Update app.py

app.py

@@ -1,69 +1,39 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    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(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    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,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = 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
-"""
-chatbot = 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)",
-        ),
-    ],
+import tensorflow as tf
+import numpy as np
+
+# 1. Load model TFLite Anda (pastikan file .tflite ada di folder yang sama)
+interpreter = tf.lite.Interpreter(model_path="tiny_sentiment_model_imdb.tflite")
+interpreter.allocate_tensors()
+
+def predict_sentiment(text):
+    # --- BAGIAN PENTING: PROSES TEXT ---
+    # TFLite memerlukan teks diubah ke angka (tokenizing) sesuai cara Anda melatih model.
+    # Contoh di bawah ini adalah placeholder logika inferensi:
+    
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    # (Opsional) Tambahkan kode tokenizing teks Anda di sini agar sesuai input_details
+    # input_data = tokenizer(text) 
+    
+    # Jalankan model (contoh dummy input)
+    # interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+    
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    prediction = output_data[0] # Misal: 0 untuk Negative, 1 untuk Positive
+
+    return "Positive" if prediction > 0.5 else "Negative"
+
+# 2. Buat UI Sederhana: 1 Input Box -> 1 Output Text
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(label="Masukkan Kalimat", placeholder="Contoh: Saya sangat senang hari ini!"),
+    outputs=gr.Textbox(label="Hasil Analisis"),
+    title="Sentimen Analisis TFLite",
+    allow_flagging="never"
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
 if __name__ == "__main__":
     demo.launch()