New Enhanced UI/UX

app.py

@@ -1,163 +1,70 @@
 import streamlit as st
-from streamlit_extras.switch_page_button import switch_page
-def apollo_page_switch():
-    switch_to_apollo = st.button("Visit the page for Apollo extract input")
-    if switch_to_apollo:
-        switch_page("pages_Apollo_Extract")
+import os
+from clients import CompanySpecificClient, UserSpecificClient,hooks,RengagmentEmail
 
-def BH_page_switch():
-    switch_to_BH = st.button("Visit the page dedicated for Business hubs use case")
-    if switch_to_BH:
-        switch_page("pages_Business_Hubs")        
-st.markdown(
-"""
-<style>
-.higher-title {
-    font-size:35px !important;
-    color: #4A90E2;  /* Shade of blue */
-    text-align: center;
-    font-weight: bold;
-    margin-top: -20px;  /* Adjust this value to control the height */
-}
-</style>
-""", 
-unsafe_allow_html=True,
-)
+st.set_page_config(page_title="SalesIntel",layout="wide")
+st.html("styles.html")
+endpoint = os.getenv('blog_lead_endpoint')
+st.html('<h1 class="title"> SalesIntel </h1>')
+st.html('<h4 class="hero-subtitle"> Your AI Sales Companions for Success</h4>')
+email_options = [
+    "[email protected]",
+    "jamesel@omdena.com",
+    "[email protected]",
+    "[email protected]",
+    "[email protected]",
+    "[email protected]",
+    "[email protected]",
+    "[email protected]",
+    "[email protected]",
+]
+email_address = st.selectbox("**Introduce yourself to us**", email_options)
+def main():
+    # First container for Hook2Lead
+    cols = st.columns(2)
 
-st.markdown('<p class="higher-title">SalesIntel AI</p>', unsafe_allow_html=True)
+    with cols[0]:
+        with st.container(border=True):
+            st.html('<h3><span>Re-engagement Campaigns</span></h3>')
+            cols_internal=st.columns(2)
+            with cols_internal[0]:
+                if st.button("Hook2lead", help="The tool will match it with the leads. You can bring your hooks either a blog, AI announcement, or trend"):
+                    hook_dialog(email_address)
+            with cols_internal[1]:
+                if st.button("let the AI hooks", help="The AI will summarize the conversation and action points for you and write an email to explore new use cases. You can bring old conversations with previous leads"):
+                    rengage_lead()                
+    with cols[1]:
+        with st.container(border=True):
+            st.html('<h3><span>Cold Campaigns</span></h3>')   
+            cols = st.columns(2)
+            with cols[0]:
+                if st.button("Tailored for companies", help="Generate cold emails based on company offerings and Omdena's services"):
+                    cold_organization_dialog()
+            with cols[1]:
+                if st.button("Tailored for executives", help="Generate cold emails based on executive achievements"):
+                    cold_executive_dialog()
 
