df_ai_int / classes /Social_Media.py
Ronio Jerico Roque
Add SEO analysis modules and refactor existing code
b438494
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 SocialMediaAnalyst:
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
'''
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 facebook_ad_campaign(self, facebook_ad_campaign):
facebook_ads = facebook_ad_campaign[~facebook_ad_campaign['Ad name'].isna()].shape[0]
st.session_state['facebook_ads'] = facebook_ads
return facebook_ads
def facebook_organic(self, facebook_organic_post):
try:
facebook_engagement_rate = (facebook_organic_post['Reactions, Comments and Shares'].mean() / self.facebooks).round(2)
st.session_state['facebook_engagement_rate'] = facebook_engagement_rate
except TypeError:
pass
# Post Frequency
facebook_post_frequency = facebook_organic_post[~facebook_organic_post['Post ID'].isna()].shape[0]
st.session_state['facebook_post_frequency'] = facebook_post_frequency
st.session_state['facebook_review_rate'] = self.facebook_rr
st.session_state['facebook_followers'] = self.facebooks
try:
return facebook_post_frequency, facebook_engagement_rate
except UnboundLocalError:
return facebook_post_frequency
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 row1(self):
self.facebooks = st.number_input('Facebook Followers:', min_value=1, max_value=99999999, value=None, step=1, placeholder='Enter Facebook Followers')
self.facebook_rr = st.text_input("Facebook Review Rate:", placeholder='Enter Facebook Review Rate')
self.instagram = st.text_input("Instagram Followers:", placeholder='Enter Instagram Followers')
self.instagram_er = st.text_input("Instagram Audience Engagement Rate:", placeholder='Enter Instagram Audience Engagement Rate')
self.instagram_pf = st.text_input("Instagram Post Frequency:", placeholder='Enter Instagram Post Frequency')
self.twitter = st.text_input("Twitter Followers:", placeholder='Enter Twitter Followers')
self.twitter_er = st.text_input("Twitter Audience Engagement Rate:", placeholder='Enter Twitter Audience Engagement Rate')
self.twitter_pf = st.text_input("Twitter Post Frequency:", placeholder='Twitter Post Frequency')
self.youtube = st.text_input("Youtube Followers:", placeholder='Enter Youtube Followers')
self.youtube_er = st.text_input("Youtube Audience Engagement Rate:", placeholder='Enter Youtube Audience Engagement Rate')
self.youtube_pf = st.text_input("Youtube Post Frequency:", placeholder='Youtube Post Frequency')
self.linkedin_f = st.text_input("Linkedin Followers:", placeholder='Enter Linkedin Followers')
self.tiktok_f = st.text_input("Tiktok Followers:", placeholder='Enter Tiktok Followers')
self.tiktok_er = st.text_input("Tiktok Audience Engagement Rate:", placeholder='Enter Tiktok Audience Engagement Rate')
self.tiktok_pf = st.text_input("Tiktok Post Frequency:", placeholder='Enter Tiktok Post Frequency')
followers = {
'Facebook Followers': self.facebooks if self.facebooks else 'N/A',
'Facebook Review Rate': self.facebook_rr if self.facebook_rr else 'N/A',
'Instagram Followers': self.instagram if self.instagram else 'N/A',
'Twitter Followers': self.twitter if self.twitter else 'N/A',
'Youtube Followers': self.youtube if self.youtube else 'N/A',
'Linkedin Followers': self.linkedin_f if self.linkedin_f else 'N/A',
'Tiktok Followers': self.tiktok_f if self.tiktok_f else 'N/A'
}
fb_organic_post = self.file_upload("fb_post", "Upload Facebook Organic Post CSV", "facebook_organic_post")
fb_ads_campaign = self.file_upload("fb_campaign", "Upload Facebook Ads Campaign CSV", "facebook_ad_campaign")
linkedin_metrics = self.file_upload("linkedin_content_metrics", "Upload Linkedin Content Metrics CSV", "linkedin_content_metrics")
