Update app.py

app.py

@@ -1,21 +1,55 @@
 import streamlit as st
-from transformers import pipeline
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score, classification_report
 
-def detect_emotion(sentence):
-    emotion_analyzer = pipeline("text-classification")
-    result = emotion_analyzer(sentence)
-    emotion = result[0]['label']
-    return emotion
+# Load the dataset
+@st.cache
+def load_data():
+    df = pd.read_csv("tweet_emotions.csv")
+    return df
+
+df = load_data()
+
+# Train a Naive Bayes classifier
+@st.cache
+def train_classifier(data):
+    vectorizer = CountVectorizer()
+    X = vectorizer.fit_transform(data['content'])
+    y = data['sentiment']
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    naive_bayes_model = MultinomialNB()
+    naive_bayes_model.fit(X_train, y_train)
+
+    return naive_bayes_model, vectorizer, X_test, y_test
+
+naive_bayes_model, vectorizer, X_test, y_test = train_classifier(df)
 
 # Streamlit UI
 st.title("Emotion Detection App")
 
 # User input
-sentence = st.text_area("Enter a sentence:")
-if not sentence:
-    st.warning("Please enter a sentence.")
+tweet = st.text_area("Enter a tweet:")
 
 # Emotion detection
 if st.button("Detect Emotion"):
-    emotion = detect_emotion(sentence)
-    st.success(f"Emotion detected: {emotion.capitalize()}")
+    tweet_vectorized = vectorizer.transform([tweet])
+    prediction = naive_bayes_model.predict(tweet_vectorized)
+    st.success(f"Predicted Emotion: {prediction[0]}")
+
+# Display the dataset
+st.subheader("Dataset Preview:")
+st.write(df.head())
+
+# Model evaluation
+st.subheader("Model Evaluation:")
+y_pred = naive_bayes_model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+classification_rep = classification_report(y_test, y_pred)
+
+st.write(f"Accuracy: {accuracy:.4f}")
+st.write("Classification Report:\n", classification_rep)