Upload app.py

app.py

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+import streamlit as st
+import pickle
+import numpy as np
+import re
+from gensim.models import Word2Vec
+from nltk.corpus import stopwords
+import nltk
+nltk.download('stopwords')
+
+# ===============================
+# Word2VecVectorizer class (must be defined before loading pickle)
+# ===============================
+class Word2VecVectorizer:
+    def __init__(self, model):
+        self.model = model
+        self.size = model.vector_size
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X):
+        vectors = []
+        for text in X:
+            tokens = text.split()
+            word_vecs = []
+            for w in tokens:
+                if w in self.model.wv:
+                    vec = self.model.wv[w]
+                    # normalize each vector
+                    vec = vec / np.linalg.norm(vec)
+                    word_vecs.append(vec)
+            if word_vecs:
+                vectors.append(np.mean(word_vecs, axis=0))
+            else:
+                vectors.append(np.zeros(self.size))
+        return np.array(vectors)
+
+# ===============================
+# Streamlit App Config
+# ===============================
+st.set_page_config(page_title="🎯 Word2Vec Sentiment Classifier")
+
+st.title("🎯 Live Sentiment Classifier (Word2Vec + Logistic Regression)")
+#st.write("This app predicts the sentiment of text using a Word2Vec-based Logistic Regression model trained on your dataset.")
+
+# Stopwords
+stop_words = set(stopwords.words('english'))
+
+# ===============================
+# Load Trained Pipeline
+# ===============================
+with open("text_classification_word2vec.pkl", "rb") as f:
+    pipeline = pickle.load(f)
+
+# ===============================
+# User Input
+# ===============================
+user_input = st.text_area("**Enter a sentence or YouTube comment to analyze:**")
+
+# ===============================
+# Prediction
+# ===============================
+if st.button("Predict Sentiment"):
+    if user_input.strip():
+        prediction = pipeline.predict([user_input])[0]
+
+        st.markdown(f"## ✅ Prediction: `{prediction.capitalize()}`")
+
+        if prediction.lower() == "positive":
+            st.success("😊 Positive Sentiment")
+        elif prediction.lower() == "negative":
+            st.error("😠 Negative Sentiment")
+        else:
+            st.info("😐 Neutral Sentiment")
+    else:
+        st.warning("⚠️ Please enter some text to analyze.")