Update app.py

app.py

@@ -1,61 +1,61 @@
-import streamlit as st
-import pandas as pd
-import re
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.model_selection import train_test_split
-from sklearn.naive_bayes import GaussianNB
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
-from sklearn.preprocessing import LabelEncoder
-
-
-df = pd.read_csv(r"C:\Users\pavilion\Desktop\Project_vs\IMDB Dataset.csv")
-st.write("### Load Dataset") 
-df = df[:5001]
-
-# Data Cleaning
-def clean_text(text):
-    text = re.sub(r'<.*?>', '', text)  
-    text = re.sub(r'[^a-zA-Z\s]', '', text)  
-    text = text.lower().strip()  
-    return text
-
-df["review"] = df["review"].apply(clean_text)
-st.write("### Cleaned Dataset") 
-
-vectorizer = CountVectorizer(stop_words="english")
-X = vectorizer.fit_transform(df["review"]).toarray()
-
-df1 = pd.DataFrame(data=X,columns=vectorizer.get_feature_names_out())
-
-label_encoder = LabelEncoder()
-y = label_encoder.fit_transform(df["sentiment"])
-X_train, X_test, y_train, y_test = train_test_split(df1, y, test_size=0.2, random_state=42)
-
-
-
-model_2 = RandomForestClassifier()
-model_2.fit(X_train,y_train)
-y_pred = model_2.predict(X_test)
-accuracy_2 = accuracy_score(y_test,y_pred)
-st.write(accuracy_2)
-
-
-
-text_input = st.text_area("Write the movie review:")
-
-if st.button("Predict"):  
-    if text_input is not None:  
-        transformed_text = vectorizer.transform([text_input]).toarray()
-        af1 = pd.DataFrame(data=transformed_text,columns=vectorizer.get_feature_names_out())
-        prediction = model_2.predict(af1)
-        
-
-        st.write("Prediction:",prediction[0])
-
-        if prediction[0] == 1:
-            st.write("Positive Review!")
-            st.balloons()  
-        else:
-            st.write("Negative Review!")
+import streamlit as st
+import pandas as pd
+import re
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+
+
+df = pd.read_csv("IMDB Dataset.csv")
+st.title("Welcome to IMDB Movie Review Prediction") 
+df = df[:5001]
+
+# Data Cleaning
+def clean_text(text):
+    text = re.sub(r'<.*?>', '', text)  
+    text = re.sub(r'[^a-zA-Z\s]', '', text)  
+    text = text.lower().strip()  
+    return text
+
+df["review"] = df["review"].apply(clean_text)
+st.header("Accuracy of model") 
+
+vectorizer = CountVectorizer(stop_words="english")
+X = vectorizer.fit_transform(df["review"]).toarray()
+
+df1 = pd.DataFrame(data=X,columns=vectorizer.get_feature_names_out())
+
+label_encoder = LabelEncoder()
+y = label_encoder.fit_transform(df["sentiment"])
+X_train, X_test, y_train, y_test = train_test_split(df1, y, test_size=0.2, random_state=42)
+
+
+
+model_2 = RandomForestClassifier()
+model_2.fit(X_train,y_train)
+y_pred = model_2.predict(X_test)
+accuracy_2 = accuracy_score(y_test,y_pred)
+st.write(accuracy_2)
+
+
+
+text_input = st.text_area("Write the movie review:")
+
+if st.button("Predict"):  
+    if text_input is not None:  
+        transformed_text = vectorizer.transform([text_input]).toarray()
+        af1 = pd.DataFrame(data=transformed_text,columns=vectorizer.get_feature_names_out())
+        prediction = model_2.predict(af1)
+        
+
+        st.write("Prediction:",prediction[0])
+
+        if prediction[0] == 1:
+            st.write("Positive Review!")
+            st.balloons()  
+        else:
+            st.write("Negative Review!")
             st.snow()