-st.markdown("""
-<h2 style="color:navy;">Business Hubs</h2>
-<h3 style="color:deepskyblue;">Generate Emails - NGO Oriented Function 🌍</h3>
-<h4 style="color:gray;">Generates personalized Emails for NGO campaigns, you have two options to use it</h4>
-<h4 style="color:cornflowerblue;">With Scraping:</h4>
-<h6 style="color:darkolivegreen;">Best for Mass Emails with minimal initial data.</h6>
-<h3 style="color:steelblue;">Required Columns:</h3>
-<ul>
-  <li><strong>👤 First Name</strong></li>
-  <li><strong>🏢 Company Name</strong></li>
-  <li><strong>🌐 Domain</strong></li>
-  <li><strong>📌 Title</strong></li>
-  <li><strong>🔗 Person LinkedIn URL</strong></li>
-  <li><strong>🔗 Company website</strong></li>
-  <li><strong>👤 Email</strong></li>
-</ul>
-<a href="https://docs.google.com/spreadsheets/d/1jkTrPqjO9bNBYlyluIGNsclJ7qz2vOrS2HOCD94ZD2Q/edit?usp=sharing" target="_blank">Check the template from here</a>
-<h4 style="color:cornflowerblue;">Opt-Out of Scraping:</h4>
-<h6 style="color:darkolivegreen;">Best for Low Volume Emails, Perfect for detailed information and targeted approach.</h6>
-<h4 style="color:steelblue;">Required Columns:</h4>
-<ul>
-  <li><strong>👤 First Name</strong></li>
-  <li><strong>🏢 Company Name</strong></li>
-  <li><strong>🌐 Domain</strong></li>
-  <li><strong>📝 User Description</strong></li>
-  <li><strong>👤 Email</strong></li>
-</ul>
-<a href="https://docs.google.com/spreadsheets/d/1TDpsbAZ62ukrUUmYp_5nWfcljWf5JYcZEevP_d4LzIc/edit?usp=sharing" target="_blank">Check the template from here</a>
-<h3 style="color:deepskyblue;">🏢 Generate Emails for Industries/Startups</h3>
-<h4 style="color:gray;">Supports personalized email generation for specific industries or startups, you have two options to use it</h4>
-<h4 style="color:cornflowerblue;">With Scraping:</h4>
-<h6 style="color:darkolivegreen;">For comprehensive company info with limited initial data.</h6>
-<h3 style="color:steelblue;">Required Columns:</h3>
-<ul>
-  <li><strong>🔗 Website</strong></li>
-  <li><strong>🏢 Company Name</strong></li>
-  <li><strong>👤 First Name</strong></li>
-  <li><strong>👤 Email</strong></li>
-</ul>
-<a href="https://docs.google.com/spreadsheets/d/1h2pQQKlTja_G3IJZvw0xNdiyZqkSToTD4zxFvCNDDeQ/edit?usp=sharing" target="_blank">Check the template from here</a>
-<h4 style="color:cornflowerblue;">Opt-Out of Scraping:</h4>
-<h6 style="color:darkolivegreen;">For complete company descriptions at hand.</h6>
-<h4 style="color:steelblue;">Required Columns:</h4>
-<ul>
-  <li><strong>🔗 Website</strong></li>
-  <li><strong>🏢 Company Name</strong></li>
-  <li><strong>👤 First Name</strong></li>
-  <li><strong>📝 Company Description</strong></li>
-  <li><strong>👤 Email</strong></li>
-</ul>
-<a href="https://docs.google.com/spreadsheets/d/1YFuE3ZC31QIGy0YLNK_2v89y0NbektheXXI1QDNA3Ng/edit?usp=sharing" target="_blank">Check the template from here</a>            
-<br>
-""",unsafe_allow_html=True)
-st.markdown("""
-<br>
-<br>
-""", unsafe_allow_html=True)
-BH_page_switch()
+@st.dialog("Bring your hook , and we will match it with the leads", width="large")
+def hook_dialog(email_address):
+    hooks(email_address)
+@st.dialog("personalized cold emails for company offerings", width="large")
+def cold_organization_dialog():
+    CompanySpecificClient(email_address)
 
+@st.dialog("personalized cold emails for executive achievements", width="large")
+def cold_executive_dialog():
+    UserSpecificClient(email_address)    
 