linkedin_post = self.file_upload("linkedin_content_post", "Upload Linkedin Content Post CSV", "linkedin_content_post")
tiktok_post = self.file_upload("Tiktok", "Upload Tiktok Content post", "Tiktok")
fb_organic_post
fb_ads_campaign
linkedin_metrics
linkedin_post
tiktok_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())
start_time = time.time()
if st.session_state['analyze'] == 'clicked':
hide_button()
try:
if (fb_organic_post and fb_organic_post.name) or (fb_ads_campaign and fb_ads_campaign.name) or (linkedin_metrics and linkedin_metrics.name) or (linkedin_post and linkedin_post.name) or (tiktok_post and tiktok_post.name):
combined_text = ""
with st.spinner('Analyzing...', show_time=True):
st.write('')
# INITIALIZING SESSIONS
combined_text += f"Client Summary: {st.session_state.nature}\n"
try: # FACEBOOK
try: # ORGANIC POST
combined_text += f"\nFacebook Followers: {self.facebooks}"
facebook_organic_post = st.session_state['facebook_organic_post']
self.facebook_organic(facebook_organic_post)
facebook_post_frequency = st.session_state['facebook_post_frequency']
combined_text += f"\nFacebook Post Frequency: {facebook_post_frequency}"
try: # ENGAGEMENT RATE TRY CATCH
facebook_engagement_rate = st.session_state['facebook_engagement_rate']
combined_text += f"\nFacebook Audience Engagement Rate: {facebook_engagement_rate}%"
except KeyError:
pass
except KeyError:
pass
try: # AD CAMPAIGN
combined_text += f"\nFacebook Review Rate: {self.facebook_rr}"
facebook_ad_campaign = st.session_state['facebook_ad_campaign']
self.facebook_ad_campaign(facebook_ad_campaign)
facebook_ads = st.session_state['facebook_ads']
combined_text += f"\nFacebook Ads: {facebook_ads}"
except KeyError:
pass
try: # FACEBOOK ORGANIC POST CSV
combined_text += f"\nFacebook Organic Post CSV: {facebook_organic_post.to_csv(index=True)}"
except UnboundLocalError:
pass
try: # FACEBOOK ADS CAMPAIGN CSV
combined_text += f"\nFacebook Ads Campaign CSV: {facebook_ad_campaign.to_csv(index=True)}"
except UnboundLocalError:
pass
except KeyError:
pass
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
try:
combined_text += f"\nInstagram Followers: {self.instagram}"
combined_text += f"\nInstagram Audience Engagement Rate: {self.instagram_er}%"
combined_text += f"\nInstagram Post Frequency: {self.instagram_pf}"
except KeyError:
pass
try:
combined_text += f"\nTwitter Followers: {self.twitter}"
combined_text += f"\nTwitter Audience Engagement Rate: {self.twitter_er}%"
combined_text += f"\nTwitter Post Frequency: {self.twitter_pf}"
except KeyError:
pass
try:
combined_text += f"\nYoutube Followers: {self.youtube}"
combined_text += f"\nYoutube Audience Engagement Rate: {self.youtube_er}%"
combined_text += f"\nYoutube Post Frequency: {self.youtube_pf}"
except KeyError:
pass
try:
combined_text += f"\nTiktok Followers: {self.tiktok_f}"
combined_text += f"\nTiktok Audience Engagement Rate: {self.tiktok_er}%"
combined_text += f"\nTiktok Post Frequency: {self.tiktok_pf}"
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 = {
'analyst': self.analyst_name,
'url_uuid': self.model_url.split("-")[-1],
'time_lapsed': time_lapsed,
'facebook_organic_post': [fb_organic_post.name] if fb_organic_post else ['Not available'],
'fb_ads_campaign': [fb_ads_campaign.name] if fb_ads_campaign else ['Not available'],
'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)
with st.expander("Debug information", icon="⚙"):
st.write(debug_info)
st.session_state['analyzing'] = False
else:
st.info("Please upload CSV or PDF files first.")
hide_button()
except AttributeError:
st.info("Please upload CSV or PDF files first.")
hide_button()
if __name__ == "__main__":
st.set_page_config(layout="wide")
upload = uploadFile()