@@ -1,52 +0,0 @@
-
-import streamlit as st
-import pickle
-import string
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from nltk.tokenize import word_tokenize
-import nltk
-
-# Download NLTK data
-nltk.download('punkt')
-nltk.download('stopwords')
-nltk.download('wordnet')
-
-# Load models and TF-IDF
-with open('rf_goboult_model.pkl', 'rb') as f:
-    goboult_model = pickle.load(f)
-with open('tfidf_goboult.pkl', 'rb') as f:
-    goboult_tfidf = pickle.load(f)
-
-with open('rf_flipflop_model.pkl', 'rb') as f:
-    flipflop_model = pickle.load(f)
-with open('tfidf_flipflop.pkl', 'rb') as f:
-    flipflop_tfidf = pickle.load(f)
-
-stop_words = set(stopwords.words('english'))
-lemmatizer = WordNetLemmatizer()
-
-def preprocess_text(text):
-    text = text.lower()
-    text = text.translate(str.maketrans('', '', string.punctuation))
-    tokens = word_tokenize(text)
-    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
-    return " ".join(tokens)
-
-# Streamlit UI
-st.title("Sentiment Analysis for Goboult & Flipflop")
-dataset = st.selectbox("Select Dataset", ["Goboult", "Flipflop"])
-review = st.text_area("Enter your review here:")
-
-if st.button("Predict Sentiment"):
-    if review.strip() == "":
-        st.warning("Please enter a review!")
-    else:
-        cleaned = preprocess_text(review)
-        if dataset.lower() == "goboult":
-            vectorized = goboult_tfidf.transform([cleaned])
-            pred = goboult_model.predict(vectorized)[0]
-        else:
-            vectorized = flipflop_tfidf.transform([cleaned])
-            pred = flipflop_model.predict(vectorized)[0]
-        st.success(f"Predicted Sentiment: {pred}")