-st.markdown("-----------------------------------------------------")
-
-
-st.markdown("""
-    <h2 style='color: navy;'>Sales Core team - Apollo -</h2>
-    <h3 style='color: darkslategray;'>Data Processing Functions</h3>
-    <h4 style='color: darkcyan;'>Company-Specific Client Function</h4>
-    <p style='color: slategray;'>Upload a CSV with company names and websites.</p>
-    <ul>
-      <li><strong style='color: darkolivegreen;'>With Scraping:</strong>
-        <ul>
-          <li>Required: <code>Website</code>, <code>Company Name for Emails</code></li>
-          <li>The tool scrapes for extra company information.</li>
-        </ul>
-      </li>
-      <li><strong style='color: darkolivegreen;'>Opt Out:</strong>
-        <ul>
-          <li>Required: <code>Website</code>, <code>Company Name for Emails</code>, <code>Company Description</code> (used as <code>scraped_content</code>)</li>
-          <li>Provide your own company descriptions; no scraping.</li>
-        </ul>
-      </li>
-    </ul>
-    <h4 style='color: darkcyan;'>User-Specific Client Function</h4>
-    <p style='color: slategray;'>Upload a CSV with personal and company information.</p>
-    <ul>
-      <li><strong style='color: darkolivegreen;'>With Scraping:</strong>
-        <ul>
-          <li>Required: <code>First Name</code>, <code>Company Name for Emails</code>, <code>Title</code>, <code>Website</code>, <code>Last Name</code>, <code>Person Linkedin Url</code>, <code>Email</code></li>
-          <li>The tool scrapes LinkedIn and other sources for additional information.</li>
-        </ul>
-      </li>
-      <li><strong style='color: darkolivegreen;'>Opt Out:</strong>
-        <ul>
-          <li>Required: <code>First Name</code>, <code>Company Name for Emails</code>, <code>Person Linkedin Url</code>, <code>User Description</code> (used as <code>Scrapped Profile</code>), <code>Email</code></li>
-          <li>Use your data without scraping additional information.</li>
-        </ul>
-      </li>
-    </ul>
-    <h4 style='color: darkcyan;'>Both Features Function</h4>
-    <p style='color: slategray;'>Combine user and company-specific data processing.</p>
-    <ul>
-      <li><strong style='color: darkolivegreen;'>With Scraping:</strong>
-        <ul>
-          <li>Required: <code>First Name</code>, <code>Company Name for Emails</code>, <code>Title</code>, <code>Last Name</code>, <code>Person Linkedin Url</code>, <code>Website</code>, <code>Email</code></li>
-          <li>Scrapes for comprehensive data on both individuals and companies.</li>
-        </ul>
-      </li>
-      <li><strong style='color: darkolivegreen;'>Opt Out:</strong>
-        <ul>
-          <li>Required: <code>First Name</code>, <code>Company Name for Emails</code>, <code>Person Linkedin Url</code>, <code>Company Description</code> (used as <code>scraped_content</code>), <code>User Description</code> (used as <code>Scrapped Profile</code>), <code>Email</code></li>
-          <li>Rely on user-provided descriptions for both individuals and companies.</li>
-        </ul>
-      </li>
-    </ul>
-    <br>
-""", unsafe_allow_html=True)
-
-
-apollo_page_switch()
-
-logo_url = "https://i.imgur.com/WYnv26e.jpeg"  # Replace this with your image's direct URL
-st.markdown(
-    f"""
-    <style>
-    .logo {{
-        position: fixed;
-        bottom: 5px;
-        right: 5px;
-        width: 100px;  # Adjust width as needed
-    }}
-    </style>
-    <img src="{logo_url}" class="logo">
-    """,
-    unsafe_allow_html=True,
-)
+@st.dialog("Tailored emails for re-engaging leads", width="large")
+def rengage_lead():
+    RengagmentEmail(email_address)    
+if __name__ == "__main__":
+    logo_url = "https://i.imgur.com/WYnv26e.jpeg"  # Replace this with your image's direct URL
+    st.markdown(
+        f"""
+        <img src="{logo_url}" class="logo">
+        """,
+        unsafe_allow_html=True,
+    )
+    
+    main()

clients.py

@@ -5,18 +5,20 @@ Created on Mon Jan  1 11:20:18 2024
 @author: mohanadafiffy
 """
 import os 
+from dotenv import load_dotenv
+
+# Load environment variables from a .env file
+load_dotenv()
 import streamlit as st
 import pandas as pd
 import requests
-import os
-host=os.getenv("backend")
-
-CompanyBackendService=host+'/receive_companies/'
-UserBackendService=host+'/receive_users/'
-BothFeaturesService=host+'/receive_data/'
-NGOEmailsService=host+'/receive_ngo_emails/'
-IndustryEmailService=host+'/receive_industry_email/'
-
+cold_host = os.getenv("backend_cold")
+hook_host = os.getenv("hook_host")  # Corrected here
+rengagment_host = os.getenv("rengagement_host")
+CompanyBackendService=cold_host+'/receive_companies/'
+UserBackendService=cold_host+'/receive_users/'
+RengagementBackendService=rengagment_host+'/query/'
+HookBackendService=hook_host+'/query'
             
 def add_https_to_urls(df, column_name):
     """
@@ -222,200 +224,85 @@ def UserSpecificClient(email_receiver):
         else:
             st.error("Data transmission failed. Please verify that your file contains the labels 'Company' and 'Person Linkedin Url'. Additionally, ensure that your file is valid and contains records and try again, if the problem persists please contact us at [email protected]")    
    
