Update app.py

app.py

@@ -1,141 +1,3 @@
-# import gradio as gr
-# import torch
-# from unsloth import FastLanguageModel
-# from transformers import TextIteratorStreamer
-# import threading
-# from peft import PeftModel
-# import json
-# import time
-
-# # -----------------------------
-# # 1️⃣ Set device
-# # -----------------------------
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-# print("Using device:", device)
-
-# # -----------------------------
-# # 2️⃣ Load base model (skip compilation)
-# # -----------------------------
-# base_model_name = "unsloth/gemma-3-4b-it-unsloth-bnb-4bit"
-# base_model, tokenizer = FastLanguageModel.from_pretrained(
-#     model_name=base_model_name,
-#     max_seq_length=2048,
-#     dtype=torch.float16,
-#     load_in_4bit=False,
-#     # <- avoids unsloth compilation errors
-# )
-
-# # -----------------------------
-# # 3️⃣ Load LoRA
-# # -----------------------------
-# lora_repo = "Ephraimmm/PIDGIN_gemma-3"
-# lora_model = PeftModel.from_pretrained(base_model, lora_repo, adapter_name="adapter_model")
-# FastLanguageModel.for_inference(lora_model)
-
-# # -----------------------------
-# # 4️⃣ Streaming generation function with Nigerian Pidgin system prompt
-# # -----------------------------
-# def generate_response(user_message):
-#     messages = [
-#         {
-#             "role": "system",
-#             "content": [{"type": "text", "text": "You be Nigerian assistant wey sabi Pidgin English only. No speak any other language. Reply friendly and in short sentences"}]
-#         },
-#         {
-#             "role": "user",
-#             "content": [{"type": "text", "text": user_message}]
-#         }
-#     ]
-
-#     inputs = tokenizer.apply_chat_template(
-#         messages,
-#         add_generation_prompt=True,
-#         return_tensors="pt",
-#         tokenize=True,
-#         return_dict=True
-#     ).to(device)
-
-#     streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-
-#     generation_kwargs = dict(
-#         **inputs,
-#         streamer=streamer,
-#         max_new_tokens=100,
-#         temperature=0.7,
-#         top_p=0.7,
-#         top_k=40,
-#         use_cache=False
-#     )
-
-#     def generate():
-#         lora_model.generate(**generation_kwargs)
-
-#     thread = threading.Thread(target=generate)
-#     thread.start()
-
-#     full_response = ""
-#     for new_token in streamer:
-#         if new_token:
-#             full_response += new_token
-#     thread.join()
-#     return full_response
-
-# # -----------------------------
-# # 5️⃣ Chat + Save
-# # -----------------------------
-# chat_history = []
-
-# def chat(user_message):
-#     bot_response = generate_response(user_message)
-#     chat_history.append((user_message, bot_response))
-#     return chat_history, ""  # also clears input box
-
-# def save_conversation():
-#     if not chat_history:
-#         return None
-    
-#     conversation = []
-#     for user_msg, bot_msg in chat_history:
-#         conversation.append({"role": "user", "content": str(user_msg)})
-#         conversation.append({"role": "assistant", "content": str(bot_msg)})
-    
-#     timestamp = time.strftime("%Y%m%d-%H%M%S")
-#     file_path = f"conversation_{timestamp}.txt"   # save as TXT not JSON
-    
-#     with open(file_path, "w", encoding="utf-8") as f:
-#         json.dump(conversation, f, indent=4, ensure_ascii=False)
-    
-#     return file_path
-
-# # -----------------------------
-# # 6️⃣ Gradio interface
-# # -----------------------------
-# with gr.Blocks() as demo:
-#     gr.Markdown("# Nigerian PIDGIN Assistant")
-#     gr.Markdown("Chat with a Nigerian assistant that only speaks Pidgin English.")
-
-#     chatbot = gr.Chatbot(label="Conversation")
-#     user_input = gr.Textbox(label="Your message", placeholder="Type your message here...")
-    
-#     with gr.Row():
-#         send_button = gr.Button("Send")
-#         save_button = gr.Button("Save Conversation")
-#         download_file = gr.File(label="Download Conversation")
-
-#     send_button.click(chat, inputs=user_input, outputs=[chatbot, user_input])
-#     save_button.click(save_conversation, outputs=download_file)
-
-# demo.launch()
-# # iface = gr.Interface(
-# #     fn=generate_response,
-# #     inputs=gr.Textbox(lines=2, placeholder="Enter your message..."),
-# #     outputs=gr.Textbox(label="PIDGIN Assistant"),
-# #     title="Nigerian PIDGIN Assistant",
-# #     description="Chat with a Nigerian assistant that only speaks Pidgin English."
-# # )
-
-# # iface.launch()
-
 import gradio as gr
 import torch
 from unsloth import FastLanguageModel
@@ -145,6 +7,7 @@ from peft import PeftModel
 import json
 import time
 import os
+
 max_token = 7000
 
 # -----------------------------
@@ -178,7 +41,10 @@ def generate_response(user_message):
     messages = [
         {
             "role": "system",
-            "content": [{"type": "text", "text": "You be Nigerian assistant wey sabi Pidgin English only. No speak any other language. Reply friendly and in short sentences"}]
+            "content": [{"type": "text", "text": """
+            You be Nigerian assistant wey sabi Pidgin English only. No speak any other language. Reply friendly and in short sentences
+            
+            """}]
         },
         {
             "role": "user",