Update app.py

app.py

@@ -1,118 +1,118 @@
-import streamlit as st  
-import pandas as pd  
-import numpy as np  
-import matplotlib.pyplot as plt  
-from sklearn.feature_extraction.text import TfidfVectorizer  
-from sklearn.model_selection import train_test_split  
-from sklearn.svm import SVC  
-from sklearn.linear_model import LogisticRegression  
-from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier  
-from sklearn.metrics import accuracy_score, classification_report  
-from transformers import BertTokenizer, BertForSequenceClassification  
-import torch  
-
-@st.cache_data  
-def load_data():  
-    return pd.read_csv('IMDB Dataset.csv')  
-
-if 'models' not in st.session_state:  
-    st.session_state.models = {}  
-if 'reports' not in st.session_state:  
-    st.session_state.reports = {}  
-if 'accuracy' not in st.session_state:  
-    st.session_state.accuracy = {}  
-
-df = load_data()  
-
-df['sentiment'] = df['sentiment'].map({'positive': 1, 'negative': 0})  
-
-X = df['review']  
-y = df['sentiment']  
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
-
-if not st.session_state.models:  
-    vectorizer = TfidfVectorizer()  
-    X_train_tfidf = vectorizer.fit_transform(X_train)  
-
-    # models
-    models = {  
-        "SVM": SVC(kernel='linear'),  
-        "Logistic Regression": LogisticRegression(max_iter=1000),  
-        "Random Forest": RandomForestClassifier(n_estimators=10),  
-        "Gradient Boosting": GradientBoostingClassifier()  
-    }  
-
-    for name, model in models.items():  
-        model.fit(X_train_tfidf, y_train)  
-        st.session_state.models[name] = model  
-        X_test_tfidf = vectorizer.transform(X_test)  
-        y_pred = model.predict(X_test_tfidf)  
-        st.session_state.accuracy[name] = accuracy_score(y_test, y_pred)  
-        report = classification_report(y_test, y_pred, output_dict=True)  
-        st.session_state.reports[name] = pd.DataFrame(report).transpose()  
-
-    st.session_state.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')  
-    st.session_state.bert_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)  
-
-    train_encodings = st.session_state.bert_tokenizer(list(X_train), truncation=True, padding=True, return_tensors='pt')  
-    train_labels = torch.tensor(y_train.values)  
-
-    train_dataset = torch.utils.data.TensorDataset(train_encodings['input_ids'], train_encodings['attention_mask'], train_labels)  
-
-    training_args = torch.optim.AdamW(st.session_state.bert_model.parameters(), lr=1e-5)  
-    st.session_state.bert_model.train()  
-
-    for epoch in range(1):  
-        for batch in train_dataset:  
-            inputs = batch[0], batch[1]  
-            labels = batch[2]  
-            outputs = st.session_state.bert_model(*inputs, labels=labels)  
-            loss = outputs.loss  
-            loss.backward()  
-            training_args.step()  
-            training_args.zero_grad()  
-
-    st.session_state.bert_model.eval()  
-    test_encodings = st.session_state.bert_tokenizer(list(X_test), truncation=True, padding=True, return_tensors='pt')  
-    with torch.no_grad():  
-        outputs = st.session_state.bert_model(test_encodings['input_ids'], test_encodings['attention_mask'])  
-    predictions = torch.argmax(outputs.logits, dim=1).numpy()  
-    st.session_state.accuracy["BERT"] = accuracy_score(y_test, predictions)  
-    report = classification_report(y_test, predictions, output_dict=True)  
-    st.session_state.reports["BERT"] = pd.DataFrame(report).transpose()  
-
-if st.session_state.accuracy:  
-    
-    plt.figure(figsize=(10, 5))  
-    plt.bar(st.session_state.accuracy.keys(), st.session_state.accuracy.values(), color=['blue', 'orange', 'green','red', 'purple'])  
-    plt.ylabel('Accuracy')  
-    plt.title('Model Accuracy Comparison')  
-    st.pyplot(plt)  
-
-    for name, report_df in st.session_state.reports.items():  
-        st.header(f"{name}",divider='orange')  
-        st.dataframe(report_df)  
-
-st.header("Manual Tryouts")
-user_input = st.text_area("Review", "")  
-
-if st.button("Predict"):  
-    if user_input:  
-        user_input_tfidf = vectorizer.transform([user_input])  
-
-        predictions = {}  
-        for name, model in st.session_state.models.items():  
-            prediction = model.predict(user_input_tfidf)  
-            predictions[name] = "Positive" if prediction[0] == 1 else "Negative"  
-
-        inputs = st.session_state.bert_tokenizer(user_input, return_tensors='pt', truncation=True, padding=True)  
-        with torch.no_grad():  
-            output = st.session_state.bert_model(inputs['input_ids'], inputs['attention_mask'])  
-        bert_prediction = torch.argmax(output.logits, dim=1).item()  
-        predictions["BERT"] = "Positive" if bert_prediction == 1 else "Negative"  
-
-        st.write("Predicted Sentiment:")  
-        for name in predictions:  
-            st.write(f"{name}: **{predictions[name]}**")  
-    else:  
+import streamlit as st  
