app.py

# Standard library imports
import datetime
import base64
import os

# Related third-party imports
import streamlit as st
from streamlit_elements import elements
from google_auth_oauthlib.flow import Flow
from googleapiclient.discovery import build
from dotenv import load_dotenv
import pandas as pd
import searchconsole
import cohere
from sklearn.metrics.pairwise import cosine_similarity
import requests
from bs4 import BeautifulSoup
from apify_client import ApifyClient
import urllib.parse


load_dotenv()


# Initialize Cohere client
APIFY_API_TOKEN = os.environ.get('APIFY_API_TOKEN')
COHERE_API_KEY = os.environ["COHERE_API_KEY"]
co = cohere.Client(COHERE_API_KEY)
if not APIFY_API_TOKEN:
    st.error("APIFY_API_TOKEN is not set in the environment variables. Please set it and restart the application.")

# Initialize the ApifyClient with the API token
client = ApifyClient(APIFY_API_TOKEN)
# Initialize the ApifyClient with the API token

# Configuration: Set to True if running locally, False if running on Streamlit Cloud
IS_LOCAL = False

# Constants
SEARCH_TYPES = ["web", "image", "video", "news", "discover", "googleNews"]
DATE_RANGE_OPTIONS = [
    "Last 7 Days",
    "Last 30 Days",
    "Last 3 Months",
    "Last 6 Months",
    "Last 12 Months",
    "Last 16 Months",
    "Custom Range"
]
DEVICE_OPTIONS = ["All Devices", "desktop", "mobile", "tablet"]
BASE_DIMENSIONS = ["page", "query", "country", "date"]
MAX_ROWS = 250_000
DF_PREVIEW_ROWS = 100

# -------------
# Streamlit App Configuration
# -------------

def setup_streamlit():
    st.set_page_config(page_title="Keyword Relevance Test", layout="wide")
    st.title("Keyword Relevance Test Using Vector Embedding")
    st.divider()
    #logging.info("Streamlit app configured")

def init_session_state():
    if 'selected_property' not in st.session_state:
        st.session_state.selected_property = None
    if 'selected_search_type' not in st.session_state:
        st.session_state.selected_search_type = 'web'
    if 'selected_date_range' not in st.session_state:
        st.session_state.selected_date_range = 'Last 7 Days'
    if 'start_date' not in st.session_state:
        st.session_state.start_date = datetime.date.today() - datetime.timedelta(days=7)
    if 'end_date' not in st.session_state:
        st.session_state.end_date = datetime.date.today()
    if 'selected_dimensions' not in st.session_state:
        st.session_state.selected_dimensions = ['page', 'query']
    if 'selected_device' not in st.session_state:
        st.session_state.selected_device = 'All Devices'
    if 'custom_start_date' not in st.session_state:
        st.session_state.custom_start_date = datetime.date.today() - datetime.timedelta(days=7)
    if 'custom_end_date' not in st.session_state:
        st.session_state.custom_end_date = datetime.date.today()
    #logging.info("Session state initialized")

# -------------
# Data Processing Functions
# -------------
def generate_embeddings(text_list, model_type):
    #logging.debug(f"Generating embeddings for model type: {model_type}")
    if not text_list:
        logging.warning("Text list is empty, returning empty embeddings")
        return []
    model = 'embed-english-v3.0' if model_type == 'english' else 'embed-multilingual-v3.0'
    input_type = 'search_document'
    response = co.embed(model=model, texts=text_list, input_type=input_type)
    embeddings = response.embeddings
   # logging.debug(f"Embeddings generated successfully for model type: {model_type}")
    return embeddings


def get_serp_results(query):
    if not APIFY_API_TOKEN:
        st.error("Apify API token is not set. Unable to fetch SERP results.")
        return []

    run_input = {
        "queries": query,
        "resultsPerPage": 5,
        "maxPagesPerQuery": 1,
        "languageCode": "",
        "mobileResults": False,
        "includeUnfilteredResults": False,
        "saveHtml": False,
        "saveHtmlToKeyValueStore": False,
        "includeIcons": False,
    }

    try:
        #logger.debug(f"Calling Apify Actor with input: {run_input}")
        # Run the Actor and wait for it to finish
        run = client.actor("nFJndFXA5zjCTuudP").call(run_input=run_input)
       # logger.info(f"Apify Actor run completed. Run ID: {run.get('id')}")
        
