435 lines
24 KiB
Python
435 lines
24 KiB
Python
import requests
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import streamlit as st
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import json
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import pandas as pd
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import base64
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import plotly.express as px
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import time
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from datetime import datetime
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def run_pagespeed(url, api_key=None, strategy='DESKTOP', locale='en'):
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"""Fetches and processes PageSpeed Insights data."""
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serviceurl = 'https://www.googleapis.com/pagespeedonline/v5/runPagespeed'
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base_url = f"{serviceurl}?url={url}&strategy={strategy}&locale={locale}&category=performance&category=accessibility&category=best-practices&category=seo"
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if api_key:
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base_url += f"&key={api_key}"
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#if categories:
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# base_url += f"&category={','.join(categories)}"
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try:
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response = requests.get(base_url)
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response.raise_for_status() # Raise an exception for bad status codes
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data = response.json()
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return data
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except requests.exceptions.RequestException as e:
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st.error(f"Error fetching PageSpeed Insights data: {e}")
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return None
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def display_results(data):
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"""Presents PageSpeed Insights data in a user-friendly format."""
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# Credits: https://www.danielherediamejias.com/pagespeed-insights-api-with-python/
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st.subheader("PageSpeed Insights Report")
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# Extract scores from the PageSpeed Insights data
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performance_overall_score = data['lighthouseResult']['categories']['performance']['score'] * 100
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accessibility_overall_score = data['lighthouseResult']['categories']['accessibility']['score'] * 100
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seo_overall_score = data['lighthouseResult']['categories']['seo']['score'] * 100
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best_practices_score = data['lighthouseResult']['categories']['best-practices']['score'] * 100
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col1, col2 = st.columns([5, 5])
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with col1:
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# Display metrics with improved help messages
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st.subheader("Performance")
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category_description = data['lighthouseResult']['categories']['performance'].get('description', "No description available")
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if "No description available" in category_description:
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category_description = "This score represents Google's assessment of your page's speed. A higher percentage indicates better performance."
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st.metric(
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label="Overall Performance Score",
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value=f"{performance_overall_score:.0f}%",
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help=category_description
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)
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with col2:
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st.subheader("Accessibility")
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category_description = data['lighthouseResult']['categories']['accessibility'].get('description', "No description available")
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if "No description available" in category_description:
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category_description = "This score evaluates how accessible your page is to users with disabilities. A higher percentage means better accessibility."
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st.metric(
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label="Overall Accessibility Score",
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value=f"{accessibility_overall_score:.0f}%",
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help=category_description
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)
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col1, col2 = st.columns([5, 5])
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with col1:
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st.subheader("SEO")
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category_description = data['lighthouseResult']['categories']['seo'].get('description', "No description available")
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if "No description available" in category_description:
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category_description = "This score measures how well your page is optimized for search engines. A higher percentage indicates better SEO practices."
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st.metric(
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label="Overall SEO Score",
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value=f"{seo_overall_score:.0f}%",
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help=category_description
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)
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with col2:
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st.subheader("Best Practises")
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category_description = data['lighthouseResult']['categories']['best-practices'].get('description', "No description available")
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if "No description available" in category_description:
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category_description = "This score reflects how well your page follows best practices for web development. A higher percentage signifies adherence to best practices."
