Update app.py

edit kode untuk fungsi bahasa indonesia

app.py

@@ -1,64 +1,56 @@
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Dataset Bahasa Indonesia
+data = {
+    "pertanyaan": [
+        "Halo", 
+        "Apa kabar?", 
+        "Siapa namamu?", 
+        "Apa hobi kamu?", 
+        "Ceritakan lelucon"
     ],
+    "jawaban": [
+        "Hai juga!", 
+        "Aku baik, bagaimana dengan kamu?", 
+        "Aku adalah IndoBot AI.", 
+        "Hobiku membantu orang seperti kamu!", 
+        "Kenapa ayam menyeberang jalan? Untuk ke sisi lain!"
+    ]
+}
+df = pd.DataFrame(data)
+
+# Preprocessing Data
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(df['pertanyaan'])
+y = df['jawaban']
+
+# Model Klasifikasi
+model = MultinomialNB()
+model.fit(X, y)
+
+# Fungsi Chatbot
+def chatbot_respon(input_text):
+    input_vec = vectorizer.transform([input_text])
+    try:
+        response = model.predict(input_vec)[0]
+    except:
+        response = "Maaf, aku tidak mengerti pertanyaanmu."
+    return response
+
+# Interface Gradio
+def gradio_chatbot(user_input):
+    return chatbot_respon(user_input)
+
+interface = gr.Interface(
+    fn=gradio_chatbot, 
+    inputs=gr.Textbox(lines=2, placeholder="Tanyakan sesuatu..."),
+    outputs="text",
+    title="IndoBot AI",
+    description="IndoBot AI adalah chatbot sederhana yang memahami bahasa Indonesia. Coba tanyakan sesuatu!"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()