app.py

import gradio as gr
import torch
from unsloth import FastLanguageModel
from transformers import TextIteratorStreamer
import threading
from peft import PeftModel
import json
import time
import os

max_token = 9000

# -----------------------------
# 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"#"unsloth/gemma-3-4b-it-unsloth-bnb-4bit" #"unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit" 
base_model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=base_model_name,
    max_seq_length=2048,
    dtype=torch.float16,
    load_in_4bit=True,
)

# -----------------------------
# 3️⃣ Load LoRA
# -----------------------------
lora_repo =  "Ephraimmm/PIDGIN_gemma-3" #"Ephraimmm/pigin-gemma-3-0.2" #"Ephraimmm/Pidgin_llamma_model" 
lora_model = PeftModel.from_pretrained(base_model, lora_repo, adapter_name="adapter_model")
FastLanguageModel.for_inference(lora_model)

# -----------------------------
# 4️⃣ Streaming generation function
# -----------------------------
def generate_response(user_message):
    messages = [
        {
            "role": "system",
            "content": [{"type": "text", "text": """You are a Nigerian assistant that speaks PIDGIN ENGLISH.when asked how far reply I de o, how you de"""}]
                                    },
        {
            "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=max_token,
        temperature=0.1,
        top_p=1.0,
        top_k=None,
        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 a small empty txt file instead of None (to avoid Gradio error)
        file_path = "conversation_empty.txt"
        with open(file_path, "w", encoding="utf-8") as f:
            f.write("[]")
        return file_path
    
    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()