diff --git a/cli.py b/cli.py index d759834..401da07 100644 --- a/cli.py +++ b/cli.py @@ -1,10 +1,15 @@ import io import json import logging +from concurrent.futures import ThreadPoolExecutor from enum import Enum +import httpx +import lxml.html import pandas as pd +import tldextract import typer +from sklearn.feature_extraction.text import TfidfVectorizer from apistemic.markets.api import create_markets_api_from_environment from apistemic.markets.models import CompetitorItem @@ -29,6 +34,119 @@ def leadgen(): pass +def fetch_text_nofail(url) -> str | None: + headers = { + "User-Agent": ( + "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 " + "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" + ), + "Accept": ( + "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8" + ), + "Accept-Language": "en-US,en;q=0.5", + "Accept-Encoding": "gzip, deflate", + "DNT": "1", + "Connection": "keep-alive", + "Upgrade-Insecure-Requests": "1", + } + try: + resp = httpx.get(url, headers=headers, follow_redirects=True, timeout=10.0) + resp.raise_for_status() + return resp.text + except Exception as e: + logging.error(f"Error fetching {url}: {e}") + return None + + +def clean_html_text(html: str | None) -> str | None: + if not html: + return None + doc = lxml.html.fromstring(html) + # Remove script and style elements + for elem in doc.xpath(".//script | .//style"): + elem.getparent().remove(elem) + return doc.text_content().strip() + + +@app.command() +def keywords(): + items = _fetch_items("linkedin:startupradar", "competitors") + df = items_to_df(items) + df = df.sample(n=20) + + # Take top 10 + random sample from remaining for proper TF-IDF corpus + df_top = df.head(10) + df_full = df + + df_full["organization.domain"] = ( + df_full["organization.website_url"] + .apply(tldextract.extract) + .apply(lambda x: x.top_domain_under_public_suffix) + ) + + df_full["html"] = df_full["organization.domain"].apply( + lambda domain: fetch_text_nofail(f"https://{domain}") + ) + + with ThreadPoolExecutor(8) as tpe: + texts = list( + tpe.map( + fetch_text_nofail, + df_full["organization.domain"].apply(lambda x: f"https://{x}"), + ) + ) + df_full["text"] = texts + + # Filter out None texts + df_full_valid = df_full[df_full["text"].notna()] + + if len(df_full_valid) == 0: + print("No valid text content found") + return + + # Apply TF-IDF on full corpus for proper IDF calculation + vectorizer = TfidfVectorizer( + max_features=10_000, + ngram_range=(1, 2), + min_df=0.1, + max_df=0.5, + stop_words="english", + ) + + all_texts = df_full_valid["text"].tolist() + tfidf_matrix = vectorizer.fit_transform(all_texts) + feature_names = vectorizer.get_feature_names_out() + + df_tfidf = pd.DataFrame( + data=tfidf_matrix.toarray(), columns=feature_names, index=df_full_valid.index + ) + print(df_tfidf) + + print(df_tfidf.loc[df_top.index].mean(axis=0).sort_values(ascending=False).head(20)) + + exit() + # Get overall top keywords from top 10 competitors only + top_10_matrix = tfidf_matrix[df_top.index] + overall_scores = top_10_matrix.sum(axis=0).A1 + top_indices = overall_scores.argsort()[-20:][::-1] + + print("\n=== Top 20 Keywords from Top Competitors ===") + for idx in top_indices: + print(f"{feature_names[idx]}: {overall_scores[idx]:.3f}") + + # Get top keywords per top 10 companies + print("\n=== Top Keywords per Top Competitor ===") + for df_idx in df_top.index: + if df_idx in df_full_valid.index: + row = df_full_valid.loc[df_idx] + scores = tfidf_matrix[df_full_valid.index.get_loc(df_idx)].toarray()[0] + top_company_indices = scores.argsort()[-5:][::-1] + print(f"\n{row['organization.name']} ({row['organization.domain']}):") + for j in top_company_indices: + if scores[j] > 0: + print(f" - {feature_names[j]}: {scores[j]:.3f}") + + @app.command() def fetch( endpoint: Endpoint = typer.Argument(..., help="Type of data to fetch"), @@ -67,13 +185,7 @@ def _fetch_items( return items -def _display_items(items: list[CompetitorItem], format: OutputFormat): - """Display the data in the requested format.""" - # early return for non-list formats - if format == OutputFormat.json: - typer.echo(json.dumps([item.model_dump() for item in items], indent=2)) - return - +def items_to_df(items: list[CompetitorItem]) -> pd.DataFrame: # all other formats are list-based items_data = [item.model_dump() for item in items] df = pd.json_normalize(items_data) @@ -88,6 +200,17 @@ def _display_items(items: list[CompetitorItem], format: OutputFormat): if col in df.columns: df[col] = df[col].astype("Int64") + return df + + +def _display_items(items: list[CompetitorItem], format: OutputFormat): + """Display the data in the requested format.""" + # early return for non-list formats + if format == OutputFormat.json: + typer.echo(json.dumps([item.model_dump() for item in items], indent=2)) + return + + df = items_to_df(items) if format == OutputFormat.csv: typer.echo(df.to_csv(index=False)) elif format == OutputFormat.parquet: diff --git 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