other_function.py

import os
from bs4 import BeautifulSoup
import requests
from requests.auth import HTTPBasicAuth
from PIL import Image
from io import BytesIO
import pandas as pd
from urllib.parse import urlparse
import os

from pypdf import PdfReader
from ai71 import AI71
import os
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import pandas as pd

from inference_sdk import InferenceHTTPClient
import base64
UPLOAD_FOLDER = '/code/uploads'
if not os.path.exists(UPLOAD_FOLDER):
    os.makedirs(UPLOAD_FOLDER)

AI71_API_KEY = os.environ.get('AI71_API_KEY')
def generate_response(query,chat_history):
    response = ''
    for chunk in AI71(AI71_API_KEY).chat.completions.create(
            model="tiiuae/falcon-180b-chat",
            messages=[
                {"role": "system", "content": "You are a best agricultural assistant.Remember to give response not more than 2 sentence.Greet the user if user greets you."},
                {"role": "user",
                 "content": f'''Answer the query based on history {chat_history}:{query}'''},
            ],
            stream=True,
    ):
        if chunk.choices[0].delta.content:
            response += chunk.choices[0].delta.content
    return response.replace("###", '').replace('\nUser:','')
class ConversationBufferMemory:
    def __init__(self, max_size=6):
        self.memory = []
        self.max_size = max_size

    def add_to_memory(self, interaction):
        self.memory.append(interaction)
        if len(self.memory) > self.max_size:
            self.memory.pop(0)  # Remove the oldest interaction

    def get_memory(self):
        return self.memory
def predict_pest(filepath):
    CLIENT = InferenceHTTPClient(
        api_url="https://detect.roboflow.com",
        api_key="oF1aC4b1FBCDtK8CoKx7"
    )
    result = CLIENT.infer(filepath, model_id="pest-detection-ueoco/1")
    return result['predictions'][0]
    

def predict_disease(filepath):
    CLIENT = InferenceHTTPClient(
        api_url="https://classify.roboflow.com",
        api_key="oF1aC4b1FBCDtK8CoKx7"
    )
    result = CLIENT.infer(filepath, model_id="plant-disease-detection-iefbi/1")
    return result['predicted_classes'][0]

def convert_img(url, account_sid, auth_token):
    try:
        # Make the request to the media URL with authentication
        response = requests.get(url, auth=HTTPBasicAuth(account_sid, auth_token))
        response.raise_for_status()  # Raise an error for bad responses

        # Determine a filename from the URL
        parsed_url = urlparse(url)
        media_id = parsed_url.path.split('/')[-1]  # Get the last part of the URL path
        filename = f"downloaded_media_{media_id}"

        # Save the media content to a file
        media_filepath = os.path.join(UPLOAD_FOLDER, filename)
        with open(media_filepath, 'wb') as file:
            file.write(response.content)
        
        print(f"Media downloaded successfully and saved as {media_filepath}")

        # Convert the saved media file to an image
        with open(media_filepath, 'rb') as img_file:
            image = Image.open(img_file)

            # Optionally, convert the image to JPG and save in UPLOAD_FOLDER
            converted_filename = f"image.jpg"
            converted_filepath = os.path.join(UPLOAD_FOLDER, converted_filename)
            image.convert('RGB').save(converted_filepath, 'JPEG')
            return converted_filepath

    except requests.exceptions.HTTPError as err:
        print(f"HTTP error occurred: {err}")
    except Exception as err:
        print(f"An error occurred: {err}")
def get_weather(city):
  city=city.strip()
  city=city.replace(' ',"+")
  r = requests.get(f'https://www.google.com/search?q=weather+in+{city}')

  soup=BeautifulSoup(r.text,'html.parser')
  temperature=soup.find('div',attrs={'class':'BNeawe iBp4i AP7Wnd'}).text
  degree=temperature[:-2]
  celcius=str(round((int(degree) - 32)* 5/9,1))+temperature[-2]+'C'
  return (celcius)
import scrapy
from scrapy.crawler import CrawlerProcess
import pandas as pd
from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings
import scrapy

class RateSpider(scrapy.Spider):
    name = 'rates'
    start_urls = ['https://www.kisandeals.com/mandiprices/ALL/TAMIL-NADU/ALL']

    def parse(self, response):
        rows = response.xpath('//table/tbody/tr')
        data = {}
        for row in rows:
            commodity = row.xpath('td[1]/text()').get()
            price = row.xpath('td[2]/text()').get()
            data[commodity] = price
        return data

def get_rates():
    # Set up a Scrapy process
    process = CrawlerProcess(get_project_settings())
    
    # Set up a dictionary to store the scraped data
    data = {}

    # Run the spider
    def crawler_finished(signal, sender, item, response, spider):
        data.update(item)
        
    process.signals.connect(crawler_finished, signal=scrapy.signals.item_scraped)
    process.crawl(RateSpider)
    process.start()  # This will block until the crawling is finished

    # Return the scraped data as a string (or format as needed)
    return str(data) + ' These prices are for 1 kg'