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requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn

ss.py

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+import pandas as pd
+import streamlit as st 
+import warnings
+warnings.filterwarnings("ignore")
+
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.pipeline import Pipeline
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score
+
+# Load and clean data
+df = pd.read_csv(r"C:\Users\91879\Downloads\New_emotions.csv")  
+print(df.columns)  
+
+df.drop_duplicates(inplace=True)
+
+x = df["sentence"]
+y = df["emotion"]
+
+# Split data
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=29)
+
+# Build model
+model = Pipeline([
+    ("vectorizer", CountVectorizer()),
+    ("classifier", MultinomialNB(alpha=2))
+])
+
+model.fit(x, y)
+y_predict = model.predict(x_test)
+
+# Streamlit App
+st.title("Emotion Detection from Text 👇")
+st.write("Model Accuracy:", accuracy_score(y_test, y_predict))
+
+# User input
+sentence = st.text_input("Enter a sentence:")
+
+if st.button("Predict Emotion"):
+    if sentence:
+        prediction = model.predict([sentence])[0]
+        st.write("Predicted Emotion:", prediction)
+
+        # Show emoji
+        emojis = {
+            "sad": "😢😢",
+            "love": "❤️❤️",
+            "surprise": "😦😦",
+            "joy": "😂😂",
+            "anger": "😠😠",
+            "fear": "😨😨"
+        }
+        st.write("Emoji:", emojis.get(prediction, "🤔"))
+
+        user_data = pd.DataFrame([[sentence, prediction]], columns=["sentence", "predicted_emotion"])
+        st.write(user_data)