import gradio as gr
from sentence_transformers import SentenceTransformer, util
import openai
import os
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"


# Initialize paths and model identifiers for easy configuration and maintenance
filename = "output_country_details.txt"  # Path to the file storing country-specific details
retrieval_model_name = 'output/sentence-transformer-finetuned/'

openai.api_key = os.environ["OPENAI_API_KEY"]


# Attempt to load the necessary models and provide feedback on success or failure
try:
    retrieval_model = SentenceTransformer(retrieval_model_name)
    print("Models loaded successfully.")
except Exception as e:
    print(f"Failed to load models: {e}")

def load_and_preprocess_text(filename):
    """
    Load and preprocess text from a file, removing empty lines and stripping whitespace.
    """
    try:
        with open(filename, 'r', encoding='utf-8') as file:
            segments = [line.strip() for line in file if line.strip()]
        print("Text loaded and preprocessed successfully.")
        return segments
    except Exception as e:
        print(f"Failed to load or preprocess text: {e}")
        return []

segments = load_and_preprocess_text(filename)

def find_relevant_segment(user_query, segments):
    """
    Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings.
    This version tries to match country names in the query with those in the segments.
    """
    try:
        # Lowercase the query for better matching
        lower_query = user_query.lower()
        # Filter segments to include only those containing country names mentioned in the query
        country_segments = [seg for seg in segments if any(country.lower() in seg.lower() for country in ['Guatemala', 'Mexico', 'U.S.', 'United States'])]
        
        # If no specific country segments found, default to general matching
        if not country_segments:
            country_segments = segments
        
        query_embedding = retrieval_model.encode(lower_query)
        segment_embeddings = retrieval_model.encode(country_segments)
        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
        best_idx = similarities.argmax()
        return country_segments[best_idx]
    except Exception as e:
        print(f"Error in finding relevant segment: {e}")
        return ""


def generate_response(user_query, relevant_segment):
    """
    Generate a response emphasizing the bot's capability in providing country-specific visa information.
    """
    try:
        system_message = "You are a chess chatbot specialized in providing information on chess rules, strategies, and terminology."
        user_message = f"Here's the information on visa requirements for your query: {relevant_segment}"
        messages = [
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_message}
        ]
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",  # Verify model name
            messages=messages,
            max_tokens=150,
            temperature=0.2,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0
        )
        return response['choices'][0]['message']['content'].strip()
    except Exception as e:
        print(f"Error in generating response: {e}")
        return f"Error in generating response: {e}"




# Define and configure the Gradio application interface to interact with users.
# Define and configure the Gradio application interface to interact with users.
def query_model(question):
    """
     Process a question, find relevant information, and generate a response.
    """
    if question == "":
        return "Welcome to ChessBot! Ask me anything about chess rules, strategies, and terminology."
    relevant_segment = find_relevant_segment(question, segments)
    if not relevant_segment:
        return "Could not find specific information. Please refine your question."
    response = generate_response(question, relevant_segment)
    return response




# Define the welcome message and specific topics and countries the chatbot can provide information about.
welcome_message = """
# Welcome to ChessBot!

## Your AI-driven assistant for all chess-related queries.
"""

topics = """
### Feel Free to ask me anything from the topics below!
- Chess piece movements
- Special moves
- Game phases
- Common strategies
- Chess terminology
- Famous games
- Chess tactics
"""


# Define and configure the Gradio application interface to interact with users.
def query_model(question):
    """
    Process a question, find relevant information, and generate a response.
    
    Args:
        question (str): User input question.

    Returns:
        str: Generated response or a default welcome message if no question is provided.
    """
    if question == "":
        return welcome_message
    relevant_segment = find_relevant_segment(question, segments)
    response = generate_response(question, relevant_segment)
    return response




# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks() as demo:
    gr.Markdown(welcome_message)  # Display the formatted welcome message
    with gr.Row():
        with gr.Column():
            gr.Markdown(topics)  # Show the topics on the left side
    with gr.Row():
        img = gr.Image(os.path.join(os.getcwd(), "final.png"), width=500)  # Include an image for visual appeal
    with gr.Row():
        with gr.Column():
            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
            answer = gr.Textbox(label="ChessBot Response", placeholder="ChessBot will respond here...", interactive=False, lines=10)
            submit_button = gr.Button("Submit")
            submit_button.click(fn=query_model, inputs=question, outputs=answer)

# Launch the Gradio app to allow user interaction
demo.launch(share=True)