feat: Enhance compatibility scoring and report generation with new methods and models
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@@ -6,6 +6,7 @@ The scoring system evaluates multiple dimensions to determine how well a project
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matches with an investor's investment criteria.
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"""
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from difflib import SequenceMatcher
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from typing import List, Optional, Tuple
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from db.models import FundTable, InvestorTable, ProjectTable
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@@ -99,12 +100,16 @@ def _calculate_project_fund_compatibility(
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else str(project.stage)
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)
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if project_stage_name in fund_stage_names:
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# Normalize both for case-insensitive comparison
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project_stage_normalized = project_stage_name.upper().strip()
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fund_stages_normalized = {name.upper().strip() for name in fund_stage_names}
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if project_stage_normalized in fund_stages_normalized:
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stage_score = 30
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else:
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# Partial credit for adjacent stages
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stage_score = _calculate_stage_proximity(
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project_stage_name, fund_stage_names
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project_stage_normalized, fund_stages_normalized
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)
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total_score += stage_score
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@@ -112,22 +117,53 @@ def _calculate_project_fund_compatibility(
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# 2. Sector Overlap (30 points)
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sector_score = 0
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if project.sector and fund.sectors:
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project_sector_ids = {sector.id for sector in project.sector}
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fund_sector_ids = {sector.id for sector in fund.sectors}
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if project_sector_ids and fund_sector_ids:
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common_sectors = project_sector_ids.intersection(fund_sector_ids)
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# Score based on what percentage of project sectors are covered by fund
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overlap_ratio = len(common_sectors) / len(project_sector_ids)
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sector_score = int(30 * overlap_ratio)
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project_sectors = [s for s in project.sector if hasattr(s, 'name')]
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fund_sectors = [s for s in fund.sectors if hasattr(s, 'name')]
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if project_sectors and fund_sectors:
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# Use fuzzy matching to account for similar but not identical sector names
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match_count = 0
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total_matches = 0
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for proj_sector in project_sectors:
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best_match_score = 0
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proj_name = proj_sector.name.lower().strip()
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for fund_sector in fund_sectors:
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fund_name = fund_sector.name.lower().strip()
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# Exact match
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if proj_name == fund_name:
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best_match_score = 1.0
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break
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# Fuzzy match using sequence matcher
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similarity = SequenceMatcher(None, proj_name, fund_name).ratio()
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# Also check if one contains the other (substring match)
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if proj_name in fund_name or fund_name in proj_name:
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similarity = max(similarity, 0.8)
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best_match_score = max(best_match_score, similarity)
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# Count matches with threshold
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# Perfect match (1.0), strong match (>0.75), partial match (>0.6)
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if best_match_score >= 0.6:
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total_matches += best_match_score
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match_count += 1
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if match_count > 0:
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# Calculate overlap ratio based on fuzzy matches
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overlap_ratio = total_matches / len(project_sectors)
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sector_score = int(30 * overlap_ratio)
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total_score += sector_score
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# 3. Geographic Match (20 points)
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geo_score = 0
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if project.location and fund.geographic_focus:
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project_location_lower = project.location.lower()
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fund_geo_lower = (fund.geographic_focus or "").lower()
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project_location_lower = project.location.lower().strip()
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fund_geo_lower = (fund.geographic_focus or "").lower().strip()
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# Exact match
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if project_location_lower == fund_geo_lower:
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@@ -137,10 +173,10 @@ def _calculate_project_fund_compatibility(
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project_location_lower in fund_geo_lower
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or fund_geo_lower in project_location_lower
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):
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geo_score = 10
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# Check for common geographic terms
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geo_score = 15
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# Check for common geographic terms or regional overlap
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elif _check_geographic_overlap(project_location_lower, fund_geo_lower):
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geo_score = 5
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geo_score = 12
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total_score += geo_score
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@@ -209,13 +245,44 @@ def _calculate_project_investor_direct_compatibility(
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# 2. Sector Overlap (30 points)
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sector_score = 0
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if project.sector and investor.sectors:
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project_sector_ids = {sector.id for sector in project.sector}
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investor_sector_ids = {sector.id for sector in investor.sectors}
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if project_sector_ids and investor_sector_ids:
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common_sectors = project_sector_ids.intersection(investor_sector_ids)
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overlap_ratio = len(common_sectors) / len(project_sector_ids)
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sector_score = int(30 * overlap_ratio)
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project_sectors = [s for s in project.sector if hasattr(s, 'name')]
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investor_sectors = [s for s in investor.sectors if hasattr(s, 'name')]
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if project_sectors and investor_sectors:
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# Use fuzzy matching to account for similar but not identical sector names
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match_count = 0
