import streamlit as st import requests from dotenv import load_dotenv import os import pandas as pd import time import chardet from helper.telemetry import collect_telemetry from helper.upload_File import uploadFile from helper.button_behaviour import hide_button from helper.initialize_analyze_session import initialize_analyze_session class Linkedin: def __init__(self, model_url): 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) # EVALUATION FORM LINK url = os.getenv('Link') st.write('Evaluation Form: [Link](%s)' % url) # RETURN BUTTON try: if st.button("Return", type='primary'): st.switch_page("./pages/home.py") except Exception: pass ''' if 'linkedin_upload' not in st.session_state: st.session_state['linkedin_upload'] = '' def request_model(self, payload_txt): response = requests.post(self.model_url, json=payload_txt) response.raise_for_status() output = response.json() categories = [] current_footprint = [] number_of_backlinks = [] for key, value in output.items(): if key == 'json': for item in value: categories.append(item.get('category', 'N/A').replace('_', ' ').title()) current_footprint.append(item.get('current_footprint', 'N/A')) number_of_backlinks.append(item.get('best_of_breed_solution', 'N/A')) output = "" for i in range(len(categories)): output += f"\n\n---\n **Category:** {categories[i]}" output += f"\n\n **Count:** {current_footprint[i]}\n\n" output += f"**Best of Breed Solution:** {number_of_backlinks[i]}" data = { "": [str(category) for category in categories], "Current Footprint": [str(footprint) for footprint in current_footprint], "Best of Breed Solution": [str(backlink) for backlink in number_of_backlinks] } df_output = pd.DataFrame(data) ''' with st.expander("AI Analysis", expanded=True, icon="🤖"): st.table(df_output.style.set_table_styles( [{'selector': 'th:first-child, td:first-child', 'props': [('width', '20px')]}, {'selector': 'th, td', 'props': [('width', '150px'), ('text-align', 'center')]}] ).set_properties(**{'text-align': 'center'})) ''' return output def detect_encoding(self, uploaded_file): result = chardet.detect(uploaded_file.read(100000)) uploaded_file.seek(0) # Reset file pointer to the beginning return result['encoding'] def linkedin_content_metrics(self, linkedin_content_metrics): # Avg. engagement rate try: linkedin_engagement_rate = linkedin_content_metrics['Engagement rate (organic)'].mean().round(2) except Exception: new_header = linkedin_content_metrics.iloc[0] #grab the first row for the header linkedin_content_metrics = linkedin_content_metrics[1:] #take the data less the header row linkedin_content_metrics.columns = new_header #set the header row as the df header linkedin_content_metrics['Engagement rate (organic)'] = pd.to_numeric(linkedin_content_metrics['Engagement rate (organic)'], errors='coerce') linkedin_engagement_rate = linkedin_content_metrics['Engagement rate (organic)'].mean().round(2) # Post Frequency st.session_state['linkedin_engagement_rate'] = linkedin_engagement_rate return linkedin_engagement_rate def linkedin_content_post(self, linkedin_content_post): try: linkedin_post_frequency = linkedin_content_post[~linkedin_content_post['Post title'].isna()].shape[0] except Exception: new_header = linkedin_content_post.iloc[0] linkedin_content_post = linkedin_content_post[1:] linkedin_content_post.columns = new_header linkedin_post_frequency = linkedin_content_post[~linkedin_content_post['Post title'].isna()].shape[0] st.write(linkedin_content_post) st.session_state['linkedin_post_frequency'] = linkedin_post_frequency return linkedin_post_frequency def terminate_session(self, session): try: del st.session_state[session] except KeyError: pass def file_upload(self, file_name, file_desc, session): st.write("") # FOR THE HIDE BUTTON file_name = st.file_uploader(f"{file_desc}", type='csv') if file_name: try: self.terminate_session(session) except UnboundLocalError: pass try: encoding = self.detect_encoding(file_name) st.session_state[f'{session}'] = pd.read_csv(file_name, encoding=encoding, low_memory=False) except Exception: pass return file_name def process(self): session = st.session_state.analyze if (self.linkedin_f or (self.linkedin_metrics and self.linkedin_metrics.name) or (self.linkedin_post and self.linkedin_post.name)) and session == "clicked": try: combined_text = "" with st.spinner('Uploading Linkedin Files...', show_time=True): st.write('') # INITIALIZING SESSIONS #combined_text += f"Client Summary: {st.session_state.nature}\n" try: # LINKEDIN try: # LINKEDIN CONTENT POST combined_text += f"\nLinkedin Followers: {self.linkedin_f}" linkedin_content_post = st.session_state['linkedin_content_post'] self.linkedin_content_post(linkedin_content_post) linkedin_post_frequency = st.session_state['linkedin_post_frequency'] combined_text += f"\nLinkedin Post Frequency: {linkedin_post_frequency}" except KeyError: pass try: # LINKEDIN CONTENT METRICS linkedin_content_metrics = st.session_state['linkedin_content_metrics'] self.linkedin_content_metrics(linkedin_content_metrics) linkedin_engagement_rate = st.session_state['linkedin_engagement_rate'] combined_text += f"\nLinkedin Engagement Rate: {linkedin_engagement_rate}%" except KeyError: pass try: # LINKEDIN CONTENT METRICS CSV combined_text += f"\nLinkedin Content Metrics: {linkedin_content_metrics.to_csv(index=True)}" except UnboundLocalError: pass try: # LINKEDIN CONTENT POST CSV combined_text += f"\nLinkedin Content Post: {linkedin_content_post.to_csv(index=True)}" except UnboundLocalError: pass except KeyError: pass # OUTPUT FOR SEO ANALYST payload_txt = {"question": combined_text} #result = self.request_model(payload_txt) #end_time = time.time() #time_lapsed = end_time - start_time debug_info = {'data_field' : 'Linkedin', 'result': combined_text} ''' debug_info = { #'analyst': self.analyst_name, 'url_uuid': self.model_url.split("-")[-1], 'time_lapsed': time_lapsed, 'linkedin_content_metrics': [linkedin_metrics.name] if linkedin_metrics else ['Not available'], 'linkedin_content_post': [linkedin_post.name] if linkedin_post else ['Not available'], 'payload': payload_txt, 'result': result, } ''' collect_telemetry(debug_info) st.session_state['linkedin_upload'] = 'uploaded' #with st.expander("Debug information", icon="⚙"): # st.write(debug_info) st.session_state['analyzing'] = False #for key in st.session_state.keys(): # del st.session_state[session] except AttributeError: st.info("Please upload CSV or PDF files first.") hide_button() def row1(self): self.linkedin_f = st.text_input("Followers:", placeholder='Enter Linkedin Followers') followers = { 'Linkedin Followers': self.linkedin_f if self.linkedin_f else 'N/A' } self.linkedin_metrics = self.file_upload("linkedin_content_metrics", "Content Metrics CSV", "linkedin_content_metrics") self.linkedin_post = self.file_upload("linkedin_content_post", "Content Post CSV", "linkedin_content_post") self.linkedin_metrics self.linkedin_post ''' st.write("") # FOR THE HIDE BUTTON st.write("") # FOR THE HIDE BUTTON st.write("AI Analyst Output: ") st.session_state['analyzing'] = False st.write("") # FOR THE HIDE BUTTON' ''' #analyze_button = st.button("Analyze", disabled=initialize_analyze_session()) self.process() if __name__ == "__main__": st.set_page_config(layout="wide") upload = uploadFile()