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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from apify_client import ApifyClient | |
| import requests | |
| # Function to fetch Google Maps info | |
| def fetch_google_maps_info(website_name): | |
| apify_client = ApifyClient("apify_api_uz0y556N4IG2aLcESj67kmnGSUpHF12XAkLp") | |
| run_input = {"searchStringsArray": [website_name]} | |
| run = apify_client.actor("nwua9Gu5YrADL7ZDj").call(run_input=run_input) | |
| items = list(apify_client.dataset(run["defaultDatasetId"]).iterate_items()) | |
| return items[0] if items else None | |
| # Function to fetch website content using Apify actor | |
| def fetch_website_content(website_url): | |
| apify_client = ApifyClient("apify_api_uz0y556N4IG2aLcESj67kmnGSUpHF12XAkLp") | |
| run_input = {"startUrls": [website_url]} | |
| run = apify_client.actor("moJRLRc85AitArpNN").call(run_input=run_input) | |
| items = list(apify_client.dataset(run["defaultDatasetId"]).iterate_items()) | |
| return items[0] if items else None | |
| # Function to fetch weather info | |
| def fetch_weather_info(lat, lon): | |
| API_KEY = "91b23cab82ee530b2052c8757e343b0d" | |
| url = f"https://api.openweathermap.org/data/3.0/onecall?lat={lat}&lon={lon}&exclude=hourly,daily&appid={API_KEY}" | |
| response = requests.get(url) | |
| return response.json() | |
| # Streamlit app | |
| st.title("Data Visualization") | |
| website_name = st.text_input("Enter a website / company name:") | |
| if website_name: | |
| google_maps_data = fetch_google_maps_info(website_name) | |
| if google_maps_data: | |
| # Display website link in a specific output box | |
| website_link = google_maps_data.get('website') | |
| st.text_area("Website Link:", website_link) | |
| # Fetch and display website content | |
| website_content = fetch_website_content(website_link) | |
| if website_content: | |
| st.subheader("Scraped Website Content") | |
| content_df = pd.DataFrame(website_content) | |
| st.table(content_df) | |
| # Display location and fetch weather info | |
| lat = google_maps_data["location"]["lat"] | |
| lng = google_maps_data["location"]["lng"] | |
| if lat and lng: | |
| st.map(pd.DataFrame({'lat': [lat], 'lon': [lng]})) # Display the map | |
| weather_data = fetch_weather_info(lat, lng) | |
| current_weather = weather_data.get("current", {}) | |
| temp_in_celsius = current_weather.get('temp') - 273.15 | |
| st.write(f"**Location:** {lat}, {lng}") | |
| st.write(f"**Temperature:** {temp_in_celsius:.2f}°C") | |
| st.write(f"**Weather:** {current_weather.get('weather')[0].get('description')}") | |
| # Occupancy Data | |
| st.subheader("Occupancy Data") | |
| occupancy_data = google_maps_data.get('popularTimesHistogram', {}) | |
| for day, day_data in occupancy_data.items(): | |
| if day_data: | |
| hours = [entry['hour'] for entry in day_data] | |
| occupancy = [entry['occupancyPercent'] for entry in day_data] | |
| st.write(day) | |
| st.bar_chart(pd.Series(occupancy, index=hours), use_container_width=True) | |
| # Review Count and Distribution | |
| st.subheader("Review Count and Distribution") | |
| st.write(f"Total Reviews Count: {google_maps_data['reviewsCount']}") | |
| review_distribution = google_maps_data['reviewsDistribution'] | |
| days_order = ['Mo', 'Tu', 'We', 'Th', 'Fr', 'Sa', 'Su'] | |
| ordered_distribution = {day: review_distribution.get(day, 0) for day in days_order} | |
| st.bar_chart(pd.Series(ordered_distribution), use_container_width=True) | |
| # Reviews Table | |
| st.subheader("Customer Reviews") | |
| reviews = google_maps_data.get('reviews', []) | |
| if reviews: | |
| review_df = pd.DataFrame(reviews) | |
| st.table(review_df[['name', 'text', 'publishAt', 'likesCount', 'stars']]) | |
| else: | |
| st.write("No reviews available.") | |
| else: | |
| st.write("No results found for this website / company name on Google Maps.") |