        # Fetch results from the run's dataset
        
        #logger.debug(f"Fetching results from dataset ID: {run.get('defaultDatasetId')}")
        results = list(client.dataset(run["defaultDatasetId"]).iterate_items())
       # logger.info(f"Fetched {len(results)} results from Apify dataset")
        
        if results and 'organicResults' in results[0]:
            urls = [item['url'] for item in results[0]['organicResults']]
           # logger.info(f"Extracted {len(urls)} URLs from organic results")
            return urls
        else:
           # logger.warning("No organic results found in the SERP data.")
            st.warning("No organic results found in the SERP data.")
            return []
    except Exception as e:
       # logger.exception(f"Error fetching SERP results: {str(e)}")
        st.error(f"Error fetching SERP results: {str(e)}")
        return []



        
def fetch_content(url):
   # logger.info(f"Fetching content from URL: {url}")
    try:
        # Decode URL-encoded characters
        decoded_url = urllib.parse.unquote(url)
        response = requests.get(decoded_url, timeout=10)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        content = soup.get_text(separator=' ', strip=True)
        #logger.debug(f"Fetched {len(content)} characters from {url}")
        return content
    except requests.RequestException as e:
       # logger.error(f"Error fetching content from {url}: {e}")
        st.warning(f"Error fetching content from {url}: {e}")
        return ""

def calculate_relevance_score(page_content, query, co):
   # logger.info(f"Calculating relevance score for query: {query}")
    try:
        if not page_content:
           # logger.warning("Empty page content. Returning score 0.")
            return 0
        
        page_embedding = co.embed(texts=[page_content], model='embed-english-v3.0', input_type='search_document').embeddings[0]
        query_embedding = co.embed(texts=[query], model='embed-english-v3.0', input_type='search_query').embeddings[0]
        score = cosine_similarity([query_embedding], [page_embedding])[0][0]
     #   logger.debug(f"Relevance score calculated: {score}")
        return score
    except Exception as e:
       # logger.exception(f"Error calculating relevance score: {str(e)}")
        st.error(f"Error calculating relevance score: {str(e)}")
        return 0

def analyze_competitors(row, co, custom_url=None):
   # logger.info(f"Analyzing competitors for query: {row['query']}")
    query = row['query']
    our_url = row['page']
    
    competitor_urls = get_serp_results(query)
    competitor_urls = [url for url in competitor_urls if not url.startswith('/search')][:5]  # Get top 5 valid competitors
    
    if custom_url and custom_url not in competitor_urls:
        competitor_urls.append(custom_url)
    
    results = []
    for url in competitor_urls:
        content = fetch_content(url)
        score = calculate_relevance_score(content, query, co)
        results.append({'url': url, 'relevancy_score': score})
    
    results.append({'url': our_url, 'relevancy_score': row['relevancy_score']})
    results_df = pd.DataFrame(results).sort_values('relevancy_score', ascending=False)
    
    return results_df

def show_competitor_analysis(row, co):
    if st.button("Check Competitors", key=f"comp_{row['page']}"):
       # logger.info(f"Competitor analysis requested for page: {row['page']}")
        with st.spinner('Analyzing competitors...'):
            results_df = analyze_competitors(row, co)
            st.write("Relevancy Score Comparison:")
            st.dataframe(results_df)
            
            our_data = results_df[results_df['url'] == row['page']]
            if our_data.empty:
                st.error(f"Our page '{row['page']}' is not in the results. This indicates an error in fetching or processing the page.")
               # logger.error(f"Our page '{row['page']}' is missing from the results.")
                
                # Additional debugging information
                # st.write("Debugging Information:")
                # st.json({
                #     "our_url": row['page'],
                #     "query": row['query'],
                #     "content_fetched": fetch_content(row['page']),
                #     "urls_processed": results_df['url'].tolist()
                # })
            else:
                our_rank = our_data.index[0] + 1
                total_results = len(results_df)
                our_score = our_data['relevancy_score'].values[0]
                
