Update app.py

app.py

@@ -1,46 +1,46 @@
- import pandas as pd
- import numpy as np
- import streamlit as st
- import requests
-
- # ▶️ Use the URL you provided
- SAMPLE_FILE_URL = "https://huggingface.co/spaces/Em4e/seo-b2b-saas-forecasting-tool/resolve/main/sample_gsc_data.csv"
-
- st.set_page_config(page_title="SEO ROI Forecasting Tool for B2B SaaS", layout="wide")
- st.title("📈 SEO ROI Forecasting Tool for B2B SaaS")
-
- st.markdown("""
- This app helps you estimate the **financial upside** of ranking improvements for your SEO keywords,  
- and compare that to what it would cost you in paid ads.  
- <br>
- 
- 👉 **Make sure your CSV has a `CPC` column** (cost per click in $).  
- If you don’t, we’ll simulate one for you.  
- <br>
- 
- Developed by: [Emilija Gjorgjevska](https://www.linkedin.com/in/emilijagjorgjevska/)
- """, unsafe_allow_html=True)
-
-+# ———————
-+# Download button for the sample file
-+sample_bytes = requests.get(SAMPLE_FILE_URL).content
-+st.download_button(
-+    label="📥 Download sample CSV",
-+    data=sample_bytes,
-+    file_name="sample_gsc_data.csv",
-+    mime="text/csv",
-+)
-+# ———————
-
- # === Sidebar inputs ===
- with st.sidebar:
-    st.header("🔧 Assumptions & Inputs")    st.header("🔧 Assumptions & Inputs")
-    uploaded_file    = st.file_uploader("Upload Google Search Console CSV", type="csv")
-    target_position  = st.slider("Target SERP Position",        1.0, 10.0, 4.0, 0.5)
-    conversion_rate  = st.slider("Conversion Rate (% → signup)", 0.1, 10.0, 2.0, 0.1)
-    close_rate       = st.slider("Close Rate (% → customer)",    1.0, 100.0, 20.0, 1.0)
-    mrr_per_customer = st.slider("MRR per Customer ($)",         10,    1000, 200, 10)
-    seo_cost         = st.slider("Total SEO Investment ($)",     1_000, 100_000, 10_000, 1_000)
+import pandas as pd
+import numpy as np
+import streamlit as st
+import requests
+
+# ▶️ Use the URL you provided
+SAMPLE_FILE_URL = "https://huggingface.co/spaces/Em4e/seo-b2b-saas-forecasting-tool/resolve/main/sample_gsc_data.csv"
+
+st.set_page_config(page_title="SEO ROI Forecasting Tool for B2B SaaS", layout="wide")
+st.title("📈 SEO ROI Forecasting Tool for B2B SaaS")
+
+st.markdown("""
+This app helps you estimate the **financial upside** of ranking improvements for your SEO keywords,
+and compare that to what it would cost you in paid ads.
+<br>
+
+👉 **Make sure your CSV has a `CPC` column** (cost per click in $).
+If you don’t, we’ll simulate one for you.
+<br>
+
+Developed by: [Emilija Gjorgjevska](https://www.linkedin.com/in/emilijagjorgjevska/)
+""", unsafe_allow_html=True)
+
+# ———————
+# Download button for the sample file
+sample_bytes = requests.get(SAMPLE_FILE_URL).content
+st.download_button(
+    label="📥 Download sample CSV",
+    data=sample_bytes,
+    file_name="sample_gsc_data.csv",
+    mime="text/csv",
+)
+# ———————
+
+# === Sidebar inputs ===
+with st.sidebar:
+    st.header("🔧 Assumptions & Inputs")
+    uploaded_file = st.file_uploader("Upload Google Search Console CSV", type="csv")
+    target_position = st.slider("Target SERP Position", 1.0, 10.0, 4.0, 0.5)
+    conversion_rate = st.slider("Conversion Rate (% → signup)", 0.1, 10.0, 2.0, 0.1)
+    close_rate = st.slider("Close Rate (% → customer)", 1.0, 100.0, 20.0, 1.0)
+    mrr_per_customer = st.slider("MRR per Customer ($)", 10, 1000, 200, 10)
+    seo_cost = st.slider("Total SEO Investment ($)", 1_000, 100_000, 10_000, 1_000)
 
 # === Load & prep data ===
 def load_csv():
@@ -65,10 +65,10 @@ def calculate_roi(df):
     # map and rename
     cols = {c.lower(): c for c in df.columns}
     need = {
-        'query':       ['query','keyword','keywords','queries'],
+        'query': ['query','keyword','keywords','queries'],
         'impressions': ['impressions'],
-        'position':    ['position','avg. position','average position'],
-        'cpc':         ['cpc']
+        'position': ['position','avg. position','average position'],
+        'cpc': ['cpc']
     }
     found = {}
     for k, opts in need.items():
@@ -93,8 +93,8 @@ def calculate_roi(df):
         return None, pd.DataFrame()
 
