import os from bs4 import BeautifulSoup import os from mistralai import Mistral 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 import cv2 import numpy as np import pytesseract import subprocess from PIL import Image from pypdf import PdfReader from ai71 import AI71 import os import PyPDF2 import pandas as pd model = "mistral-large-latest" api_key='xQ2Zhfsp4cLar4lvBRDWZKljvp0Ej427' client = Mistral(api_key=api_key) def extract_text_from_image(image_path): img = cv2.imread(image_path) if img is None: raise ValueError("Image not found or unable to load") img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) text = pytesseract.image_to_string(img_rgb) return text from inference_sdk import InferenceHTTPClient import base64 UPLOAD_FOLDER = '/code/uploads' if not os.path.exists(UPLOAD_FOLDER): os.makedirs(UPLOAD_FOLDER) pdf_text='' AI71_API_KEY = os.environ.get('AI71_API_KEY') def generate_response(query,chat_history): chat_response = client.chat.complete( model= model, messages = [ { "role": "user", "content": f"{User_querry}? provide response within 2 sentence", }, ] ) return chat_response.choices[0].message.content 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): try: CLIENT = InferenceHTTPClient( api_url="https://detect.roboflow.com", api_key="oF1aC4b1FBCDtK8CoKx7" ) result = CLIENT.infer(filepath, model_id="pest-detection-ueoco/1") a= result['predictions'][0] if a=='x': return 'APHIDS' return a except: return None def predict_disease(filepath): try: CLIENT = InferenceHTTPClient( api_url="https://classify.roboflow.com", api_key="oF1aC4b1FBCDtK8CoKx7" ) result = CLIENT.infer(filepath, model_id="plant-disease-detection-iefbi/1") a= result['predicted_classes'][0] if a=='x': return 'APHIDS' return a except: return None def convert_img(url, account_sid, auth_token): if 1==1: # Make the request to the media URL with authentication response = requests.get(url.replace(' ',''), 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.replace(' ','')) media_id = parsed_url.path.split('/')[-1] # Get the last part of the URL path filename = f"image.jpg" # 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 else : return 'errir in process none' 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') temp = soup.find('div', class_='BNeawe iBp4i AP7Wnd').text return (temp) 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): global pdf_text 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}") pdf_text=extract_text_from_pdf(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 extract_text_from_pdf(pdf_path): global pdf_text with open(pdf_path, 'rb') as file: reader = PyPDF2.PdfReader(file) pdf_text = '' for page_num in range(len(reader.pages)): page = reader.pages[page_num] pdf_text += page.extract_text() return pdf_text def respond_pdf(query): extracted_text=pdf_text res = '' for chunk in AI71(AI71_API_KEY).chat.completions.create( model="tiiuae/falcon-11b", messages=[ {"role": "system", "content": "You are a pdf answering assistant and you have a pdf as a data."}, {"role": "user", "content": f"Content:{extracted_text},Query:{query}"}, ], stream=True, ): if chunk.choices[0].delta.content: res += chunk.choices[0].delta.content return ( res.replace("User:",'').strip()) def booktask(data): res = '' for chunk in AI71(AI71_API_KEY).chat.completions.create( model="tiiuae/falcon-11b", messages=[ {"role": "system", "content": "You are an assistant."}, {"role": "user", "content": f"My bookkeeping data is {data}.Provide the data in points."}, ], stream=True, ): if chunk.choices[0].delta.content: res += chunk.choices[0].delta.content return ( res.replace("User:",'').strip()) def return_bookdata(querry,data): res = '' for chunk in AI71(AI71_API_KEY).chat.completions.create( model="tiiuae/falcon-11b", messages=[ {"role": "system", "content": "You are an assistant."}, {"role": "user", "content": f"My notes data is {data}.user:{querry.replace('bookkeeping','data')}.Give the format of bookkeeping data in points.Make your response very concise to maximum of 10 points"}, ], stream=True, ): if chunk.choices[0].delta.content: res += chunk.choices[0].delta.content return ( res.replace("User:",'').strip())