               # logger.info(f"Our page ranks {our_rank} out of {total_results} in terms of relevancy score.")
                st.write(f"Our page ('{row['page']}') ranks {our_rank} out of {total_results} in terms of relevancy score.")
                st.write(f"Our relevancy score: {our_score:.4f}")
                
                if our_score == 0:
                    st.warning("Our page's relevancy score is 0. This might indicate an issue with content fetching or score calculation.")
                    # Additional debugging information
                    # st.write("Debugging Information:")
                    # content = fetch_content(row['page'])
                    # st.json({
                    #     "content_length": len(content),
                    #     "content_preview": content[:500] if content else "No content fetched",
                    #     "query": row['query']
                    # })
                elif our_rank == 1:
                    st.success("Your page has the highest relevancy score!")
                elif our_rank <= 3:
                    st.info("Your page is among the top 3 most relevant results.")
                elif our_rank > total_results / 2:
                    st.warning("Your page's relevancy score is in the lower half of the results. Consider optimizing your content.")

def process_gsc_data(df):
    #logging.info("Processing GSC data")
    df_sorted = df.sort_values(['impressions'], ascending=[False])
    df_unique = df_sorted.drop_duplicates(subset='page', keep='first')
    
    if 'relevancy_score' not in df_unique.columns:
        df_unique['relevancy_score'] = 0
    else:
        df_unique['relevancy_score'] = df_sorted.groupby('page')['relevancy_score'].first().values
    
    result = df_unique[['page', 'query', 'clicks', 'impressions', 'ctr', 'position', 'relevancy_score']]
    #logging.info("GSC data processed successfully")
    return result

# -------------
# Google Authentication Functions
# -------------

def load_config():
    #logging.info("Loading Google client configuration")
    client_config = {
        "web": {
            "client_id": os.environ["CLIENT_ID"],
            "client_secret": os.environ["CLIENT_SECRET"],
            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
            "token_uri": "https://oauth2.googleapis.com/token",
            "redirect_uris": ["https://poemsforaphrodite-gscpro.hf.space/"],
        }
    }
    #logging.info("Google client configuration loaded")
    return client_config

def init_oauth_flow(client_config):
    #logging.info("Initializing OAuth flow")
    scopes = ["https://www.googleapis.com/auth/webmasters.readonly"]
    flow = Flow.from_client_config(
        client_config,
        scopes=scopes,
        redirect_uri=client_config["web"]["redirect_uris"][0]
    )
    #logging.info("OAuth flow initialized")
    return flow

def google_auth(client_config):
   # logging.info("Starting Google authentication")
    flow = init_oauth_flow(client_config)
    auth_url, _ = flow.authorization_url(prompt="consent")
    #logging.info("Google authentication URL generated")
    return flow, auth_url

def auth_search_console(client_config, credentials):
    #logging.info("Authenticating with Google Search Console")
    token = {
        "token": credentials.token,
        "refresh_token": credentials.refresh_token,
        "token_uri": credentials.token_uri,
        "client_id": credentials.client_id,
        "client_secret": credentials.client_secret,
        "scopes": credentials.scopes,
        "id_token": getattr(credentials, "id_token", None),
    }
    #logging.info("Google Search Console authenticated")
    return searchconsole.authenticate(client_config=client_config, credentials=token)

# -------------
# Data Fetching Functions
# -------------

def list_gsc_properties(credentials):
   # logging.info("Listing GSC properties")
    service = build('webmasters', 'v3', credentials=credentials)
    site_list = service.sites().list().execute()
    properties = [site['siteUrl'] for site in site_list.get('siteEntry', [])] or ["No properties found"]
    #logging.info(f"GSC properties listed: {properties}")
    return properties

def fetch_gsc_data(webproperty, search_type, start_date, end_date, dimensions, device_type=None):
    #logging.info(f"Fetching GSC data for property: {webproperty}, search_type: {search_type}, date_range: {start_date} to {end_date}, dimensions: {dimensions}, device_type: {device_type}")
    query = webproperty.query.range(start_date, end_date).search_type(search_type).dimension(*dimensions)
    if 'device' in dimensions and device_type and device_type != 'All Devices':
        query = query.filter('device', 'equals', device_type.lower())
    try:
        df = query.limit(MAX_ROWS).get().to_dataframe()
        #logging.info("GSC data fetched successfully")
        return process_gsc_data(df)
    except Exception as e:
        #logging.error(f"Error fetching GSC data: {e}")
        show_error(e)
        return pd.DataFrame()

