# -*- coding: utf-8 -*-
"""nino bot

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1UgXple_p_R-0mq9p5vhOmFPo9cgdayJy
"""

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "microsoft/DialoGPT-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

rules = {
  "hi": "Hello! How can I help you today?",
    "hello": "Hi there! How can I assist you?",
    "hey": "Hey! What can I do for you?",
    "how are you": "I'm just a bot, but I'm doing great! 😊 How about you?",
    "good morning": "Good morning! Hope you have a wonderful day!",
    "good afternoon": "Good afternoon! How can I help you?",
    "good evening": "Good evening! What can I do for you?",
    "bye": "Goodbye! Have a nice day! 👋",
    "thank you": "You're welcome! 😊",
    "thanks": "No problem! Happy to help!",
    "what is your name": "I'm your friendly chatbot assistant.",
    "help": "Sure! Ask me anything or type 'bye' to exit.",
    "what can you do": "I can answer simple questions and chat with you. Try saying hi!",
    "tell me a joke": "Why did the computer show up at work late? It had a hard drive!",
    "what time is it": "Sorry, I don't have a clock yet. But you can check your device's time!",
    "where are you from": "I'm from the cloud, here to assist you anytime!",
    "what is ai": "AI stands for Artificial Intelligence, which is intelligence demonstrated by machines.",
    "who created you": "I was created by a talented developer using Python and machine learning!",
    "how can i learn programming": "Start with basics like Python. There are many free tutorials online to get you started!",
    'ok':'ok',
    'who are you?':'I am nino',
    'hi nino' : 'hi there',
}

def respond(user_input, history):
    if history is None:
        history = []

    user_input_clean = user_input.lower().strip()

    if user_input_clean in rules:
        bot_reply = rules[user_input_clean]
    else:
        prompt = ""
        # Build the prompt including the conversation history
        for user_msg, bot_msg in history:
             # Ensure bot_msg is not None before adding to prompt
            if bot_msg is not None:
                prompt += f"{user_msg} {tokenizer.eos_token}\n{bot_msg} {tokenizer.eos_token}\n"
            else:
                 # If bot_msg is None, just add the user message
                 prompt += f"{user_msg} {tokenizer.eos_token}\n"
        # Add the current user input
        prompt += f"{user_input} {tokenizer.eos_token}\n"

        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(
            **inputs,
            max_new_tokens=100,
            pad_token_id=tokenizer.eos_token_id,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
        )

        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

        # Extract the newly generated bot response from the full text
        # This assumes the model output starts with the prompt
        bot_reply = generated_text[len(prompt):].strip()


        if len(bot_reply) < 5 or bot_reply.lower() in ["", "idk", "i don't know", "huh"]:
            bot_reply = "I'm not sure how to respond to that. Can you rephrase it?"

    # Append the new interaction to the history
    history.append((user_input, bot_reply))
    return history, history

def save_chat(history):
    # Ensure history is not None before attempting to save
    if history is not None:
        with open("chat_history.txt", "w", encoding="utf-8") as f:
            for user_msg, bot_msg in history:
                # Ensure bot_msg is not None before writing
                if bot_msg is not None:
                    f.write(f"You: {user_msg}\nBot: {bot_msg}\n\n")
                else:
                    f.write(f"You: {user_msg}\nBot: (No response)\n\n")


# New function to process input, respond, save, and clear the textbox
def process_input(user_input, history):
    # Get the updated history and bot response
    updated_history, _ = respond(user_input, history)

    # Save the updated chat history
    save_chat(updated_history)

    # Return the updated history for the chatbot display and an empty string for the textbox
    return updated_history, "", updated_history # Also return updated history for the state

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Type your message here...")
    state = gr.State([]) # This state variable holds the chat history

    # Update the submit action to call the new function
    # Add 'msg' to the outputs so its value can be updated
    msg.submit(process_input, inputs=[msg, state], outputs=[chatbot, msg, state])

demo.launch()

with open("my_data.txt", "w", encoding="utf-8") as f:
    f.write("Hello! This is my nino bot  text file.\nYou can use this as a data source.")