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 test: def __init__(self, model_url, analyst_name, data_src, analyst_description): 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) # 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(self, facebook_organic_post, facebook_ad_campaign): 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] # Ads facebook_ads = facebook_ad_campaign[~facebook_ad_campaign['Ad name'].isna()].shape[0] st.session_state['facebook_review_rate'] = self.facebook_rr st.session_state['facebook_ads'] = facebook_ads st.session_state['facebook_post_frequency'] = facebook_post_frequency st.session_state['facebook_followers'] = self.facebooks try: return facebook_engagement_rate, facebook_ads, facebook_post_frequency except UnboundLocalError: return facebook_ads, facebook_post_frequency def terminate_session(self, session): try: del st.session_state[session] except KeyError: pass def row1(self): col1, col2 = st.columns(gap="medium", spec=[0.33, 0.66]) with col1: intensity = st.select_slider( "Vague - Precise", options=[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], ) 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') followers = { 'Facebook Followers': self.facebooks if self.facebooks else 'N/A', 'Facebook Review Rate': self.facebook_rr if self.facebook_rr else 'N/A', } with col2: 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 analyze_button: hide_button() try: if self.facebooks: combined_text = "" with st.spinner('Analyzing...', show_time=True): st.write('') intensity = str(intensity) combined_text += f"Intensity: {intensity}\n" # INITIALIZING SESSIONS try: facebook_organic_post = st.session_state['facebook_organic_post'] facebook_ad_campaign = st.session_state['facebook_ad_campaign'] self.facebook(facebook_organic_post, facebook_ad_campaign) try: facebook_engagement_rate = st.session_state['facebook_engagement_rate'] combined_text += f"\nFacebook Audience Engagement Rate: {facebook_engagement_rate}%" except KeyError: pass facebook_ads = st.session_state['facebook_ads'] facebook_post_frequency = st.session_state['facebook_post_frequency'] combined_text += f"\nFacebook Followers: {self.facebooks}" combined_text += f"\nFacebook Review Rate: {self.facebook_rr}" combined_text += f"\nFacebook Ads: {facebook_ads}" combined_text += f"\nFacebook Post Frequency: {facebook_post_frequency}" combined_text += facebook_organic_post.to_csv(index=True) combined_text += facebook_ad_campaign.to_csv(index=True) except KeyError: pass try: combined_text += f"Instagram Followers: {self.instagram}\n" combined_text += f"Instagram Audience Engagement Rate: {self.instagram_er}%\n" combined_text += f"Instagram Post Frequency: {self.instagram_pf}\n" 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, #'backlink_files': [*st.session_state['uploaded_files']], 'payload': payload_txt, 'result': result, } collect_telemetry(debug_info) with st.expander("Debug information", icon="⚙"): st.write(debug_info) for df in st.session_state.keys(): del st.session_state[df] for facebook_ad_campaign in st.session_state.keys(): del st.session_state[facebook_ad_campaign] 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()