Update app.py

app.py

@@ -1,4 +1,4 @@
-from unsloth import FastLanguageModel
+from app import FastLanguageModel
 import torch
 import gradio as gr
 
@@ -6,20 +6,17 @@ max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
 dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
 load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
 alpaca_prompt = """Berikut adalah instruksi yang deskripsikan tugas dan sepasang input dan konteksnya. Tulis response sesuai dengan permintaan.
-
 ### Instruction:
 {}
-
 ### Input:
 {}
-
 ### Response:
 {}"""
 
 if True:
-    from unsloth import FastLanguageModel
+    from app import FastLanguageModel
     model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name = "abdfajar707/llama3_8B_lora_model_rkp_v3", # YOUR MODEL YOU USED FOR TRAINING
+        model_name = "abdfajar707/llama3_8B_lora_model_rkp_v2", # YOUR MODEL YOU USED FOR TRAINING
         max_seq_length = max_seq_length,
         dtype = dtype,
         load_in_4bit = load_in_4bit,
@@ -42,37 +39,26 @@ def generate_response(prompt, max_length=1000):
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return response
 
-# Fungsi untuk antarmuka Gradio
-def chatbot_interface(user_input, history):
-    # Buat respons dari model
-    response = generate_response(user_input)
-    # Perbarui riwayat percakapan
-    history.append(("User", user_input))
-    history.append(("Bot", response))
-    return history, history
+history = []
+def wrapper_chat_history(chat_history, history):
+    chat_history = history[1:]
+    return chat_history
 
-# Definisikan input dan output untuk antarmuka menggunakan Gradio versi terbaru
-inputs = [
-    gr.Textbox(lines=1, label="Masukkan pesan Anda"),
-    gr.State(value=[])  # Untuk menyimpan riwayat percakapan
-]
+def converse(message, chat_history):
+    response = generate_response(message)
+    print(response)
+    user_msg = {"role": "user", "content": message}
+    history.append(user_msg)
+    ai_msg = {"role": "assistant", "content": response}
+    history.append(ai_msg)
+    return history[-1]["content"]
+    
+with gr.Blocks() as interface:
+  with gr.Row():
+    with gr.Column(scale=1):
+      gr.HTML('<img src="https://datahub.data.go.id/data/static/Kementerian%20PPN%20Bappenas%20Tanpa%20Teks.png" width="100px" alt="Image" style="max-width: 100%;">')
+  with gr.Row():
+    with gr.Column():
+      gr.ChatInterface(fn=converse, title="PPN/Bappenas - AI Interlinked")
 
-outputs = [
-    gr.Chatbot(label="Respons Chatbot"),
-    gr.State()  # Untuk memperbarui riwayat percakapan
-]
-
-# Buat dan luncurkan antarmuka Gradio
-interface = gr.Interface(
-    fn=chatbot_interface,
-    inputs=inputs,
-    outputs=outputs,
-    title="LLaMA3 LoRA Chatbot",
-    description="Chatbot yang didukung oleh model LLaMA3 dengan modifikasi LoRA."
-)
-
-# Jalankan antarmuka
 interface.launch()
-
-#demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-#demo.launch()