+import pandas as pd  
+import numpy as np  
+import matplotlib.pyplot as plt  
+from sklearn.feature_extraction.text import TfidfVectorizer  
+from sklearn.model_selection import train_test_split  
+from sklearn.svm import SVC  
+from sklearn.linear_model import LogisticRegression  
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier  
+from sklearn.metrics import accuracy_score, classification_report  
+from transformers import BertTokenizer, BertForSequenceClassification  
+import torch  
+
+@st.cache_data  
+def load_data():  
+    return pd.read_csv('IMDB Dataset.csv')  
+
+if 'models' not in st.session_state:  
+    st.session_state.models = {}  
+if 'reports' not in st.session_state:  
+    st.session_state.reports = {}  
+if 'accuracy' not in st.session_state:  
+    st.session_state.accuracy = {}  
+
+df = load_data()  
+
+df['sentiment'] = df['sentiment'].map({'positive': 1, 'negative': 0})  
+
+X = df['review']  
+y = df['sentiment']  
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
+
+if not st.session_state.models:  
+    vectorizer = TfidfVectorizer()  
+    X_train_tfidf = vectorizer.fit_transform(X_train)  
+
+    # models
+    models = {  
+        # "SVM": SVC(kernel='linear'),  
+        "Logistic Regression": LogisticRegression(max_iter=1000),  
+        # "Random Forest": RandomForestClassifier(n_estimators=10),  
+        # "Gradient Boosting": GradientBoostingClassifier()  
+    }  
+
+    for name, model in models.items():  
+        model.fit(X_train_tfidf, y_train)  
+        st.session_state.models[name] = model  
+        X_test_tfidf = vectorizer.transform(X_test)  
+        y_pred = model.predict(X_test_tfidf)  
+        st.session_state.accuracy[name] = accuracy_score(y_test, y_pred)  
+        report = classification_report(y_test, y_pred, output_dict=True)  
+        st.session_state.reports[name] = pd.DataFrame(report).transpose()  
+
+    # st.session_state.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')  
+    # st.session_state.bert_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)  
+
+    # train_encodings = st.session_state.bert_tokenizer(list(X_train), truncation=True, padding=True, return_tensors='pt')  
+    # train_labels = torch.tensor(y_train.values)  
+
+    # train_dataset = torch.utils.data.TensorDataset(train_encodings['input_ids'], train_encodings['attention_mask'], train_labels)  
+
+    # training_args = torch.optim.AdamW(st.session_state.bert_model.parameters(), lr=1e-5)  
+    # st.session_state.bert_model.train()  
+
+    # for epoch in range(1):  
+    #     for batch in train_dataset:  
+    #         inputs = batch[0], batch[1]  
+    #         labels = batch[2]  
+    #         outputs = st.session_state.bert_model(*inputs, labels=labels)  
+    #         loss = outputs.loss  
+    #         loss.backward()  
+    #         training_args.step()  
+    #         training_args.zero_grad()  
+
+    # st.session_state.bert_model.eval()  
+    # test_encodings = st.session_state.bert_tokenizer(list(X_test), truncation=True, padding=True, return_tensors='pt')  
+    # with torch.no_grad():  
+    #     outputs = st.session_state.bert_model(test_encodings['input_ids'], test_encodings['attention_mask'])  
+    # predictions = torch.argmax(outputs.logits, dim=1).numpy()  
+    # st.session_state.accuracy["BERT"] = accuracy_score(y_test, predictions)  
+    # report = classification_report(y_test, predictions, output_dict=True)  
+    # st.session_state.reports["BERT"] = pd.DataFrame(report).transpose()  
+
+if st.session_state.accuracy:  
+    
+    plt.figure(figsize=(10, 5))  
+    plt.bar(st.session_state.accuracy.keys(), st.session_state.accuracy.values(), color=['blue', 'orange', 'green','red', 'purple'])  
+    plt.ylabel('Accuracy')  
+    plt.title('Model Accuracy Comparison')  
+    st.pyplot(plt)  
+
+    for name, report_df in st.session_state.reports.items():  
+        st.header(f"{name}",divider='orange')  
+        st.dataframe(report_df)  
+
+st.header("Manual Tryouts")
+user_input = st.text_area("Review", "")  
+
+if st.button("Predict"):  
+    if user_input:  
+        user_input_tfidf = vectorizer.transform([user_input])  
+
+        predictions = {}  
+        for name, model in st.session_state.models.items():  
+            prediction = model.predict(user_input_tfidf)  
+            predictions[name] = "Positive" if prediction[0] == 1 else "Negative"  
+
+        # inputs = st.session_state.bert_tokenizer(user_input, return_tensors='pt', truncation=True, padding=True)  
+        # with torch.no_grad():  
+        #     output = st.session_state.bert_model(inputs['input_ids'], inputs['attention_mask'])  
+        # bert_prediction = torch.argmax(output.logits, dim=1).item()  
+        # predictions["BERT"] = "Positive" if bert_prediction == 1 else "Negative"  
+
+        st.write("Predicted Sentiment:")  
+        for name in predictions:  
+            st.write(f"{name}: **{predictions[name]}**")  
+    else:  
         st.write("Please enter a review.")