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total_matches = 0
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for proj_sector in project_sectors:
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best_match_score = 0
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proj_name = proj_sector.name.lower().strip()
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for inv_sector in investor_sectors:
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inv_name = inv_sector.name.lower().strip()
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# Exact match
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if proj_name == inv_name:
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best_match_score = 1.0
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break
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# Fuzzy match using sequence matcher
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similarity = SequenceMatcher(None, proj_name, inv_name).ratio()
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# Also check if one contains the other (substring match)
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if proj_name in inv_name or inv_name in proj_name:
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similarity = max(similarity, 0.8)
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best_match_score = max(best_match_score, similarity)
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# Count matches with threshold
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if best_match_score >= 0.6:
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total_matches += best_match_score
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match_count += 1
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if match_count > 0:
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# Calculate overlap ratio based on fuzzy matches
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overlap_ratio = total_matches / len(project_sectors)
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sector_score = int(30 * overlap_ratio)
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total_score += sector_score
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@@ -278,8 +345,11 @@ def _calculate_stage_proximity(project_stage: str, fund_stages: set) -> int:
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"""
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stage_order = ["SEED", "SERIES_A", "SERIES_B", "SERIES_C", "GROWTH", "LATE_STAGE"]
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# Normalize project stage for comparison
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project_stage_normalized = project_stage.upper().strip()
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try:
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project_idx = stage_order.index(project_stage)
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project_idx = stage_order.index(project_stage_normalized)
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except ValueError:
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return 0
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@@ -290,8 +360,10 @@ def _calculate_stage_proximity(project_stage: str, fund_stages: set) -> int:
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if project_idx < len(stage_order) - 1:
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adjacent_stages.append(stage_order[project_idx + 1])
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# Normalize fund stages and check for matches
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for stage in fund_stages:
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if stage in adjacent_stages:
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stage_normalized = stage.upper().strip()
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if stage_normalized in adjacent_stages:
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return 15 # Half credit for adjacent stage
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return 0
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@@ -305,24 +377,62 @@ def _check_geographic_overlap(location1: str, location2: str) -> bool:
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- "San Francisco, CA" and "California" -> True
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- "New York" and "USA" -> True (if both contain USA/US)
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- "London, UK" and "United Kingdom" -> True
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- "Germany" and "Europe" -> True
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"""
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# Common geographic groupings
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# Normalize inputs
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loc1 = location1.lower().strip()
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loc2 = location2.lower().strip()
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# Common geographic groupings with broader regional mappings
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geo_groups = [
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["usa", "us", "united states", "america"],
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["uk", "united kingdom", "britain"],
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["california", "ca"],
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["new york", "ny"],
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# North America
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["usa", "us", "united states", "america", "u.s.", "u.s.a"],
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["canada", "canadian"],
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["mexico", "mexican"],
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# Europe and countries
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["europe", "european", "eu", "germany", "france", "uk", "united kingdom",
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"britain", "spain", "italy", "netherlands", "belgium", "sweden", "denmark",
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"norway", "finland", "poland", "portugal", "austria", "switzerland",
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"ireland", "greece", "czech", "romania"],
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# UK specific
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["uk", "united kingdom", "britain", "england", "scotland", "wales", "london"],
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# US states
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["california", "ca", "san francisco", "los angeles", "silicon valley"],
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["new york", "ny", "nyc"],
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["texas", "tx"],
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["europe", "eu"],
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["asia", "asian"],
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["africa", "african"],
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["massachusetts", "ma", "boston"],
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["washington", "seattle"],
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# Asia
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["asia", "asian", "china", "japan", "korea", "singapore", "hong kong",
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"india", "indonesia", "thailand", "vietnam", "malaysia", "philippines"],
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# Middle East
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["middle east", "israel", "uae", "dubai", "saudi arabia"],
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# Latin America
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["latin america", "brazil", "argentina", "chile", "colombia", "mexico"],
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# Africa
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["africa", "african", "south africa", "nigeria", "kenya", "egypt"],
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# Oceania
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["australia", "australian", "new zealand"],
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]
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# Check if both locations match any group
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for group in geo_groups:
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found_in_1 = any(term in location1 for term in group)
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found_in_2 = any(term in location2 for term in group)
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found_in_1 = any(term in loc1 for term in group)
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found_in_2 = any(term in loc2 for term in group)
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if found_in_1 and found_in_2:
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return True
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# Check for direct substring match (one contains the other)
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if loc1 in loc2 or loc2 in loc1:
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return True
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return False
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