    
def calculate_relevancy_scores(df, model_type):
    #logging.info("Calculating relevancy scores")
    with st.spinner('Calculating relevancy scores...'):
        try:
            page_contents = [fetch_content(url) for url in df['page']]
            page_embeddings = generate_embeddings(page_contents, model_type)
            query_embeddings = generate_embeddings(df['query'].tolist(), model_type)
            relevancy_scores = cosine_similarity(query_embeddings, page_embeddings).diagonal()
            df = df.assign(relevancy_score=relevancy_scores)
            #logging.info("Relevancy scores calculated successfully")
        except Exception as e:
            #logging.error(f"Error calculating relevancy scores: {e}")
            st.warning(f"Error calculating relevancy scores: {e}")
            df = df.assign(relevancy_score=0)
    return df

# -------------
# Utility Functions
# -------------

def update_dimensions(selected_search_type):
   # logging.debug(f"Updating dimensions for search type: {selected_search_type}")
    return BASE_DIMENSIONS + ['device'] if selected_search_type in SEARCH_TYPES else BASE_DIMENSIONS

def calc_date_range(selection, custom_start=None, custom_end=None):
   # logging.debug(f"Calculating date range for selection: {selection}")
    range_map = {
        'Last 7 Days': 7,
        'Last 30 Days': 30,
        'Last 3 Months': 90,
        'Last 6 Months': 180,
        'Last 12 Months': 365,
        'Last 16 Months': 480
    }
    today = datetime.date.today()
    if selection == 'Custom Range':
        if custom_start and custom_end:
            #logging.debug(f"Custom date range: {custom_start} to {custom_end}")
            return custom_start, custom_end
        else:
            #logging.debug("Defaulting custom date range to last 7 days")
            return today - datetime.timedelta(days=7), today
    date_range = today - datetime.timedelta(days=range_map.get(selection, 0)), today
    #logging.debug(f"Date range calculated: {date_range}")
    return date_range

def show_error(e):
    #logging.error(f"An error occurred: {e}")
    st.error(f"An error occurred: {e}")

def property_change():
    #logging.info(f"Property changed to: {st.session_state['selected_property_selector']}")
    st.session_state.selected_property = st.session_state['selected_property_selector']

# -------------
# File & Download Operations
# -------------

def show_dataframe(report):
    #logging.info("Showing dataframe preview")
    with st.expander("Preview the First 100 Rows (Unique Pages with Top Query)"):
        st.dataframe(report.head(DF_PREVIEW_ROWS))

def download_csv_link(report):
    #logging.info("Generating CSV download link")
    def to_csv(df):
        return df.to_csv(index=False, encoding='utf-8-sig')
    csv = to_csv(report)
    b64_csv = base64.b64encode(csv.encode()).decode()
    href = f'<a href="data:file/csv;base64,{b64_csv}" download="search_console_data.csv">Download CSV File</a>'
    st.markdown(href, unsafe_allow_html=True)
    #logging.info("CSV download link generated")

# -------------
# Streamlit UI Components
# -------------

def show_google_sign_in(auth_url):
   # logging.info("Showing Google sign-in button")
    with st.sidebar:
        if st.button("Sign in with Google"):
            st.write('Please click the link below to sign in:')
            st.markdown(f'[Google Sign-In]({auth_url})', unsafe_allow_html=True)

def show_property_selector(properties, account):
  #  logging.info("Showing property selector")
    selected_property = st.selectbox(
        "Select a Search Console Property:",
        properties,
        index=properties.index(
            st.session_state.selected_property) if st.session_state.selected_property in properties else 0,
        key='selected_property_selector',
        on_change=property_change
    )
    return account[selected_property]

def show_search_type_selector():
  #  logging.info("Showing search type selector")
    return st.selectbox(
        "Select Search Type:",
        SEARCH_TYPES,
        index=SEARCH_TYPES.index(st.session_state.selected_search_type),
        key='search_type_selector'
    )

def show_model_type_selector():
  #  logging.info("Showing model type selector")
    return st.selectbox(
        "Select the embedding model:",
        ["english", "multilingual"],
        key='model_type_selector'
    )

def show_tabular_data(df, co):
    st.write("Data Table with Relevancy Scores")
    
    # Create a copy of the dataframe to modify
    df_display = df.copy()
    
    # Function to calculate relevancy score for a single row
    def calculate_single_relevancy(row):
        content = fetch_content(row['page'])
        score = calculate_relevance_score(content, row['query'], co)
        return score