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st.metric(
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label="Overall Best Practices Score",
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value=f"{best_practices_score:.0f}%",
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help=category_description
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)
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# Display additional metrics from the article
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st.subheader("Additional Metrics")
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additional_metrics = []
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for metric_name, metric_value in {
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"First Contentful Paint (FCP)": data['lighthouseResult']['audits']['first-contentful-paint']['displayValue'],
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"Largest Contentful Paint (LCP)": data['lighthouseResult']['audits']['largest-contentful-paint']['displayValue'],
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"Time to Interactive (TTI)": data['lighthouseResult']['audits']['interactive']['displayValue'],
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"Total Blocking Time (TBT)": data['lighthouseResult']['audits']['total-blocking-time']['displayValue'],
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"Cumulative Layout Shift (CLS)": data['lighthouseResult']['audits']['cumulative-layout-shift']['displayValue']
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}.items():
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additional_metrics.append({"Metric": metric_name, "Value": metric_value})
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st.table(pd.DataFrame(additional_metrics))
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# 2.4.1.- Network Requests
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st.subheader("Network Requests")
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if 'network-requests' in data['lighthouseResult']['audits']:
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listrequests = []
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for item in data["lighthouseResult"]["audits"]["network-requests"]["details"]["items"]:
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endtime = item.get("endTime", "N/A")
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starttime = item.get("startTime", "N/A")
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transfersize = item.get("transferSize", "N/A")
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resourcesize = item.get("resourceSize", "N/A")
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url = item.get("url", "N/A")
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# Filter for significant requests (adjust thresholds as needed)
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if transfersize != "N/A" and transfersize > 100000 or resourcesize != "N/A" and resourcesize > 100000:
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# Convert bytes to MBs
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transfersize_mb = round(transfersize / 1048576, 2) # 1 MB = 1048576 bytes
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resourcesize_mb = round(resourcesize / 1048576, 2)
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list1 = [endtime, starttime, transfersize_mb, resourcesize_mb, url]
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listrequests.append(list1)
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if listrequests:
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st.dataframe(pd.DataFrame(listrequests, columns=["End Time", "Start Time", "Transfer Size (MB)", "Resource Size (MB)", "URL"]), use_container_width=True)
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else:
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st.write("No significant network requests found.")
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# 2.4.2.- Mainthread Work Breakdown
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st.subheader("Mainthread Work Breakdown")
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if 'mainthread-work-breakdown' in data['lighthouseResult']['audits']:
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mainthread_score = data["lighthouseResult"]["audits"]["mainthread-work-breakdown"]["score"]
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mainthread_duration = data["lighthouseResult"]["audits"]["mainthread-work-breakdown"]["displayValue"]
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st.write(f"Score: {mainthread_score}, Duration: {mainthread_duration}")
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# Extract data for visualization
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breakdown_data = []
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for item in data["lighthouseResult"]["audits"]["mainthread-work-breakdown"]["details"]["items"]:
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duration = item.get("duration", "N/A")
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process = item.get("groupLabel", "N/A")
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if duration != "N/A": # Only include non-N/A values
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breakdown_data.append({"Process": process, "Duration (ms)": duration}) # Make sure the column name is "Process"
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# Create a bar chart using Streamlit
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if breakdown_data:
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fig = px.bar(
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pd.DataFrame(breakdown_data),
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x="Process", # Now using the "Process" column
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y="Duration (ms)",
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title="Mainthread Work Breakdown",
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labels={"Process": "Process", "Duration (ms)": "Duration (ms)"},
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hover_data=["Process", "Duration (ms)"]
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)
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.write("No significant main thread work breakdown data found.")
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# 2.4.3.- Use of Passive Event Listeners
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st.subheader("Use of Passive Event Listeners")
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if 'uses-passive-event-listeners' in data['lighthouseResult']['audits']:
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event_listeners = data["lighthouseResult"]["audits"]["uses-passive-event-listeners"]["score"]
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st.write(f"Score: {event_listeners}")
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listevents = []
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for item in data["lighthouseResult"]["audits"]["uses-passive-event-listeners"]["details"]["items"]:
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url = item.get("url", "N/A")
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line = item.get("label", "N/A")
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if url != "N/A" and line != "N/A":
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list1 = [url, line]
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listevents.append(list1)
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if listevents:
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st.table(pd.DataFrame(listevents, columns=["URL", "Code Line"]))
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else:
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st.write("No significant passive event listener data found.")
