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app.py

@@ -1,65 +1,90 @@
-import gradio as gr
-import pandas as pd
-import re
-import nltk
-import joblib
-from nltk.corpus import stopwords
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LogisticRegression
-
-# Download stopwords if not available
-nltk.download('stopwords')
-
-# Paths to datasets
-train_path = r"train_data.csv"
-test_path = r"test_data.csv"
-
-# Load datasets
-train_df = pd.read_csv(train_path)
-
-# Text preprocessing function
-def preprocess_text(text):
-    text = text.lower()  # Convert to lowercase
-    text = re.sub(r'\W', ' ', text)  # Remove special characters
-    text = re.sub(r'\s+', ' ', text).strip()  # Remove extra spaces
-    stop_words = set(stopwords.words('english'))  # Load stopwords
-    text = ' '.join(word for word in text.split() if word not in stop_words)  # Remove stopwords
-    return text
-
-# Apply preprocessing
-train_df['cleaned_review'] = train_df['review'].astype(str).apply(preprocess_text)
-
-# Train the model
-vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1,2))
-X_train_tfidf = vectorizer.fit_transform(train_df['cleaned_review'])
-y_train = train_df['sentiment']
-
-model = LogisticRegression(max_iter=500)
-model.fit(X_train_tfidf, y_train)
-
-# Save the model and vectorizer
-joblib.dump(model, "sentiment_model.pkl")
-joblib.dump(vectorizer, "tfidf_vectorizer.pkl")
-
-# Load model and vectorizer for prediction
-model = joblib.load("sentiment_model.pkl")
-vectorizer = joblib.load("tfidf_vectorizer.pkl")
-
-# Gradio prediction function
-def predict_sentiment(review):
-    processed_review = preprocess_text(review)  # Preprocess input
-    review_tfidf = vectorizer.transform([processed_review])  # Convert to TF-IDF
-    prediction = model.predict(review_tfidf)[0]  # Get prediction
-    return f"Predicted Sentiment: {prediction}"
-
-# Gradio UI
-interface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(label="Enter a Review"),
-    outputs=gr.Textbox(label="Sentiment Prediction"),
-    title=" Movie Review Sentiment Analysis App ",
-    description="Enter a review, and the model will predict if it's Positive, Negative, or Neutral."
-)
-
-# Launch the app
-interface.launch()
+import gradio as gr
+import pandas as pd
+import numpy as np
+from bs4 import BeautifulSoup
+from sklearn.model_selection import train_test_split
+from nltk.stem import PorterStemmer
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from collections import Counter
+from sklearn.model_selection import train_test_split
+lemmatizer = WordNetLemmatizer()
+stemmer = PorterStemmer()
+import re
+import nltk
+import joblib
+from nltk.corpus import stopwords
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+
+# Download stopwords if not available
+nltk.download('stopwords')
+custom_stopwords = set(stopwords.words('english')) 
+# Paths to datasets
+train_path = r"E:\Projects\Sentiment Analysis Project DEPI\train_data.csv"
+test_path = r"E:\Projects\Sentiment Analysis Project DEPI\test_data.csv"
+
+# Load datasets
+train_df = pd.read_csv(train_path)
+
+# Text preprocessing function
+def preprocess_text(text):
+    if isinstance(text, str):  # Ensure text is a string
+        ## REMOVE HTML
+        if "<" in text and ">" in text:
+            text = BeautifulSoup(text, "html.parser").get_text()
+        ## CLEANING
+        # Remove special characters
+        text = re.sub(r'\W+', ' ', text)  
+        # Remove digits
+        text = re.sub(r'\d+', '', text)
+        ## LOWERCASING
+        text = text.lower()
+        ## TOKENIZATION
+        words = text.split()
+        ## REMOVE STOPWORDS
+        words = [w for w in words if w not in custom_stopwords] 
+        ## APPLY LEMMATIZATION
+        words = [lemmatizer.lemmatize(w) for w in words]
+        ## RETURN CLEANED TEXT
+        return ' '.join(words)
+    return ""
+
+# Apply preprocessing
+train_df['cleaned_review'] = train_df['review'].astype(str).apply(preprocess_text)
+
+# Train the model
+vectorizer = TfidfVectorizer(max_features=5000, ngram_range=(1,2))
+X_train_tfidf = vectorizer.fit_transform(train_df['cleaned_review'])
+y_train = train_df['sentiment']
+
+model = LogisticRegression(max_iter=500)
+model.fit(X_train_tfidf, y_train)
+
+# Save the model and vectorizer
+joblib.dump(model, "sentiment_model.pkl")
+joblib.dump(vectorizer, "tfidf_vectorizer.pkl")
+
+# Load model and vectorizer for prediction
+model = joblib.load("sentiment_model.pkl")
+vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+# Gradio prediction function
+def predict_sentiment(review):
+    processed_review = preprocess_text(review)  # Preprocess input
+    review_tfidf = vectorizer.transform([processed_review])  # Convert to TF-IDF
+    prediction = model.predict(review_tfidf)[0]  # Get prediction
+    return f"Predicted Sentiment: {prediction}"
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(label="Enter a Review"),
+    outputs=gr.Textbox(label="Sentiment Prediction"),
+    title="Movie Review Sentiment Analysis App",
+    description="Enter a review, and the model will predict if it's Positive, Negative, or Neutral."
+)
+
+# Launch the app
+interface.launch()

train_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91d028cbb0228d5f70b3175f4ae5597c11ac42de4a77df5bfbdb19f00aa5f827
+size 6120687