app.py

import os 
import wget
import sys
import bs4
import json
import pandas as pd
from huggingface_hub import InferenceClient
import urllib.request
import gradio as gr

def get_menu():
    fp = urllib.request.urlopen("https://www.sw-ka.de/en/hochschulgastronomie/speiseplan/mensa_adenauerring/")
    mybytes = fp.read()

    html_content = mybytes.decode("utf8")

    #html_content = "".join(open('index.html',mode='r',encoding='utf-8').readlines())
    # Parse the HTML content

    # Parse the HTML using BeautifulSoup
    soup = bs4.BeautifulSoup(html_content, 'html.parser')

    canteen_div = soup.find('div', id='canteen_day_1')
    # Find all tables within the canteen_div
    tables = canteen_div.find_all('table')

    foods = []
    prices = []
    nutri = []
    line_names = []



    cnt = 0
    canteen_div = soup.find('div', id='canteen_day_1')
    # Find all tables within the canteen_div
    tables = canteen_div.find_all('table')
    # Iterate over each table
    for table in tables:
        # Extract food name
    # Find all table rows with a class starting with "mt-"
        menu_items = table.find_all('tr', class_=lambda class_name: class_name and class_name.startswith('mt-'))

        # Iterate through each menu item
        for item in menu_items:
            food_name = item.find('span', class_='bg').text.strip()
            
            # Extract price
            price = item.find('span', class_='bgp price_1').text.strip()
            
            # Extract nutritional information
            nutritional_info = {}
            nutritional_data = item.find('div', class_='nutrition_facts')
            if nutritional_data:
                for element in nutritional_data.find_all('div', class_=['energie', 'proteine', 'kohlenhydrate', 'zucker', 'fett', 'gesaettigt', 'salz']):
                    key = element.find('div').text.strip()
                    value = element.find_all('div')[1].text.strip()
                    nutritional_info[key] = value
            
            # Print extracted information

            #print(f"\nFood Name: {food_name}")
            foods.append(food_name)
            prices.append(price)
            try:
                nutri.append(json.dumps(nutritional_info['Energie'], indent=4))
            except:
                nutri.append("")
            #print(f"Price: {price}")
            if nutritional_info:
                #print("Nutritional Information:")
                for key, value in nutritional_info.items():
                    pass
                    #print(f"- {key}: {value}")
            else:
                pass
                #print("No nutritional information available.")
            cnt+=1
        break
        # Iterate over each row
        # Find all rows (tr) with class 'mensatype_rows'
        #rows = table.find_all('tr', class_='mensatype_rows')
    # Find all menu items within the table

    # Find all rows with class 'mensatype_rows'
    canteen_div = soup.find('div', id='canteen_day_1')
    # Find all tables within the canteen_div
    tables = canteen_div.find_all('table')


    # Iterate over each table
    for table in tables:
        # Iterate over each row
        # Find all rows (tr) with class 'mensatype_rows'
        rows = table.find_all('tr', class_='mensatype_rows')
        
        # Iterate over each row
        for row in rows:
            # Extract the row name
            row_name = row.find('div').get_text(strip=True)
            menu_titles = row.find_all('td', class_='menu-title')
            
            # Iterate over each food item
            for menu_title in menu_titles:
                line_names.append(row_name)

    menu = ""
    df = pd.DataFrame(zip(line_names,foods,prices,nutri),columns=['line','food','price','nutri'])
    #df = df[~df['line'].str.contains("Abendessen")]
    #df = df[~df['line'].str.contains("pizza")]
    #df = df[~df['line'].str.contains("werk")]
    df_line = df.groupby('line', sort=False)
    for line, df_group in df_line:
        menu+= "Line Name: " + line + "\n"
        for idx,row in df_group.iterrows():
            menu+=row['food'] + "\n"
            menu+= "Price: " + row['price'] + "\n"
            menu+= "Calories: " + row['nutri'] + "\n"
    return menu

def reply_bot(message, history):
    client = InferenceClient(model="https://hardy-casual-adder.ngrok-free.app")
    curr_prompt = message
    try:
        print(curr_prompt)
    #answer = client.text_generation(prompt=prompt, max_new_tokens=512)
        answer = ""
        for token in client.text_generation(prompt=curr_prompt, max_new_tokens=512, stream=True):
            answer+=token
            yield answer
    except:
        return "Clear History or ask FR to increase Context Window. Current capacity only 4k tokens"

    #return answer

gr.ChatInterface(reply_bot).launch()