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# 2.4.4.- Dom Size
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st.subheader("DOM Size")
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if 'dom-size' in data['lighthouseResult']['audits']:
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dom_size_score = data["lighthouseResult"]["audits"]["dom-size"]["score"]
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dom_size_elements = data["lighthouseResult"]["audits"]["dom-size"]["displayValue"]
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st.write(f"Score: {dom_size_score}, DOM Size: {dom_size_elements}")
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st.info("A large DOM can impact performance, especially for mobile devices. Consider optimizing your HTML structure and using techniques like lazy loading to improve page load times.")
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# 2.4.5.- OffScreen Images
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st.subheader("Offscreen Images")
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if 'offscreen-images' in data['lighthouseResult']['audits']:
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offscreen_images_score = data["lighthouseResult"]["audits"]["offscreen-images"]["score"]
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offscreen_images = data["lighthouseResult"]["audits"]["offscreen-images"]["displayValue"]
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st.write(f"Score: {offscreen_images_score}, Offscreen Images: {offscreen_images}")
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listoffscreenimages = []
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for item in data["lighthouseResult"]["audits"]["offscreen-images"]["details"]["items"]:
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url = item.get("url", "N/A")
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totalbytes = item.get("totalBytes", "N/A")
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wastedbytes = item.get("wastedBytes", "N/A")
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wastedpercent = item.get("wastedPercent", "N/A")
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if url != "N/A":
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list1 = [url, totalbytes, wastedbytes, wastedpercent]
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listoffscreenimages.append(list1)
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if listoffscreenimages:
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st.table(pd.DataFrame(listoffscreenimages, columns=["URL", "Total Bytes", "Wasted Bytes", "Wasted Percentage"]))
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st.info("Consider using lazy loading for offscreen images. This will delay their loading until they are visible in the viewport, improving initial page load times.")
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else:
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st.write("No significant offscreen image data found.")
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# 2.4.6.- Critical Requests Chains
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st.subheader("Critical Request Chains")
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if 'critical-request-chains' in data['lighthouseResult']['audits']:
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critical_requests = data["lighthouseResult"]["audits"]["critical-request-chains"]["displayValue"]
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st.write(f"Number of Critical Request Chains: {critical_requests}")
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listchains = []
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for keys in data["lighthouseResult"]["audits"]["critical-request-chains"]["details"]["chains"].keys():
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try:
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for values in data["lighthouseResult"]["audits"]["critical-request-chains"]["details"]["chains"][keys]["children"].values():
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url = values["request"]["url"]
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startime = values["request"]["startTime"]
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endtime = values["request"]["endTime"]
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transfersize = values["request"]["transferSize"]
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list1 = [url,startime,endtime,transfersize, keys]
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listchains.append(list1)
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except:
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continue
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if listchains:
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st.table(pd.DataFrame(listchains, columns=["URL", "Start Time", "End Time", "Transfer Size", "Chain"]))
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st.info("Optimizing the critical request chains can significantly improve the loading time of your website. Consider prioritizing essential resources and deferring non-critical ones.")
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else:
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st.write("No significant critical request chain data found.")
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# 2.4.7.- Total Bytes Weight
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st.subheader("Total Bytes Weight")
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if 'total-byte-weight' in data['lighthouseResult']['audits']:
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bytes_weight_score = data["lighthouseResult"]["audits"]["total-byte-weight"]["score"]
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bytes_weight = data["lighthouseResult"]["audits"]["total-byte-weight"]["displayValue"]
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st.write(f"Score: {bytes_weight_score}, Total Bytes Weight: {bytes_weight}")
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listbytes = []
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for item in data["lighthouseResult"]["audits"]["total-byte-weight"]["details"]["items"]:
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url = item.get("url", "N/A")
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bytes_total = item.get("totalBytes", "N/A")
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if url != "N/A":
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list1 = [url, bytes_total]
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listbytes.append(list1)
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if listbytes:
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st.table(pd.DataFrame(listbytes, columns=["URL", "Total Bytes"]))
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st.info("Reducing the total bytes weight of your website is crucial for improving performance, especially on mobile devices and for users with slower internet connections.")
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else:
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st.write("No significant total bytes weight data found.")
