Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+# Load and preprocess the data
+# Replace 'your_dataset.csv' with the actual file path
+data = pd.read_csv('dataset.csv')
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(data['text'])
+y = data['label']
+
+# Train the model
+classifier = LogisticRegression()
+classifier.fit(X, y)
+
+# Define the prediction function
+def predict(text):
+    text_vectorized = vectorizer.transform([text])
+    prediction = classifier.predict(text_vectorized)[0]
+    if prediction == 'AI':
+        score = classifier.predict_proba(text_vectorized)[0][0]
+    else:
+        score = 1 - classifier.predict_proba(text_vectorized)[0][1]
+    response = [
+        {
+            'label': prediction,
+            'score': round(float(score), 4)
+        }
+    ]
+    return response
+
+# Streamlit app
+st.title("Sentiment Analysis App")
+
+# Text input for prediction
+text = st.text_area("Enter some text")
+
+# Perform prediction if text is provided
+if text:
+    result = predict(text)
+    st.json(result)