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 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)

def get_rates():
    
    proxies = {
        "http": "http://160.86.242.23:8080",
        "https": "http://160.86.242.23:8080",
    }
    
    r = requests.get('https://www.kisandeals.com/mandiprices/ALL/TAMIL-NADU/ALL', proxies=proxies)
    soup = BeautifulSoup(r.text, 'html.parser')
    
    # Finding the table in the HTML
    table = soup.find('table')
    
    # Check if a table was found
    if table:
        # Convert the HTML table to a DataFrame
        df = pd.read_html(str(table))[0]
        
        # Dropping the 'Quintal Price' column if it exists
        if 'Quintal Price' in df.columns:
            df.drop(columns=['Quintal Price'], inplace=True)
        
        # Creating a dictionary from the DataFrame
        d = {}
        for i in range(len(df)):
            d[df.iloc[i, 0]] = df.iloc[i, 1]
        
        return str(d) + ' These prices are for 1 kg'
    else:
        return get_rates()