Create app.py

app.py

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+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_tab')  # Changed from 'punkt'
+nltk.download('stopwords')
+nltk.download('wordnet')
+nltk.download('omw-1.4')  # Added for better lemmatization
+
+stop_words = set(stopwords.words('english'))
+lemmatizer = WordNetLemmatizer()
+
+# Function to preprocess text
+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)
+
+# --- Load models from local files in Space ---
+@st.cache_resource
+def load_models():
+    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)
+    return goboult_model, goboult_tfidf, flipflop_model, flipflop_tfidf
+
+goboult_model, goboult_tfidf, flipflop_model, flipflop_tfidf = load_models()
+
+# --- 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}")