Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,77 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import joblib
+import neattext.functions as nfx
 
-"""
-# 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])),
-    ))
+# Sayfa Ayarları / Page Settings
+st.set_page_config(page_title="Amazon Review AI", page_icon="🛒", layout="centered")
+
+# Modeli Yükle / Load the Model
+try:
+    svm_model = joblib.load("svm_amazon.pkl")
+except:
+    st.error("Model dosyası (svm_amazon.pkl) bulunamadı! / Model file not found!")
+
+# --- SOL MENÜ (SIDEBAR) ---
+with st.sidebar:
+    st.title("💡 Sample Reviews / Örnek Yorumlar")
+    st.write("Copy and paste to test: / Test etmek için kopyalayın:")
+    
+    st.subheader("✅ Positive / Pozitif")
+    st.code("This product is fantastic and totally worth the buy!")
+    st.code("Great quality, I am very satisfied with this purchase.")
+    
+    st.subheader("❌ Negative / Negatif")
+    st.code("Worst product ever. It broke in one day, total waste of money.")
+    st.code("Terrible experience. The quality is very poor and awful.")
+    
+    st.divider()
+    
+    # Teknik Not Bölümü / Technical Note Section
+    st.info("""
+    **📊 Technical Note / Teknik Not:**
+    
+    This app uses the **SVM (Support Vector Machine)** model. 
+    During the training phase, **Random Forest (84.6%)** and **SVM (92.6%)** models were compared. 
+    The SVM model, which provided the highest accuracy, was deployed.
+    
+    ---
+    
+    Bu uygulama arka planda **SVM** modelini kullanmaktadır. 
+    Eğitim aşamasında **Random Forest (%84.6)** ve **SVM (%92.6)** modelleri karşılaştırılmış, en yüksek doğruluğu veren SVM modeli yayına alınmıştır.
+    """)
+
+# --- ANA PANEL (MAIN PANEL) ---
+st.title("🛒 Amazon Review Sentiment Analysis")
+st.subheader("Sentiment Analysis Dashboard / Duygu Analizi Paneli")
+st.write("Enter a review, and AI will analyze it. / Yorumunuzu girin, yapay zeka analiz etsin.")
+
+# Kullanıcı Girişi / User Input
+user_input = st.text_area(
+    "Your Review / Yorumunuz:", 
+    height=150, 
+    placeholder="Type here or paste a sample from the left... / Buraya yazın veya soldan bir örnek seçin..."
+)
+
+# Analiz Butonu / Analysis Button
+predict_btn = st.button("🔍 Analyze Sentiment / Duyguyu Analiz Et", use_container_width=True)
+
+if predict_btn:
+    if user_input.strip() == "":
+        st.warning("Please enter a text for analysis! / Lütfen analiz için bir metin girin!")
+    else:
+        # Metni temizle / Clean the text
+        clean_text = nfx.normalize(user_input)
+
+        # Tahmin (SVM) / Prediction
+        pred = svm_model.predict([clean_text])[0]
+
+        # Sonuç Ekranı / Result Display
+        st.divider()
+        if pred == 1:
+            st.success("### ✨ RESULT: POSITIVE / SONUÇ: POZİTİF 😊")
+            st.balloons()
+        else:
+            st.error("### ⚠️ RESULT: NEGATIVE / SONUÇ: NEGATİF 😡")
+
+        # Alt Bilgi / Footer Caption
+        st.caption("Analysis performed with SVM algorithm (%92.6 Accuracy). / Analiz SVM algoritması ile %92.6 doğrulukla yapılmıştır.")