Update app.py

app.py

@@ -1,18 +1,53 @@
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import gradio as gr
 import csv
 
-# Nama file input dan output
-input_file = "dataset.csv"
-output_file = "cleaned_dataset.csv"
+# Load Dataset
+try:
+    df = pd.read_csv("cleaned_dataset.csv")
+except FileNotFoundError:
+    data = {"pertanyaan": [], "jawaban": []}
+    df = pd.DataFrame(data)
 
-# Bersihkan dataset
-with open(input_file, "r") as infile, open(output_file, "w", newline="") as outfile:
-    reader = csv.reader(infile)
-    writer = csv.writer(outfile)
+# Preprocessing Data
+vectorizer = CountVectorizer()
+if not df.empty:
+    X = vectorizer.fit_transform(df['pertanyaan'])
+    y = df['jawaban']
+    model = MultinomialNB()
+    model.fit(X, y)
+else:
+    model = None
 
-    # Periksa setiap baris
-    for row in reader:
-        # Hanya simpan baris dengan 2 kolom
-        if len(row) == 2:
-            writer.writerow(row)
+# Fungsi Chatbot
+def chatbot_respon(user_input):
+    if model:
+        try:
+            input_vec = vectorizer.transform([user_input])
+            response = model.predict(input_vec)[0]
+        except:
+            response = "Maaf, aku belum memahami pertanyaan ini."
+            log_input(user_input)
+    else:
+        response = "Model belum dilatih. Silakan tambahkan dataset."
+    return response
 
-print(f"Dataset telah dibersihkan. Simpan ke: {output_file}")
+# Log Pertanyaan Baru
+def log_input(user_input):
+    with open("chat_log.csv", "a", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow([user_input, ""])
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=chatbot_respon, 
+    inputs=gr.Textbox(lines=2, placeholder="Tanyakan sesuatu..."),
+    outputs="text",
+    title="IndoBot AI",
+    description="Chatbot berbasis bahasa Indonesia dengan kemampuan belajar dari log percakapan."
+)
+
+if __name__ == "__main__":
+    interface.launch()