-def bothFeaturesFunction(email_receiver):
-    input_data=None    
-    submitted=None 
-    column_selections = {}
-    uploaded_file = st.file_uploader("Kindly upload a CSV file that includes the names and websites of the companies", type=["csv"],key="BothFeaturesUploader")
-    opt_out_scraping = st.checkbox("Opt out of scraping",key="BothOptOut")
-    with st.form(key='User_Form'):
-        if uploaded_file is not None:
-            try:
-                # Detect file type and read accordingly
-                file_type = uploaded_file.name.split('.')[-1]
-                if file_type == 'csv':
-                    try:
-                        df = pd.read_csv(uploaded_file)
-                    except:
-                        df = pd.read_csv(uploaded_file, encoding='ISO-8859-1')
-                # Check if 'Person Linkedin Url' column exists
-                required_essential_columns = ['First Name','Company Name for Emails','Email']
-                missing_essential_columns = [col for col in required_essential_columns if col not in df.columns]
-                required_scraping_columns=['Title','Last Name','Person Linkedin Url','Website']
-                missing_scraping_columns = [col for col in required_scraping_columns if col not in df.columns]
-                for col in missing_essential_columns:
-                    all_columns = df.columns.tolist()
-                    selected_column = st.selectbox(f"Select the column for {col}:", all_columns,key=col)
-                    column_selections[col] = selected_column
-                # Generate selectboxes for missing scraping columns if not opting out
-                if not opt_out_scraping:
-                    for col in missing_scraping_columns:
-                        all_columns = df.columns.tolist()
-                        selected_column = st.selectbox(f"Select the column for {col}:", all_columns, key=col)
-                        column_selections[col] = selected_column
-                # Process the column renaming based on the selections
-                for col, selected_column in column_selections.items():
-                    df.rename(columns={selected_column: col}, inplace=True)
-                    
-                if opt_out_scraping:
-                    if 'Company Description' not in df.columns:
-                        all_columns = df.columns.tolist()
-                        description_column = st.selectbox("Select the column for Company Description:", all_columns,key="bothCompanyDescription")
-                        df.rename(columns={description_column: 'scraped_content'}, inplace=True)
-                    else:
-                        df.rename(columns={'Company Description': 'scraped_content'}, inplace=True)    
-                    if 'User Description' not in df.columns:
-                        all_columns = df.columns.tolist()
-                        description_column = st.selectbox("Select the column for User Description:", all_columns,key="bothuserdescription")
-                        df.rename(columns={description_column: 'Scrapped Profile'}, inplace=True)
-                    else:
-                        df.rename(columns={'User Description': 'Scrapped Profile'}, inplace=True)                    
-                    # Check if "Person Linkedin Url" is in the DataFrame
-                    if 'Person Linkedin Url' not in df.columns:
-                        # Use the DataFrame index to generate a unique value for each row
-                        # You can adjust this to create a more complex identifier
-                        df['Person Linkedin Url'] = 'LI_' + df.index.astype(str)    
-                input_data = df
-    
-            except Exception as E:
-                st.write(E)
-                st.error("An error occurred while processing the file")
-        # If the button is clicked, it will return True for this run
-        prompt_notes= st.text_input("If applicable please mention the network name",key="CompanyPromptNotes")
-        button_clicked = st.form_submit_button("Submit")
+def hooks(email_address):
 
-        # Update session state for the button
-        if button_clicked:
-            submitted = True
+    account_stages = [
+        "Pre-Sales",
+        "Pre-Sales (Unresponsive, After Call)",
+        "Pre-Sales (Long-Term/ Cold)",
+        "Sales Opportunity",
+        "Closed Lost (Opportunity)",
+        "Current Client",
+        "Pre-Sales (Short-Term/ Hot)",
+        "Pre-Sales (Mid-Term/ Warm)",
+        "Project Cancelled"
+    ]
 
-    # Use the session state variable to determine if the button was previously clicked
-    if submitted and input_data is not None:
-        df = input_data
-        df = df.drop_duplicates(subset="Person Linkedin Url", keep='first') 
-        if opt_out_scraping:
-            df=df[['First Name','Person Linkedin Url','Scrapped Profile',"Company Name for Emails","scraped_content","Email"]]
-        else:
-            df=df[['First Name', 'Last Name', 'Title', 'Person Linkedin Url',"Website","Company Name for Emails","Email"]]
-            df=add_https_to_urls(df, 'Website')
-            
-        df = df.dropna().loc[~(df == '').all(axis=1)]  
-            
-        st.write(df)
-        # Convert DataFrame to CSV for transmission
-        csv = df.to_csv(index=False)
+    query_types = ["blog", "announcement", "AI_trend"]
 
-        # Construct the data to send
-        data_to_send = {"prompt_notes": prompt_notes, "dataframe": csv,"email_receiver":email_receiver,"filename": uploaded_file.name}
+    # Get the current number of queries from query params
+    if 'num_queries' not in st.session_state:
+        st.session_state.num_queries = 1
 
-        # Sending the POST request to FastAPI
-        response = requests.post(BothFeaturesService, json=data_to_send)
+    # Creating a form
+    with st.form(key='blog2lead_form'):
+        selected_stages = st.multiselect("**Select Account Stages (optional)**", options=account_stages,key="Account stages multi-select")
 
-        if response.status_code == 200:
-            st.info(f"We're processing your request. You can close the app now. An email will be sent to {email_receiver} once the process is finished.")
-        else:
-            st.error("Data transmission failed. Please verify that your file contains the labels 'Company' and 'Person Linkedin Url'. Additionally, ensure that your file is valid and contains records and try again, if the problem persists please contact us at [email protected]")  
+        queries = {}
+        query_types_selected = []
 
