Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,44 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
 
-"""
-# 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])),
-    ))
+
+st.title("IMDB Movie Review Sentiment Analysis")
+
+st.write("Bu uygulama, girilen film yorumunun pozitif mi negatif mi olduğunu tahmin eder.")
+
+
+model = tf.keras.models.load_model("src/imdb_lstm_model.h5")
+
+with open("tokenizer.pkl", "rb") as file:
+    tokenizer = pickle.load(file)
+
+
+maxlen = 200
+
+
+yorum = st.text_area("Film yorumunu yazınız:")
+
+
+if st.button("Tahmin Et"):
+    
+    if yorum.strip() == "":
+        st.warning("Lütfen bir yorum yazınız.")
+        
+    else:
+        yorum_dizi = tokenizer.texts_to_sequences([yorum])
+        
+        yorum_pad = pad_sequences(
+            yorum_dizi,
+            maxlen=maxlen
+        )
+        
+        tahmin = model.predict(yorum_pad)[0][0]
+        
+        if tahmin >= 0.5:
+            st.success("Tahmin: Pozitif Yorum")
+        else:
+            st.error("Tahmin: Negatif Yorum")
+        
+        st.write("Pozitif olma olasılığı:", round(float(tahmin), 4))