Create enrich.py

enrich.py

@@ -0,0 +1,32 @@
+# enrich.py
+import pandas as pd
+import random
+
+# Simulate enrichment for now
+def enrich_company_info(name, website):
+    return {
+        "LinkedIn Employees": random.randint(10, 1000),
+        "Tech Stack": random.choice(["React, Node.js", "PHP, MySQL", "Django, PostgreSQL"]),
+        "Funding (USD)": random.choice([None, 500000, 2000000, 10000000]),
+        "Glassdoor Rating": round(random.uniform(2.5, 4.9), 1),
+        "SSL Secure": website.startswith("https")
+    }
+
+def score_lead(row):
+    score = 0
+    if row["LinkedIn Employees"] > 100: score += 20
+    if row["Funding (USD)"] and row["Funding (USD)"] > 1000000: score += 30
+    if row["Glassdoor Rating"] > 4: score += 20
+    if "React" in row["Tech Stack"] or "Django" in row["Tech Stack"]: score += 20
+    if row["SSL Secure"]: score += 10
+    return score
+
+def enrich_and_score(df):
+    enriched_data = []
+    for _, row in df.iterrows():
+        company_info = enrich_company_info(row['Company Name'], row['Website'])
+        full_data = {**row, **company_info}
+        enriched_data.append(full_data)
+    enriched_df = pd.DataFrame(enriched_data)
+    enriched_df["Lead Score (0-100)"] = enriched_df.apply(score_lead, axis=1)
+    return enriched_df