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| from io import StringIO | |
| from urllib.parse import urlparse | |
| import streamlit as st | |
| import requests | |
| from dotenv import load_dotenv | |
| import os | |
| import time | |
| from helper.upload_response import upload_response | |
| from helper.upload_File import uploadFile | |
| from helper.button_behaviour import hide_button, unhide_button | |
| from helper.initialize_analyze_session import initialize_analyze_session | |
| import pandas as pd | |
| import asyncio | |
| import json | |
| from pymongo import MongoClient | |
| from helper.data_field import data_field | |
| class SeoOffPageAnalyst: | |
| def __init__(self, model_url): | |
| self.uploaded_files = [] | |
| self.file_dict = {} | |
| self.model_url = model_url | |
| #self.analyst_name = analyst_name | |
| #self.data_src = data_src | |
| #self.analyst_description = analyst_description | |
| self.initialize() | |
| self.row1() | |
| def initialize(self): | |
| # FOR ENV | |
| load_dotenv() | |
| # AGENT NAME | |
| #st.header(self.analyst_name) | |
| def request_model(self, payload_txt, headers): | |
| response = requests.post(self.model_url, json=payload_txt, headers=headers) | |
| response.raise_for_status() | |
| output = response.json() | |
| text = output["outputs"][0]["outputs"][0]["results"]["text"]["data"]["text"] | |
| text = json.loads(text) | |
| backlinks = text[0] | |
| referring_domains = text[1] | |
| return text | |
| def fetch_data(self, data_field): | |
| mongodb_uri = os.getenv("MONGODB_URI") | |
| myclient = MongoClient(mongodb_uri) | |
| mydb = myclient.get_database() | |
| mycol = mydb["df_data"] | |
| # Sort by timestamp field in descending order | |
| x = mycol.find_one( | |
| {"data_field": data_field}, | |
| sort=[("timestamp", -1)] | |
| ) | |
| x = x["result"] | |
| return x | |
| def process(self): | |
| with st.spinner('SEO Off Page Analyst...', show_time=True): | |
| st.write('') | |
| headers = {"Content-Type": "application/json", "x-api-key": f"{os.getenv('x-api-key')}"} | |
| try: | |
| payload_txt = {"input_value": self.payload, "output_type": "text", "input_type": "chat"} | |
| payload_txt_model = self.request_model(payload_txt, headers) | |
| debug_info = {'data_field' : 'SEO Off Page Analyst', 'result': payload_txt_model} | |
| upload_response(debug_info) | |
| st.session_state['off_page_file_uploaded'] = '' | |
| count = 0 | |
| except Exception as e: | |
| pass | |
| st.session_state['analyzing'] = False | |
| def row1(self): | |
| st.session_state['analyzing'] = False | |
| self.payload = "" | |
| count = 0 | |
| try: | |
| session_off_page_file_uploaded = st.session_state['off_page_file_uploaded'] | |
| if session_off_page_file_uploaded == 'uploaded': | |
| count += 1 | |
| self.payload += self.fetch_data("Backlinks") | |
| except Exception as e: | |
| pass | |
| if count >= 1: | |
| summary = self.fetch_data("Client Summary") | |
| self.payload = summary + self.payload | |
| self.process() | |
| if __name__ == "__main__": | |
| st.set_page_config(layout="wide") | |
| upload = uploadFile() | |