    # Function to analyze competitors
    def analyze_competitors(row, custom_url=None):
        query = row['query']
        our_url = row['page']
        our_score = row['relevancy_score']
        
        competitor_urls = get_serp_results(query)
        competitor_urls = [url for url in competitor_urls if not url.startswith('/search')][:5]  # Get top 5 valid competitors
        
        if custom_url and custom_url not in competitor_urls:
            competitor_urls.append(custom_url)
        
        results = []
        for url in competitor_urls:
            content = fetch_content(url)
            score = calculate_relevance_score(content, query, co)
            results.append({'url': url, 'relevancy_score': score})
        
        results.append({'url': our_url, 'relevancy_score': our_score})
        results_df = pd.DataFrame(results).sort_values('relevancy_score', ascending=False)
        
        return results_df

    # Display the dataframe
    st.dataframe(df_display)

    # Create buttons for each row
    for index, row in df_display.iterrows():
        col1, col2, col3 = st.columns([3, 1, 1])
        with col1:
            st.write(f"Query: {row['query']} | Page: {row['page']}")
        with col2:
            if pd.isna(row['relevancy_score']) or row['relevancy_score'] == 0:
                if st.button(f"Calculate Score", key=f"calc_{index}"):
                    score = calculate_single_relevancy(row)
                    df.at[index, 'relevancy_score'] = score
                    df_display.at[index, 'relevancy_score'] = score
                    st.success(f"Relevancy Score: {score:.4f}")
                    st.experimental_rerun()
            else:
                st.write(f"Relevancy Score: {row['relevancy_score']:.4f}")
        with col3:
            if st.button(f"Compare Competitors", key=f"comp_{index}"):
                custom_url = st.text_input("Enter a custom URL to compare (optional):", key=f"custom_url_{index}")
                
                if st.button("Analyze", key=f"analyze_{index}"):
                    with st.spinner('Analyzing competitors...'):
                        results_df = analyze_competitors(row, custom_url)
                        st.write("Competitor Comparison:")
                        st.dataframe(results_df)
                        
                        our_rank = results_df.index[results_df['url'] == row['page']].tolist()[0] + 1
                        total_results = len(results_df)
                        st.write(f"Our page ranks {our_rank} out of {total_results} in terms of relevancy score.")
                        
                        if our_rank == 1:
                            st.success("Your page has the highest relevancy score!")
                        elif our_rank <= 3:
                            st.info("Your page is among the top 3 most relevant results.")
                        elif our_rank > total_results / 2:
                            st.warning("Your page's relevancy score is in the lower half of the results. Consider optimizing your content.")
                        
                        if custom_url:
                            custom_rank = results_df.index[results_df['url'] == custom_url].tolist()[0] + 1
                            custom_score = results_df[results_df['url'] == custom_url]['relevancy_score'].values[0]
                            st.write(f"Custom URL '{custom_url}' ranks {custom_rank} out of {total_results} with a relevancy score of {custom_score:.4f}")

    return df  # Return the updated dataframe

def show_date_range_selector():
  #  logging.info("Showing date range selector")
    return st.selectbox(
        "Select Date Range:",
        DATE_RANGE_OPTIONS,
        index=DATE_RANGE_OPTIONS.index(st.session_state.selected_date_range),
        key='date_range_selector'
    )

def show_custom_date_inputs():
   # logging.info("Showing custom date inputs")
    st.session_state.custom_start_date = st.date_input("Start Date", st.session_state.custom_start_date)
    st.session_state.custom_end_date = st.date_input("End Date", st.session_state.custom_end_date)

def show_dimensions_selector(search_type):
  #  logging.info("Showing dimensions selector")
    available_dimensions = update_dimensions(search_type)
    return st.multiselect(
        "Select Dimensions:",
        available_dimensions,
        default=st.session_state.selected_dimensions,
        key='dimensions_selector'
    )

def show_paginated_dataframe(report, rows_per_page=20):
  #  logging.info("Showing paginated dataframe")
    report['position'] = report['position'].astype(int)
    report['impressions'] = pd.to_numeric(report['impressions'], errors='coerce')
    
    def format_ctr(x):
        try:
            return f"{float(x):.2%}"
        except ValueError:
            return x
    
    def format_relevancy_score(x):
        try:
            return f"{float(x):.2f}"
        except ValueError:
            return x
    
