mirror of
https://github.com/Marketscrape/marketscrape-web.git
synced 2026-06-12 15:04:14 -04:00
99 lines
4.4 KiB
Python
99 lines
4.4 KiB
Python
from django.shortcuts import render
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from django.views import View
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from .forms import MarketForm
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from .utils import *
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from .scraper_class import FacebookScraper
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import re
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import plotly.express as px
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import pandas as pd
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class Index(View):
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def get(self, request):
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form = MarketForm()
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context = {'form': form}
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return render(request, 'scraper/index.html', context)
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def post(self, request):
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form = MarketForm(request.POST)
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if form.is_valid():
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url = form.cleaned_data['url']
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shortened_url = re.search(r".*[0-9]", url).group(0)
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mobile_url = shortened_url.replace("www", "m")
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mobile_soup = create_soup(mobile_url, headers=None)
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base_soup = create_soup(url, headers=None)
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scraper_instance = FacebookScraper(mobile_soup, base_soup)
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if scraper_instance.is_listing_missing():
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return render(request, 'scraper/missing.html')
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image = scraper_instance.get_listing_image()
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days, hours = scraper_instance.get_listing_date()
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description = scraper_instance.get_listing_description()
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title = scraper_instance.get_listing_title()
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condition = scraper_instance.get_listing_condition()
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category = scraper_instance.get_listing_category()
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price = scraper_instance.get_listing_price()
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city = scraper_instance.get_listing_city()
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similar_descriptions, similar_prices, similar_urls, best_similar_product = find_viable_product(title, ramp_down=0.0)
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similar_prices = [float(price.replace(',', '')) for price in similar_prices]
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shortened_item_names = [description[:8] + '...' if len(description) > 10 else description for description in similar_descriptions]
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# Based on the best similar product, get the price, description, category, and URL
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idx = similar_urls.index(best_similar_product[1]["url"])
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best_similar_price = f"{similar_prices[idx]:,.2f}"
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best_similar_description = similar_descriptions[idx]
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best_similar_category = shortened_item_names[idx]
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best_similar_url = similar_urls[idx]
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best_similar_score = best_similar_product[1]["similarity"] * 100
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# Create a DataFrame from the data
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data = {'Product': shortened_item_names, 'Price': similar_prices, 'Description': similar_descriptions, 'URL': similar_urls}
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df = pd.DataFrame(data)
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# Used to determine colour range bounds
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cmin = min(similar_prices)
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cmax = max(similar_prices)
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# Ratio to limit the total bubble size
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desired_diameter = 150
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sizeref = cmax / desired_diameter
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fig = px.scatter(df, x='Product', text='Description', y='Price', size='Price', color='Price', color_continuous_scale='RdYlGn_r', range_color=[cmin, cmax])
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fig.update_traces(mode='markers', marker=dict(symbol='circle', sizemode='diameter', sizeref=sizeref))
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chart = fig.to_json()
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# Percetage difference between the listing price and the best found price
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list_best_context = percentage_difference(float(price), float(best_similar_price))
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# Needs to be redone
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price_rating = price_difference_rating(float(price), float(best_similar_price))
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categories = list(set(shortened_item_names))
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context = {
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'shortened_url': shortened_url,
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'mobile_url': mobile_url,
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'title': title,
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'price': f"{float(price):,.2f}",
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'chart': chart,
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'price_rating': round(price_rating, 1),
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'days': days,
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'hours': hours,
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'image': image,
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'description': description,
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'condition': condition,
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'category': category,
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'city': city,
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'categories': categories,
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'best_similar_price': best_similar_price,
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'best_similar_description': best_similar_description,
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'best_similar_category': best_similar_category,
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'best_similar_url': best_similar_url,
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'best_similar_score': f"{best_similar_score:.2f}",
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'list_best_context': list_best_context
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}
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return render(request, 'scraper/result.html', context)
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