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 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): 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 return (temperature) from zenrows import ZenRowsClient from bs4 import BeautifulSoup Zenrow_api=os.environ.get('Zenrow_api') # Initialize ZenRows client with your API key client = ZenRowsClient(str(Zenrow_api)) def get_rates(): # URL to scrape url = "https://www.kisandeals.com/mandiprices/ALL/TAMIL-NADU/ALL" # Fetch the webpage content using ZenRows response = client.get(url) # Check if the request was successful if response.status_code == 200: # Parse the raw HTML content with BeautifulSoup soup = BeautifulSoup(response.content, 'html.parser') # Find the table rows containing the data rows = soup.select('table tbody tr') data = {} for row in rows: # Extract commodity and price using BeautifulSoup columns = row.find_all('td') if len(columns) >= 2: commodity = columns[0].get_text(strip=True) price = columns[1].get_text(strip=True) if '₹' in price: data[commodity] = price return str(data)+" This are the prices for 1 kg" def get_news(): news=[] # URL to scrape url = "https://economictimes.indiatimes.com/news/economy/agriculture?from=mdr" # Fetch the webpage content using ZenRows response = client.get(url) # Check if the request was successful if response.status_code == 200: # Parse the raw HTML content with BeautifulSoup soup = BeautifulSoup(response.content, 'html.parser') # Find the table rows containing the data headlines = soup.find_all("div", class_="eachStory") for story in headlines: # Extract the headline headline = story.find('h3').text.strip() news.append(headline) return news def download_and_save_as_txt(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"pdf_file.pdf" # Save the media content to a .txt file txt_filepath = os.path.join(UPLOAD_FOLDER, filename) with open(txt_filepath, 'wb') as file: file.write(response.content) print(f"Media downloaded successfully and saved as {txt_filepath}") return txt_filepath except requests.exceptions.HTTPError as err: print(f"HTTP error occurred: {err}") except Exception as err: print(f"An error occurred: {err}") def download_file(url, extension): try: response = requests.get(url) response.raise_for_status() filename = f"{uuid.uuid4()}{extension}" file_path = os.path.join(UPLOAD_FOLDER, filename) with open(file_path, 'wb') as file: file.write(response.content) print(f"File downloaded and saved as {file_path}") return file_path except requests.exceptions.HTTPError as err: print(f"HTTP error occurred: {err}") except Exception as err: print(f"An error occurred: {err}") return None