from typing import List
from dataclasses import asdict
import pandas as pd
import gradio as gr

from DDICourseSearch import DDICourseSearch, CourseSearchResult


# Load the courses
df = pd.read_csv('./data/courses.csv')
df.head()

# data cleaning
df = df.replace({r'\s+$': '', r'^\s+': ''}, regex=True).replace(r'\n',  ' ', regex=True)
df = df.drop(df.columns[[4]], axis=1) 
search_engine = DDICourseSearch(
    courses_df = df
)

def convert_to_dataframe(course_list: List[CourseSearchResult]) -> pd.DataFrame:
    # Convert each CourseSearchResult object to a dictionary and then to a DataFrame
    return pd.DataFrame([asdict(course) for course in course_list])

# Function to search for products
def search_courses(query, k, search_type, rerank=True):
    if not query.strip():
        return df  # Return all products if the query is empty
    results = search_engine.search(query, k=k, search_type=search_type, rerank=rerank)
    return convert_to_dataframe(results)

# Gradio interface
def search_interface(query):
    results = search_courses(query, k=5)
    return results


with gr.Blocks() as demo:
    with gr.Row():
        query = gr.Textbox(label="Search Query", placeholder="write a query to find the courses")
    with gr.Row():
        search_type = gr.Dropdown(label="Search Type", choices=['semantic', 'keyword', 'hybrid'], value='hybrid')
        k = gr.Number(label="Items Count", value=10)
        rerank = gr.Checkbox(value=True, label="Rerank")
    results = gr.Dataframe(label="Search Results")
    
    search_button = gr.Button("Search", variant='primary')
    search_button.click(fn=search_courses, inputs=[query, k, search_type, rerank], outputs=results)

demo.launch()