Update app.py

app.py

@@ -1,30 +1,46 @@
 import streamlit as st
 import pandas as pd
-import requests
 from apify_client import ApifyClient
+import requests
 
 # Function to fetch Google Maps info using the antonces~gmaps actor
 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)
+    run = apify_client.actor("antonces~gmaps").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 from OpenWeatherMap API
-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()
-
-# Function to fetch website content using the antonces~web-scraper-task actor
-def fetch_website_content(website_url):
-    apify_client = ApifyClient("apify_api_uz0y556N4IG2aLcESj67kmnGSUpHF12XAkLp")
-    run_input = {}
-    run = apify_client.actor("moJRLRc85AitArpNN").call(run_input=run_input)
-    items = list(apify_client.dataset(run["defaultDatasetId"]).iterate_items())
-    return items if items else None
+# Function to fetch customer reviews using the new API
+def fetch_customer_reviews(location_query):
+    client = ApifyClient("apify_api_uz0y556N4IG2aLcESj67kmnGSUpHF12XAkLp")
+    run_input = {
+        "searchStringsArray": ["restaurant"],
+        "locationQuery": location_query,
+        "maxCrawledPlacesPerSearch": 50,
+        "language": "en",
+        "maxImages": None,
+        "onlyDataFromSearchPage": False,
+        "includeWebResults": False,
+        "deeperCityScrape": False,
+        "maxReviews": None,
+        "oneReviewPerRow": False,
+        "reviewsSort": "newest",
+        "reviewsFilterString": "",
+        "scrapeReviewerName": True,
+        "scrapeReviewerId": True,
+        "scrapeReviewerUrl": True,
+        "scrapeReviewId": True,
+        "scrapeReviewUrl": True,
+        "scrapeResponseFromOwnerText": True,
+        "countryCode": None,
+        "searchMatching": "all",
+        "placeMinimumStars": "",
+        "skipClosedPlaces": False,
+        "allPlacesNoSearchAction": "",
+    }
+    run = client.actor("mc9KJTQJg3zfQpANg/nwua9Gu5YrADL7ZDj").call(run_input=run_input)
+    return list(client.dataset(run["defaultDatasetId"]).iterate_items())
 
 # Streamlit app for Data Visualization
 st.title("Data Visualization")
@@ -33,60 +49,19 @@ st.title("Data Visualization")
 website_name = st.text_input("Enter a website / company name:")
 
 if website_name:
-    # Initialize the progress bar
-    progress_bar = st.progress(0)
-    
     # Fetch Google Maps data
     google_maps_data = fetch_google_maps_info(website_name)
-    progress_bar.progress(50)
-
+    
     if google_maps_data:
-        # Display website link
-        website_link = google_maps_data.get('website')
-        st.text_area("Website Link:", website_link)
-
-        # Display location and fetch weather info
-        lat = google_maps_data["location"]["lat"]
-        lng = google_maps_data["location"]["lng"]
-        st.map(pd.DataFrame({'lat': [lat], 'lon': [lng]}))
-        weather_data = fetch_weather_info(lat, lng)
-        current_weather = weather_data.get("current", {})
-        temp = current_weather.get('temp')
-        temp_in_celsius = 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')}")
+        location_query = google_maps_data.get("locationQuery")
+        reviews_data = fetch_customer_reviews(location_query)
         
-        # Display Occupancy Data
-        st.subheader("Occupancy Data")
-        occupancy_data = google_maps_data.get('popularTimesHistogram', {})
-        for day, day_data in occupancy_data.items():
-            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))
-
-        # Display 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.get('reviewsDistribution', {})
-        st.bar_chart(pd.Series(review_distribution))
+        # Display Google Maps data
+        # ... (use the original display code for Google Maps data here) ...
         
-        # Display Reviews Table
-        st.subheader("Customer Reviews")
-        reviews = google_maps_data.get('reviews', [])
-        review_df = pd.DataFrame(reviews)
+        # Display reviews_data
+        review_df = pd.DataFrame(reviews_data)
+        st.subheader("Customer Reviews from New API")
         st.table(review_df[['name', 'text', 'publishAt', 'likesCount', 'stars']])
-        
-        # Fetch and Display Website Content
-        st.subheader("Website Content")
-        website_content_data = fetch_website_content(website_link)
-        progress_bar.progress(100)
-        
-        if website_content_data:
-            website_df = pd.DataFrame(website_content_data)
-            st.table(website_df)
-        else:
-            st.write("Unable to retrieve website content.")
     else:
         st.write("No results found for this website / company name on Google Maps.")