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| # 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 | |