    report['ctr'] = report['ctr'].apply(format_ctr)
    report['relevancy_score'] = report['relevancy_score'].apply(format_relevancy_score)
    
    def make_clickable(url):
        return f'<a href="{url}" target="_blank">{url}</a>'
    
    report['clickable_url'] = report['page'].apply(make_clickable)
    
    columns = ['clickable_url', 'query', 'impressions', 'clicks', 'ctr', 'position', 'relevancy_score']
    report = report[columns]

    sort_column = st.selectbox("Sort by:", columns[1:], index=columns[1:].index('impressions'))
    sort_order = st.radio("Sort order:", ("Descending", "Ascending"))
    
    ascending = sort_order == "Ascending"
    
    def safe_float_convert(x):
        try:
            return float(x.rstrip('%')) / 100 if isinstance(x, str) and x.endswith('%') else float(x)
        except ValueError:
            return 0
    
    report['ctr_numeric'] = report['ctr'].apply(safe_float_convert)
    report['relevancy_score_numeric'] = report['relevancy_score'].apply(safe_float_convert)
    
    sort_column_numeric = sort_column + '_numeric' if sort_column in ['ctr', 'relevancy_score'] else sort_column
    report = report.sort_values(by=sort_column_numeric, ascending=ascending)
    
    report = report.drop(columns=['ctr_numeric', 'relevancy_score_numeric'])

    total_rows = len(report)
    total_pages = (total_rows - 1) // rows_per_page + 1

    if 'current_page' not in st.session_state:
        st.session_state.current_page = 1

    col1, col2, col3 = st.columns([1,3,1])
    with col1:
        if st.button("Previous", disabled=st.session_state.current_page == 1):
            st.session_state.current_page -= 1
    with col2:
        st.write(f"Page {st.session_state.current_page} of {total_pages}")
    with col3:
        if st.button("Next", disabled=st.session_state.current_page == total_pages):
            st.session_state.current_page += 1

    start_idx = (st.session_state.current_page - 1) * rows_per_page
    end_idx = start_idx + rows_per_page
    
    st.markdown(report.iloc[start_idx:end_idx].to_html(escape=False, index=False), unsafe_allow_html=True)

# -------------
# Main Streamlit App Function
# -------------

def main():
   # logging.info("Starting main function")
    setup_streamlit()
    print("hello")
    client_config = load_config()
    
    if 'auth_flow' not in st.session_state or 'auth_url' not in st.session_state:
        st.session_state.auth_flow, st.session_state.auth_url = google_auth(client_config)

    query_params = st.query_params
    auth_code = query_params.get("code", None)
    
    if auth_code and 'credentials' not in st.session_state:
        st.session_state.auth_flow.fetch_token(code=auth_code)
        st.session_state.credentials = st.session_state.auth_flow.credentials

    if 'credentials' not in st.session_state:
        show_google_sign_in(st.session_state.auth_url)
    else:
        init_session_state()
        account = auth_search_console(client_config, st.session_state.credentials)
        properties = list_gsc_properties(st.session_state.credentials)

        if properties:
            webproperty = show_property_selector(properties, account)
            search_type = show_search_type_selector()
            date_range_selection = show_date_range_selector()
            model_type = show_model_type_selector()
            if date_range_selection == 'Custom Range':
                show_custom_date_inputs()
                start_date, end_date = st.session_state.custom_start_date, st.session_state.custom_end_date
            else:
                start_date, end_date = calc_date_range(date_range_selection)

            selected_dimensions = show_dimensions_selector(search_type)

            if 'report_data' not in st.session_state:
                st.session_state.report_data = None

            if st.button("Fetch Data"):
                with st.spinner('Fetching data...'):
                    st.session_state.report_data = fetch_gsc_data(webproperty, search_type, start_date, end_date, selected_dimensions)

            if st.session_state.report_data is not None and not st.session_state.report_data.empty:
                st.write("Data fetched successfully.")
                
                st.session_state.report_data = show_tabular_data(st.session_state.report_data, co)
                
                download_csv_link(st.session_state.report_data)
            elif st.session_state.report_data is not None:
               # logger.warning("No data found for the selected criteria.")
                st.warning("No data found for the selected criteria.")

if __name__ == "__main__":
   # logging.info("Running main function")
    main()
    #logger.info("Script completed")