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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('Linkedin...', 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()
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