Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,31 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import numpy as np
+from tensorflow.keras.models import load_model
+import joblib
+
+st.title('Sentiment Analysis Prediction')
+
+# Load the model and preprocessor
+try:
+    model = load_model('Sentiment Analyis/cnn/cnn_model.keras')
+    preproc = joblib.load('Sentiment Analyis/cnn/preproc.joblib')
+except Exception as e:
+    st.error(f"Error loading model or preprocessor: {e}")
+
+# Input field for text
+text_input = st.text_area('Enter text for sentiment analysis:', '')
+
+if st.button('Predict Sentiment'):
+    if not text_input.strip():
+        st.warning('Please enter some text.')
+    else:
+        try:
+            # Preprocess input text
+            X = preproc.transform([text_input])
+            # Predict sentiment
+            prediction = model.predict(X)
+            # Assuming binary classification: 0=Negative, 1=Positive
+            sentiment = 'Positive' if prediction[0][0] > 0.5 else 'Negative'
+            st.success(f'Prediction: {sentiment} (score: {prediction[0][0]:.2f})')
+        except Exception as e:
+            st.error(f"Error making prediction: {e}")