-def BH_Ngo(email_receiver,calendly_link,sender_name):
-    input_data=None    
-    submitted=None 
-    column_selections = {}
-    uploaded_file = st.file_uploader("Kindly upload a CSV file that includes the names and websites of the companies", type=["csv"],key="BothFeaturesUploader")
-    opt_out_scraping = st.checkbox("Opt out of scraping",key="BothOptOut")
-    with st.form(key='User_Form'):
-        if uploaded_file is not None:
-            try:
-                # Detect file type and read accordingly
-                file_type = uploaded_file.name.split('.')[-1]
-                if file_type == 'csv':
-                    try:
-                        df = pd.read_csv(uploaded_file)
-                    except:
-                        df = pd.read_csv(uploaded_file, encoding='ISO-8859-1')
-                # Check if 'Person Linkedin Url' column exists
-                required_essential_columns = ['First Name','Company Name for Emails','Domain','Email']
-                missing_essential_columns = [col for col in required_essential_columns if col not in df.columns]
-                required_scraping_columns=['Title','Person Linkedin Url','Website']
-                missing_scraping_columns = [col for col in required_scraping_columns if col not in df.columns]
-                for col in missing_essential_columns:
-                    all_columns = df.columns.tolist()
-                    selected_column = st.selectbox(f"Select the column for {col}:", all_columns,key=col)
-                    column_selections[col] = selected_column
-                # Generate selectboxes for missing scraping columns if not opting out
-                if not opt_out_scraping:
-                    for col in missing_scraping_columns:
-                        all_columns = df.columns.tolist()
-                        selected_column = st.selectbox(f"Select the column for {col}:", all_columns, key=col)
-                        column_selections[col] = selected_column
-                # Process the column renaming based on the selections
-                for col, selected_column in column_selections.items():
-                    df.rename(columns={selected_column: col}, inplace=True)
-                    
-                if opt_out_scraping: 
-                    if 'User Description' not in df.columns:
-                        all_columns = df.columns.tolist()
-                        User_description_column = st.selectbox("Select the column for User Description:", all_columns,key="bothuserdescription")
-                        df.rename(columns={User_description_column: 'Scrapped Profile'}, inplace=True)
-                    else:
-                        df.rename(columns={'User Description': 'Scrapped Profile'}, inplace=True)                    
-                    # Check if "Person Linkedin Url" is in the DataFrame
-                    if 'Person Linkedin Url' not in df.columns:
-                        # Use the DataFrame index to generate a unique value for each row
-                        # You can adjust this to create a more complex identifier
-                        df['Person Linkedin Url'] = 'LI_' + df.index.astype(str)    
-                input_data = df
-    
-            except Exception as E:
-                st.write(E)
-                st.error("An error occurred while processing the file")
-        # If the button is clicked, it will return True for this run
-        button_clicked = st.form_submit_button("Submit")
+        # Add query fields based on the current number of queries
+        for i in range(st.session_state.num_queries):
+            cols = st.columns([3, 1])  # Adjust the width ratio here
+            with cols[0]:
+                query_label = ["first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth", "ninth", "tenth"][i]
+                query = st.text_input(f"**Enter your {query_label} hook**", key=f"query_{i}", help="you can enter your hook directly or a url")
+            with cols[1]:
+                query_type = st.selectbox(f"**Select {query_label}  hook type**", query_types, key=f"type_{i}")
+            if query.strip():
+              queries[query] = query_type
 
-        # Update session state for the button
-        if button_clicked:
-            submitted = True
+        # Button to add more query fields
+        add_query = st.form_submit_button(label='Add another query')
+        submit_button = st.form_submit_button(label='Submit')
 
-    # Use the session state variable to determine if the button was previously clicked
-    if submitted and input_data is not None:
-        df = input_data
+    if add_query:
+        st.session_state.num_queries += 1
         
-        df = df.drop_duplicates(subset="Person Linkedin Url", keep='first')    
-        if opt_out_scraping:
-            df=df[['First Name','Person Linkedin Url','Scrapped Profile',"Company Name for Emails","Domain","Email"]]
-            
-        else:
-            
-            columns_to_select = ['First Name', 'Title', 'Person Linkedin Url', "Company Name for Emails", "Domain","Website","Email"]
-            df=add_https_to_urls(df, 'Website')
-            if 'Last Name' in df.columns:
-                columns_to_select.insert(1, 'Last Name')  # Insert 'Last Name' at the correct position
-            
-            df = df[columns_to_select]
-
-            
-        # Convert DataFrame to CSV for transmission
-        df = df.dropna().loc[~(df == '').all(axis=1)]  
-        st.write(df)
-        csv = df.to_csv(index=False)
+    if submit_button:
+        if queries and email_address:
+            # Define your data payload to send
+            queries = {k: v for k, v in queries.items() if k and v}
+            data_to_send = {
+                "queries": queries,
+                "email_receiver": email_address,
+            }
 