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# # 2.4.8.- Use of responsive images
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# st.subheader("Use of Responsive Images")
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# if 'uses-responsive-images' in data['lighthouseResult']['audits']:
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# responsive_images_score = data["lighthouseResult"]["audits"]["uses-responsive-images"]["score"]
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# responsive_image_savings = data["lighthouseResult"]["audits"]["uses-responsive-images"]["displayValue"]
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# st.write(f"Score: {responsive_images_score}, Potential Savings: {responsive_image_savings}")
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# listresponsivesavings = []
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# for item in data["lighthouseResult"]["audits"]["uses-responsive-images"]["details"]["items"]:
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# url = item.get("url", "N/A")
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# wastedbytes = item.get("wastedBytes", "N/A")
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# totalbytes = item.get("totalBytes", "N/A")
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# if url != "N/A":
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# list1 = [url, wastedbytes, totalbytes]
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# listresponsivesavings.append(list1)
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# if listresponsivesavings:
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# st.table(pd.DataFrame(listresponsivesavings, columns=["URL", "Wasted Bytes", "Total Bytes"]))
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# st.info("Serving images in different sizes based on the user's device screen size can significantly reduce download times and improve performance.")
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# else:
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# st.write("No significant responsive image data found.")
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# 2.4.9.- Render Blocking Resources
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st.subheader("Render Blocking Resources")
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if 'render-blocking-resources' in data['lighthouseResult']['audits']:
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blocking_resources_score = data["lighthouseResult"]["audits"]["render-blocking-resources"]["score"]
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# Handle potential missing 'displayValue'
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blocking_resoures_savings = data["lighthouseResult"]["audits"]["render-blocking-resources"].get("displayValue", "N/A")
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st.write(f"Score: {blocking_resources_score}, Potential Savings: {blocking_resoures_savings}")
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listblockingresources = []
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for item in data["lighthouseResult"]["audits"]["render-blocking-resources"]["details"]["items"]:
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url = item.get("url", "N/A")
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totalbytes = item.get("totalBytes", "N/A")
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wastedbytes = item.get("wastedMs", "N/A")
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if url != "N/A":
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list1 = [url, totalbytes, wastedbytes]
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listblockingresources.append(list1)
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if listblockingresources:
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st.table(pd.DataFrame(listblockingresources, columns=["URL", "Total Bytes", "Wasted Milliseconds"]))
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st.info("Render-blocking resources can delay the initial rendering of your page, making it appear slow to users. Consider optimizing the loading of critical resources and deferring non-critical ones.")
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else:
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st.write("No significant render-blocking resource data found.")
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# 2.4.10.- Use of Rel Preload
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st.subheader("Use of Rel Preload")
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if 'uses-rel-preload' in data['lighthouseResult']['audits']:
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rel_preload_score = data["lighthouseResult"]["audits"]["uses-rel-preload"]["score"]
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rel_preload_savings = data["lighthouseResult"]["audits"]["uses-rel-preload"]["displayValue"]
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st.write(f"Score: {rel_preload_score}, Potential Savings: {rel_preload_savings}")
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listrelpreload = []
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for item in data["lighthouseResult"]["audits"]["uses-rel-preload"]["details"]["items"]:
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url = item.get("url", "N/A")
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wastedms = item.get("wastedMs", "N/A")
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if url != "N/A":
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list1 = [url, wastedms]
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listrelpreload.append(list1)
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if listrelpreload:
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st.table(pd.DataFrame(listrelpreload, columns=["URL", "Wasted Milliseconds"]))
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st.info("The `rel=preload` attribute can be used to prioritize loading essential resources, improving initial page load times.")
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else:
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st.write("No significant rel preload data found.")
