import streamlit as st
import pandas as pd
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the games.csv file into a pandas DataFrame
@st.cache_resource  # Caches the loaded data to improve performance
def load_data():
    data = pd.read_csv('games.csv')
    return data.copy()

games_data = load_data()

# Load the pre-trained GPT-2 model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Function to handle user questions and provide answers based on the loaded data
def answer_question(question):
    question = question.lower()
    answer = ""

    if "release date" in question:
        # Find the release date of a game
        game_title = question.split("release date of ")[1].strip()
        matching_games = games_data[games_data['Title'].str.lower() == game_title]
        if not matching_games.empty:
            release_date = matching_games.iloc[0]['Release_Date']
            answer = f"The release date of '{game_title}' is {release_date}"
        else:
            answer = f"Sorry, I couldn't find any information about '{game_title}'"

    elif "developer" in question:
        # Find the developers of a game
        game_title = question.split("developers of ")[1].strip()
        matching_games = games_data[games_data['Title'].str.lower() == game_title]
        if not matching_games.empty:
            developers = matching_games.iloc[0]['Developers']
            answer = f"The developers of '{game_title}' are {developers}"
        else:
            answer = f"Sorry, I couldn't find any information about '{game_title}'"

    elif "similar games to" in question:
        # Find similar games based on user question
        game_title = question.split("similar games to ")[1].strip()
        matching_games = games_data[games_data['Title'].str.lower() == game_title]
        if not matching_games.empty:
            genre = matching_games.iloc[0]['Genres']
            similar_games = find_similar_games(game_title, genre)
            if similar_games:
                answer = f"Here are some similar games to '{game_title}': {', '.join(similar_games)}"
            else:
                answer = f"Sorry, I couldn't find any similar games to '{game_title}'"
        else:
            answer = f"Sorry, I couldn't find any information about '{game_title}'"

    # Add more question-answer logic here based on the columns in your games.csv file

    return answer

# Perform prompt tuning to improve model responses
def perform_prompt_tuning(input_text):
    responses = []
    for _ in range(3):
        inputs = tokenizer.encode(input_text, return_tensors='pt')
        prompt_len = inputs.shape[1]
        outputs = model.generate(inputs, max_length=200, num_return_sequences=1, no_repeat_ngram_size=2, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        responses.append(response)
    return responses

# Find similar games based on genre
def find_similar_games(game_title, genre):
    # Implement your logic for finding similar games based on genre
    # Return a list of similar game titles
    pass

# Define the main function that will run the Streamlit app
def main():
    st.title("Game Wikipedia")

    # Create a text input field for user queries
    user_question = st.text_input("Ask a question")
      
  
    # Display example questions for the user to copy-paste
    st.write("Example Questions:")
    st.write("1. release date of Hades")
    st.write("2. developers of God of War")
    st.write("3. Summary for Hollow Knight")
    


    # When the user submits a question, get the answer and display it
    if st.button("Submit"):
    


        # Perform prompt tuning to get model responses
        responses = perform_prompt_tuning(user_question)
       


        # Provide correct answer if available
        correct_answer = answer_question(user_question)
        if correct_answer:
            st.write("Correct Answer:")
            st.write(correct_answer)

        # Display the responses
        st.write("Model Responses:")
        for i, response in enumerate(responses):
            st.write(f"{i+1}. {response}")

        # Provide additional information if available
        if "Sorry, I couldn't find any information" not in responses[0]:
            st.write("Additional Information:")
            additional_info = answer_question(user_question)
            st.write(additional_info)

        # Display the answer based on user selection
        selected_index = st.number_input("Select the best response (1, 2, 3)", value=1, min_value=1, max_value=3, step=1)
        answer = responses[selected_index - 1]

        # Display the answer
        st.write("Model's Answer:")
        st.write(answer)


if __name__ == "__main__":
    main()