-        # Construct the data to send
-        data_to_send = {"dataframe": csv, "email_receiver": email_receiver,"calendly_link":calendly_link,"sender_name":sender_name}
+            # Add the filter to the payload only if selected_stages is not empty
+            if selected_stages:
+                data_to_send["filter"] = {
+                    "Account Stage": {"$in": selected_stages}
+                }
 
-        # Sending the POST request to FastAPI
-        response = requests.post(NGOEmailsService, json=data_to_send)
+            # Sending the POST request to FastAPI
+            response = requests.post(HookBackendService, json=data_to_send)
 
-        if response.status_code == 200:
-            st.info(f"We're processing your request. You can close the app now. An email will be sent to {email_receiver} once the process is finished.")
+            # Handling the response
+            if response.status_code == 200:
+                st.info("Your request has been processed successfully.")
+            else:
+                st.error("Data transmission failed. Please try again later.")
         else:
-            st.error("Data transmission failed. Please verify that your file contains the labels 'Company' and 'Person Linkedin Url'. Additionally, ensure that your file is valid and contains records and try again, if the problem persists please contact us at [email protected]")  
-            
-def BH_industry(email_receiver,calendly_link,sender_name):
-    input_data_companies = None
-    submitted_companies = False 
-    uploaded_file = st.file_uploader("Kindly upload a CSV file that includes the names and websites of the companies", type=["csv"],key="CompanyUploader")
-    opt_out_scraping = st.checkbox("Opt out of scraping",key="CompanyScraper")
-    with st.form(key='Comapny_form'):  
-        if uploaded_file is not None:
+            st.error("Please fill out all fields.")
+
+
+def RengagmentEmail(email_receiver):
+    input_data_emails = None
+    submitted_emails = False 
+    uploaded_file = st.file_uploader("Kindly upload a CSV file that includes the required columns", type=["csv"], key="Re-engagment email file uploader")
     
+    with st.form(key='Email_form'):  
+        if uploaded_file is not None:
             try:
                 # Detect file type and read accordingly
                 file_type = uploaded_file.name.split('.')[-1]
@@ -423,81 +310,45 @@ def BH_industry(email_receiver,calendly_link,sender_name):
                     df = pd.read_csv(uploaded_file)
                 elif file_type == 'xlsx':
                     df = pd.read_excel(uploaded_file)
-                    # Check if 'Website' column exists
-                if 'Website' not in df.columns:
-                    all_columns = df.columns.tolist()
-                    website_column  = st.selectbox("Select the column for Website:", all_columns,key="CompanyWebsite")
-                else:
-                    website_column  = 'Website'
-                if 'First Name' not in df.columns:
-                    all_columns = df.columns.tolist()
-                    name_column  = st.selectbox("Select the column for first name:", all_columns,key="firstname")
-                else:
-                    name_column  = 'First Name'                    
-                    # Check if 'Company Name for Emails' column exists
-                if 'Company Name for Emails' not in df.columns:
-                    all_columns = df.columns.tolist()
-                    company_column= st.selectbox("Select the column for Company Name :", all_columns,key="CompanyName")
-                else:
-                    company_column = 'Company Name for Emails'
-                    
-                if 'Email' not in df.columns:
-                    all_columns = df.columns.tolist()
-                    Email_column= st.selectbox("Select the column for email:", all_columns,key="Companyemail")
+                
+                # Check if required columns exist
+                required_columns = ['To Email', 'Subject', 'Body HTML', 'Reply Message', 'To Company', 'website']
+                missing_columns = [col for col in required_columns if col not in df.columns]
+                
+                if missing_columns:
+                    st.error(f"Missing columns: {', '.join(missing_columns)}")
                 else:
-                    Email_column = 'Email'                    
-                if opt_out_scraping:
-                    if 'Company Description' not in df.columns:
-                        all_columns = df.columns.tolist()
-                        description_column = st.selectbox("Select the column for Description:", all_columns,key="CompanyDescription")
-                        df.rename(columns={description_column: 'scraped_content'}, inplace=True)
-                    else:
-                        df.rename(columns={'Company Description': 'scraped_content'}, inplace=True)     
-                    
-                input_data_companies = df  
+                    input_data_emails = df
                 