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# 2.4.11.- Estimated Input Latency (DEPRECATED)
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st.subheader("Estimated Input Latency")
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if 'estimated-input-latency' in data['lighthouseResult']['audits']:
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eil_score = data["lighthouseResult"]["audits"]["estimated-input-latency"]["score"]
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eil_duration = data["lighthouseResult"]["audits"]["estimated-input-latency"]["displayValue"]
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st.write(f"Score: {eil_score}, Duration: {eil_duration}")
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# 2.4.12.- Redirects
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st.subheader("Redirects")
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if 'redirects' in data['lighthouseResult']['audits']:
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redirects_score = data["lighthouseResult"]["audits"]["redirects"]["score"]
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redirect_savings = data["lighthouseResult"]["audits"]["redirects"]["displayValue"]
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st.write(f"Score: {redirects_score}, Potential Savings: {redirect_savings}")
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listredirects = []
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for item in data["lighthouseResult"]["audits"]["redirects"]["details"]["items"]:
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url = item.get("url", "N/A")
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wastedms = item.get("wastedMs", "N/A")
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list1 = [url,wastedms]
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listredirects.append(list1)
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st.table(pd.DataFrame(listredirects, columns=["URL", "Wasted Milliseconds"]))
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# 2.4.13.- Unused JavaScript
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st.subheader("Unused JavaScript")
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if 'unused-javascript' in data['lighthouseResult']['audits']:
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unused_js_score = data["lighthouseResult"]["audits"]["unused-javascript"]["score"]
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unused_js_savings = data["lighthouseResult"]["audits"]["unused-javascript"]["displayValue"]
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st.write(f"Score: {unused_js_score}, Potential Savings: {unused_js_savings}")
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listunusedjavascript = []
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for item in data["lighthouseResult"]["audits"]["unused-javascript"]["details"]["items"]:
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url = item.get("url", "N/A")
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totalbytes = item.get("totalBytes", "N/A")
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wastedbytes = item.get("wastedBytes", "N/A")
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wastedpercentage= item.get("wastedPercent", "N/A")
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list1 = [url, totalbytes, wastedbytes, wastedpercentage]
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listunusedjavascript.append(list1)
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st.table(pd.DataFrame(listunusedjavascript, columns=["URL", "Total Bytes", "Wasted Bytes", "Wasted Percentage"]))
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# 2.4.14.- Total Blocking Time
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st.subheader("Total Blocking Time")
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if 'total-blocking-time' in data['lighthouseResult']['audits']:
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blocking_time_score = data["lighthouseResult"]["audits"]["total-blocking-time"]["score"]
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blocking_time_duration = data["lighthouseResult"]["audits"]["total-blocking-time"]["displayValue"]
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st.write(f"Score: {blocking_time_score}, Duration: {blocking_time_duration}")
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# 2.4.15.- First Meaningful Paint
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st.subheader("First Meaningful Paint")
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if 'first-meaningful-paint' in data['lighthouseResult']['audits']:
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fmp_score = data["lighthouseResult"]["audits"]["first-meaningful-paint"]["score"]
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fmp = data["lighthouseResult"]["audits"]["first-meaningful-paint"]["displayValue"]
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st.write(f"Score: {fmp_score}, Time: {fmp}")
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# 2.4.16.- Cumulative Layout Shift
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st.subheader("Cumulative Layout Shift")
