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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:
color = ['blue','orange']
plt.figure(figsize=(10, 5))
# plt.bar(st.session_state.accuracy.keys(), st.session_state.accuracy.values(), color=['blue', 'orange', 'green','red', 'purple'])
plt.bar(st.session_state.accuracy.keys(), st.session_state.accuracy.values(), color=color)
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.") |