-            except Exception as E :
-                st.error("An error occured while processing the file")
-       
-            # Fetch the filtered data
-            
+            except Exception as E:
+                st.error("An error occurred while processing the file")
         
         # If the button is clicked, it will return True for this run
         button_clicked = st.form_submit_button("Submit for processing")
         
-        # 2. Update session state for the button
+        # Update session state for the button
         if button_clicked:
-            submitted_companies = True
-# 3. Use the session state variable to determine if the button was previously clicked
-    if submitted_companies and input_data_companies is not None:
-        df = input_data_companies
-        if not opt_out_scraping:
-            df[website_column] = df[website_column].astype(str)
-            df=df[[website_column,company_column,name_column,Email_column]]
-            df.columns = ["Website","Company Name for Emails","First Name","Email"]
-            df = df.drop_duplicates(subset="Email", keep='first')
-            df = df.dropna().loc[~(df == '').all(axis=1)]
-        else:
-            df[website_column] = df[website_column].astype(str)
-            df=df[[website_column,company_column,"scraped_content",name_column,Email_column]]
-            df.columns = ["Website","Company Name for Emails","scraped_content","First Name","Email"]
-            df = df.drop_duplicates(subset="Email", keep='first')
-            df = df.dropna().loc[~(df == '').all(axis=1)]
-            
-        df = df.dropna().loc[~(df == '').all(axis=1)] 
-        df=add_https_to_urls(df, 'Website')
-        st.write(df)    
+            submitted_emails = True
+    
+    # Use the session state variable to determine if the button was previously clicked
+    if submitted_emails and input_data_emails is not None:
+        df = input_data_emails
+        df = df.dropna().loc[~(df == '').all(axis=1)]
+        
+        st.write(df)
+        
         # Convert DataFrame to CSV for transmission
         csv = df.to_csv(index=False)
-    
+        
         # Construct the data to send
-        data_to_send = {"dataframe": csv, "email_receiver": email_receiver,"calendly_link":calendly_link,"sender_name":sender_name}
-    
+        data_to_send = {"dataframe": csv, "email_receiver": email_receiver, "filename": uploaded_file.name}
+        
         # Sending the POST request to FastAPI
-        response = requests.post(IndustryEmailService, json=data_to_send)
-    
+        response = requests.post(RengagementBackendService, json=data_to_send)
+        
         if response.status_code == 200:
             st.info(f"We're processing your request. You can close the app now. An email will be sent to {email_receiver} once the process is finished.")
         else:
-            st.error("Data transmission failed. Please verify that your file contains the labels 'Company Website' and 'Company Name'. Additionally, ensure that your file is valid and contains records and try again , if the problem persists please contact us at mohanad@omdena.com") 
-    return None  
+            st.error("Data transmission failed. Please verify that your file contains the required columns and try again. If the problem persists, please contact us.")
+    
+    return None

styles.html

@@ -1,68 +1,160 @@
 <style>
-    /* General form styling */
-    .stForm {
-        background-color: #f9f9f9;
-        padding: 20px;
-        border-radius: 10px;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+    @font-face {
+        font-family: PermanentMarker;
+        src: url(PermanentMarker-Regular.ttf);
     }
-    
-    /* Label styling */
-    .stForm label {
-        font-weight: bold;
-        color: #333;
+
+    @font-face {
+        font-family: "Open Sans";
+        src: url(app/static/OpenSans-Regular.ttf);
     }
 
+    .title {
+        font-family: "Montserrat", sans-serif;
+        color: #000000;
+        font-size: 28px;
+        margin: 0px 0px 8px 0px;
+        padding: 0px;
+    }
 
+    h1 {
+        font-family: "Montserrat", sans-serif;
+        color: #000000;
+        font-size: 28px;
+        margin: 0px 0px 8px 0px;
+        padding: 30px 0px 0px 0px;
+    }
+    h3 {
+        font-family: "Open Sans", sans-serif;
+        font-size: 1.1em;
+        font-weight: 700;
+        color: #174C4F;
+    }
+    .hero-subtitle {
+        font-family: "Montserrat", sans-serif;
+        color: #FF6666; 
+        font-size: 16px;
+        margin: 0px 0px 8px 50px; /* Adds 20px margin to the left */
+        padding-right: 100px;
+        font-style: italic;  /* Adjust padding as needed */
+    }
+    div[data-testid="stVerticalBlockBorderWrapper"]:has(.stHtml > .watchlist_card) {
+        padding-bottom: 0em;
+    }
 