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if 'cumulative-layout-shift' in data['lighthouseResult']['audits']:
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cls_score = data["lighthouseResult"]["audits"]["cumulative-layout-shift"]["score"]
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cls = data["lighthouseResult"]["audits"]["cumulative-layout-shift"]["displayValue"]
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st.write(f"Score: {cls_score}, Value: {cls}")
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# 2.4.17.- Network RTT
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st.subheader("Network RTT")
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if 'network-rtt' in data['lighthouseResult']['audits']:
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network_rtt = data["lighthouseResult"]["audits"]["network-rtt"]["displayValue"]
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st.write(f"Network RTT: {network_rtt}")
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# 2.4.18.- Speed Index
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st.subheader("Speed Index")
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if 'speed-index' in data['lighthouseResult']['audits']:
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speed_index_score = data["lighthouseResult"]["audits"]["speed-index"]["score"]
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speed_index = data["lighthouseResult"]["audits"]["speed-index"]["displayValue"]
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st.write(f"Score: {speed_index_score}, Speed Index: {speed_index}")
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|
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# 2.4.19.- Use of Rel Preconnect
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st.subheader("Use of Rel Preconnect")
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if 'uses-rel-preconnect' in data['lighthouseResult']['audits']:
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rel_preconnect_score = data["lighthouseResult"]["audits"]["uses-rel-preconnect"]["score"]
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rel_preconnect_warning = data["lighthouseResult"]["audits"]["uses-rel-preconnect"]["warnings"]
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|
st.write(f"Score: {rel_preconnect_score}, Warnings: {rel_preconnect_warning}")
|
|
|
|
# # 2.4.20.- Use of Optimized Images
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|
# st.subheader("Use of Optimized Images")
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|
# if 'uses-optimized-images' in data['lighthouseResult']['audits']:
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|
# optimized_images_score = data["lighthouseResult"]["audits"]["uses-optimized-images"]["score"]
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|
# optimized_images = data["lighthouseResult"]["audits"]["uses-optimized-images"]["displayValue"]
|
|
# st.write(f"Score: {optimized_images_score}, Potential Savings: {optimized_images}")
|
|
# listoptimisedimages = []
|
|
# for item in data["lighthouseResult"]["audits"]["uses-optimized-images"]["details"]["items"]:
|
|
# url = item.get("url", "N/A")
|
|
# wastedbytes = item.get("wastedBytes", "N/A")
|
|
# totalbytes = item.get("totalBytes", "N/A")
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|
# list1 = [url, wastedbytes, totalbytes]
|
|
# listoptimisedimages.append(list1)
|
|
# st.table(pd.DataFrame(listoptimisedimages, columns=["URL", "Wasted Bytes", "Total Bytes"]))
|
|
|
|
|
|
|
|
|
|
def google_pagespeed_insights():
|
|
|
|
st.markdown("<h1 style='text-align: center; color: #1565C0;'>PageSpeed Insights Analyzer</h1>", unsafe_allow_html=True)
|
|
st.markdown(
|
|
"<h3 style='text-align: center;'>Get detailed insights into your website's performance! Powered by Google PageSpeed Insights <a href='https://developer.chrome.com/docs/lighthouse/overview/'>[Learn More]</a></h3>",
|
|
unsafe_allow_html=True
|
|
)
|
|
|
|
# User Input
|
|
with st.form("pagespeed_form"):
|
|
url = st.text_input("Enter Website URL", placeholder="https://www.example.com")
|
|
api_key = st.text_input("Enter Google API Key (Optional)", placeholder="Your API Key", help="Get your API key here: [https://developers.google.com/speed/docs/insights/v5/get-started#key](https://developers.google.com/speed/docs/insights/v5/get-started#key)")
|
|
device = st.selectbox("Choose Device", ["Mobile", "Desktop"])
|
|
locale = st.selectbox("Choose Locale", ["en", "fr", "es", "de", "ja"])
|
|
categories = st.multiselect(
|
|
"Select Categories to Analyze",
|
|
['PERFORMANCE', 'ACCESSIBILITY', 'BEST_PRACTICES', 'SEO'],
|
|
default=['PERFORMANCE', 'ACCESSIBILITY', 'BEST_PRACTICES', 'SEO']
|
|
)
|
|
|
|
submitted = st.form_submit_button("Analyze")
|
|
|
|
if submitted:
|
|
if not url:
|
|
st.error("Please provide the website URL.")
|
|
else:
|
|
# Fetch and process PageSpeed Insights data
|
|
strategy = 'mobile' if device == "Mobile" else 'desktop'
|
|
data = run_pagespeed(url, api_key, strategy=strategy, locale=locale)
|
|
|
|
# Display results in a user-friendly format
|
|
if data:
|
|
display_results(data)
|
|
else:
|
|
st.error("Failed to retrieve PageSpeed Insights data.")
|