     # compute clicks
-    df['Current_CTR']    = df.position.apply(get_ctr)
-    df['Target_CTR']     = get_ctr(target_position)
+    df['Current_CTR'] = df.position.apply(get_ctr)
+    df['Target_CTR'] = get_ctr(target_position)
     df['Current_Clicks'] = df.impressions * df.Current_CTR
     df['Projected_Clicks'] = df.impressions * df.Target_CTR
     df['Incremental_Clicks'] = df.Projected_Clicks - df.Current_Clicks
@@ -104,40 +104,40 @@ def calculate_roi(df):
         return None, pd.DataFrame()
 
     # monetize
-    conv  = conversion_rate / 100
+    conv = conversion_rate / 100
     close = close_rate / 100
-    df['Signups']    = df.Incremental_Clicks * conv
-    df['Customers']  = df.Signups            * close
-    df['MRR']        = df.Customers          * mrr_per_customer
+    df['Signups'] = df.Incremental_Clicks * conv
+    df['Customers'] = df.Signups * close
+    df['MRR'] = df.Customers * mrr_per_customer
 
     # paid-ads cost & savings
-    df['Paid_Cost']      = df.Incremental_Clicks * df.cpc
-    total_paid_cost      = df.Paid_Cost.sum()
-    savings_vs_paid_ads  = total_paid_cost - seo_cost
+    df['Paid_Cost'] = df.Incremental_Clicks * df.cpc
+    total_paid_cost = df.Paid_Cost.sum()
+    savings_vs_paid_ads = total_paid_cost - seo_cost
 
     # totals & ROI
-    tot_clicks    = df.Incremental_Clicks.sum()
-    tot_signups   = df.Signups.sum()
+    tot_clicks = df.Incremental_Clicks.sum()
+    tot_signups = df.Signups.sum()
     tot_customers = df.Customers.sum()
-    tot_mrr       = df.MRR.sum()
-    seo_roi_pct   = float('inf') if seo_cost == 0 else ((tot_mrr - seo_cost) / seo_cost) * 100
+    tot_mrr = df.MRR.sum()
+    seo_roi_pct = float('inf') if seo_cost == 0 else ((tot_mrr - seo_cost) / seo_cost) * 100
 
     summary = {
-        "clicks":       f"{tot_clicks:,.0f}",
-        "signups":      f"{tot_signups:,.1f}",
-        "customers":    f"{tot_customers:,.1f}",
-        "mrr":          f"${tot_mrr:,.2f}",
-        "roi":          f"{seo_roi_pct:,.2f}%",
-        "paid_cost":    f"${total_paid_cost:,.2f}",
-        "savings":      f"${savings_vs_paid_ads:,.2f}"
+        "clicks": f"{tot_clicks:,.0f}",
+        "signups": f"{tot_signups:,.1f}",
+        "customers": f"{tot_customers:,.1f}",
+        "mrr": f"${tot_mrr:,.2f}",
+        "roi": f"{seo_roi_pct:,.2f}%",
+        "paid_cost": f"${total_paid_cost:,.2f}",
+        "savings": f"${savings_vs_paid_ads:,.2f}"
     }
 
     # table
     out = df[['query','MRR','Paid_Cost']].copy()
     out.rename(columns={
-        'query':       'Keyword',
-        'MRR':         'Projected Incremental MRR ($)',
-        'Paid_Cost':   'Equivalent Paid Ads Cost ($)'
+        'query': 'Keyword',
+        'MRR': 'Projected Incremental MRR ($)',
+        'Paid_Cost': 'Equivalent Paid Ads Cost ($)'
     }, inplace=True)
     out['Impact'] = pd.cut(
         out['Projected Incremental MRR ($)'],
@@ -155,13 +155,13 @@ if st.button("Run Forecast"):
         summary, table = calculate_roi(df)
         if summary:
             c1, c2, c3, c4, c5, c6, c7 = st.columns(7)
-            c1.metric("Incremental Clicks",     summary['clicks'])
-            c2.metric("Projected Signups",      summary['signups'])
-            c3.metric("New Customers",          summary['customers'])
-            c4.metric("Incremental MRR",        summary['mrr'])
-            c5.metric("SEO ROI",                summary['roi'])
-            c6.metric("Paid Ads Cost",          summary['paid_cost'])
-            c7.metric("Savings vs Paid Ads",    summary['savings'])
+            c1.metric("Incremental Clicks", summary['clicks'])
+            c2.metric("Projected Signups", summary['signups'])
+            c3.metric("New Customers", summary['customers'])
+            c4.metric("Incremental MRR", summary['mrr'])
+            c5.metric("SEO ROI", summary['roi'])
+            c6.metric("Paid Ads Cost", summary['paid_cost'])
+            c7.metric("Savings vs Paid Ads", summary['savings'])
 
             st.subheader("📊 Opportunity Keywords")
-            st.dataframe(table, use_container_width=True)
+            st.dataframe(table, use_container_width=True)