Update app.py

app.py

@@ -1,3 +1,342 @@
+# 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
+
+load_dotenv()
+#test
+
+# Initialize Cohere client
+COHERE_API_KEY = os.environ["COHERE_API_KEY"]
+co = cohere.Client(COHERE_API_KEY)
+
+# 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="Simple Google Search Console Data", layout="wide")
+    st.title("GSC Relenvacy Score Calculator")
+   # st.markdown(f"### Lightweight GSC Data Extractor. (Max {MAX_ROWS:,} Rows)")
+    st.divider()
+
+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()
+
+# -------------
+# Data Processing Functions
+# -------------
+
+def fetch_content(url):
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.text, 'html.parser')
+        content = soup.get_text(separator=' ', strip=True)
+        return content
+    except requests.RequestException as e:
+        return str(e)
+
+def generate_embeddings(text_list, model_type):
+    if not text_list:
+        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
+    return embeddings
+
+def calculate_relevancy_scores(df, model_type):
+    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)
+    except Exception as e:
+        st.warning(f"Error calculating relevancy scores: {e}")
+        df = df.assign(relevancy_score=0)
+    return df
+
+def process_gsc_data(df):
+    # Filter for queries below position 10
+    df_filtered = df[df['position'] > 10].copy()
+    
+    # Sort by impressions in descending order
+    df_sorted = df_filtered.sort_values(['impressions'], ascending=[False])
+    
+    # Keep only the highest impression query for each page
+    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']]
+    return result
+
+# -------------
+# Google Authentication Functions
+# -------------
+
+def load_config():
+    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/"],
+        }
+    }
+    return client_config
+
+def init_oauth_flow(client_config):
+    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]
+    )
+    return flow
+
+def google_auth(client_config):
+    flow = init_oauth_flow(client_config)
+    auth_url, _ = flow.authorization_url(prompt="consent")
+    return flow, auth_url
+
+def auth_search_console(client_config, credentials):
+    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),
+    }
+    return searchconsole.authenticate(client_config=client_config, credentials=token)
+
+# -------------
+# Data Fetching Functions
+# -------------
+
+def list_gsc_properties(credentials):
+    service = build('webmasters', 'v3', credentials=credentials)
+    site_list = service.sites().list().execute()
+    return [site['siteUrl'] for site in site_list.get('siteEntry', [])] or ["No properties found"]
+
+def fetch_gsc_data(webproperty, search_type, start_date, end_date, dimensions, device_type=None):
+    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()
+        return process_gsc_data(df)
+    except Exception as e:
+        show_error(e)
+        return pd.DataFrame()
+
+def fetch_data_loading(webproperty, search_type, start_date, end_date, dimensions, device_type=None, model_type='english'):
+    with st.spinner('Fetching data and calculating relevancy scores...'):
+        df = fetch_gsc_data(webproperty, search_type, start_date, end_date, dimensions, device_type)
+        if not df.empty:
+            df = calculate_relevancy_scores(df, model_type)
+        processed_df = process_gsc_data(df)
+        return processed_df
+
+# -------------
+# Utility Functions
+# -------------
+
+def update_dimensions(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):
+    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:
+            return custom_start, custom_end
+        else:
+            return today - datetime.timedelta(days=7), today
+    return today - datetime.timedelta(days=range_map.get(selection, 0)), today
+
+def show_error(e):
+    st.error(f"An error occurred: {e}")
+
+def property_change():
+    st.session_state.selected_property = st.session_state['selected_property_selector']
+
+# -------------
+# File & Download Operations
+# -------------
+
+def show_dataframe(report):
+    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):
+    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)
+
+# -------------
+# Streamlit UI Components
+# -------------
+
+def show_google_sign_in(auth_url):
+    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):
+    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():
+    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():
+    return st.selectbox(
+        "Select the embedding model:",
+        ["english", "multilingual"],
+        key='model_type_selector'
+    )
+
+def show_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():
+    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):
+    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):
+    # Convert 'position' column to integer
+    report['position'] = report['position'].astype(int)
+    
+    # Create a clickable URL column
+    def make_clickable(url):
+        return f'<a href="{url}" target="_blank">{url}</a>'
+    
+    report['clickable_url'] = report['page'].apply(make_clickable)
+    
+    # Reorder columns to put clickable_url first and sort by impressions
+    columns = ['clickable_url', 'query', 'impressions', 'clicks', 'ctr', 'position', 'relevancy_score']
+    report = report[columns].sort_values('impressions', ascending=False)
+
+    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
+    
+    # Use st.markdown to display the dataframe with clickable links
+    st.markdown(report.iloc[start_idx:end_idx].to_html(escape=False, index=False), unsafe_allow_html=True)
+# -------------
+# Main Streamlit App Function
+# -------------
+
 def main():
     setup_streamlit()
     client_config = load_config()