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .watchlist_symbol_name) {
+        & p {
+            color: #174C4F;
+            font-family: "Open Sans", sans-serif;
+            font-size: 1em;
+            font-weight: 700;
+            margin-bottom: 0;
+        }
+    }
 
-    /* Button styling */
-    .stFormSubmitButton button {
-        background-color: #4CAF50;
-        color: white;
-        padding: 10px 20px;
-        border: none;
-        border-radius: 5px;
-        cursor: pointer;
-        font-size: 16px;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .watchlist_ticker) {
+        text-align: right;
+
+        & p {
+            font-size: 0.8em;
+            margin-bottom: 0;
+        }
+    }
+
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .watchlist_price_label) {
+        & p {
+            font-size: 0.8em;
+            margin-bottom: 0;
+        }
     }
 
-    .stFormSubmitButton button:hover {
-        background-color: #45a049;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .watchlist_price_value) {
+        & p {
+            color: #174C4F;
+            font-family: "Open Sans", sans-serif;
+            font-size: 1.2em;
+            margin-bottom: 0;
+        }
     }
 
-    /* Column styling */
-    .st-emotion-cache-ml2xh6, .st-emotion-cache-12w0qpk {
-        padding: 0 10px;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .column_plotly) {
+        & .stPlotlyChart {
+            margin-top: 1em;
+            padding: 1em;
+            border-radius: 16px;
+            box-shadow: 0px 0px 10px rgba(81, 85, 195, 0.2);
+        }
     }
 
-    /* Header styling */
-    .stTitle {
-        font-size: 2.5em;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .column_indicator) {
+        margin-top: 2.5em;
+        padding-left: 4em;
+    }
+    
+    /* Adapted from https://startbootstrap.com/theme/sb-admin-2 */
+    div[data-testid="stMetric"] {
+        background-color: #FFFFFF;
+        border: 1px solid #CCCCCC;
+        padding: 1em 2em;
+        border-radius: 5px;
+        box-shadow: 0 0.15rem 1.75rem 0 rgba(58, 59, 69, 0.15);
+    }
+
+    label[data-testid="stMetricLabel"] p {
+        font-size: 1em;
+    }
+
+    div[data-testid="stMetricValue"] {
+        font-size: 1.3em;
         font-weight: 700;
-        color: #4CAF50;
-        text-align: left;
-        margin-bottom: 10px;
-        text-decoration: underline;
-        text-decoration-color: #4CAF50;
-        transition: color 0.3s ease;
+        color: #174C4F;
     }
 
-    .stTitle:hover {
-        color: #388E3C;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .low_indicator) {
+        & div[data-testid="stMetric"] {
+            border-left: 0.5rem solid #FF6666;
+        }
     }
 
-    /* Subheader styling */
-    .stSubheader {
-        font-size: 1.5em;
-        font-weight: 400;
-        color: #555;
-        text-align: left;
-        margin-bottom: 20px;
-        transition: color 0.3s ease;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .high_indicator) {
+        & div[data-testid="stMetric"] {
+            border-left: 0.5rem solid rgb(15, 56, 109);
+        }
     }
 
-    .stSubheader:hover {
-        color: #333;
+    div[data-testid="stVerticalBlock"]:has(> div > .stHtml > .bottom_indicator) {
+        margin-top: 0.5em;
     }
+
+    /* Custom button styling */
+    .stButton button, .stFormSubmitButton button {
+    background: linear-gradient(90deg, #3A8DFF 0%, #7B61FF 100%); /* Primary color */
+    color: white;
+    padding: 12px 24px;
+    border: none;
+    border-radius: 8px;
+    cursor: pointer;
+    font-size: 18px;
+    font-family: 'PermanentMarker', sans-serif; /* Change font */
+    font-weight: bold; /* Make font bold */
+    letter-spacing: 1px; /* Add letter spacing */
+    transition: background-color 0.3s ease, box-shadow 0.3s ease;
+}
+
+    .stButton button:hover {
+        background-color: #093031; /* Darker color on hover */
+        box-shadow: 0 6px 12px rgba(0, 0, 0, 0.2); /* Add shadow on hover */
+    }
+
+    .stButton button:active {
+        background: linear-gradient(90deg, #3A8DFF 0%, #7B61FF 100%); /* Primary color */
+        box-shadow: 0 3px 6px rgba(0, 0, 0, 0.2); /* Reduce shadow on click */
+    }
+    .logo {
+        position: fixed;
+        bottom: 10px;
+        right: 5px;
+        width: 100px;  /* Adjust width as needed */
+    }
+
 </style>