Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,62 @@
-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 streamlit as st
+import joblib
+import re
+from nltk.corpus import stopwords
+import nltk
+
+nltk.download("stopwords")
+
+st.set_page_config(
+    page_title="Disaster Tweet Classification",
+    page_icon="🚨",
+    layout="centered"
+)
+
+stop_words = set(stopwords.words("english"))
+
+def temizle(text):
+    text = text.lower()
+    text = re.sub(r"http\S+", " ", text)
+    text = re.sub(r"www\S+", " ", text)
+    text = re.sub(r"@\w+", " ", text)
+    text = re.sub(r"&", " ", text)
+    text = re.sub(r"rt", " ", text)
+    text = re.sub(r"[^a-z\s]", " ", text)
+
+    kelimeler = [
+        kelime for kelime in text.split()
+        if kelime not in stop_words
+    ]
+
+    return " ".join(kelimeler)
+
+model = joblib.load("src/disaster_tweet_model.pkl")
+tfidf = joblib.load("src/tfidf_vectorizer.pkl")
+
+st.title("🚨 Afet Tweet Sınıflandırma Uygulaması")
+
+st.write(
+    "Bu uygulama, girilen bir tweet metninin gerçek bir afet olayıyla ilgili olup olmadığını tahmin eder."
+)
+
+tweet = st.text_area(
+    "Tweet metnini giriniz:",
+    placeholder="Örnek: Forest fire near La Ronge Sask. Canada"
+)
+
+if st.button("Tahmin Et"):
+    if tweet.strip() == "":
+        st.warning("Lütfen bir tweet metni giriniz.")
+    else:
+        clean_tweet = temizle(tweet)
+        tweet_tfidf = tfidf.transform([clean_tweet])
+        prediction = model.predict(tweet_tfidf)[0]
+
+        if prediction == 1:
+            st.error("Sonuç: Bu tweet gerçek bir afetle ilgili olabilir.")
+        else:
+            st.success("Sonuç: Bu tweet gerçek bir afetle ilgili görünmüyor.")
+
+        st.write("Temizlenmiş metin:")
+        st.info(clean_tweet)