feat: Update investor report generation and HTML template to include fund details and improve data handling
This commit is contained in:
bolade
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+97
-25
@@ -145,16 +145,74 @@ Return the lower and upper bounds in USD."""
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"""
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Manually parse the JSON profile from the CSV.
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Returns a cleaned dictionary with the investor profile data.
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Handles JSON wrapped in markdown code blocks (```json ... ```).
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Handles trailing quotes and extra data after JSON.
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"""
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if not json_str or pd.isna(json_str):
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return None
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try:
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# Clean the JSON string
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cleaned_json = json_str.strip()
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# Check if it's plain text (no JSON structure)
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if not cleaned_json.startswith(("{", "```", "'")):
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print(" ⚠️ No JSON structure found - skipping")
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return None
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# Remove markdown code block markers if present
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if cleaned_json.startswith("```"):
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# Remove opening marker (```json or ```Json or ```)
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lines = cleaned_json.split("\n")
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if lines[0].startswith("```"):
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lines = lines[1:] # Remove first line
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# Remove closing marker (``` or ```')
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if lines and lines[-1].strip() in ("```", "```'", '```"'):
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lines = lines[:-1] # Remove last line
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cleaned_json = "\n".join(lines).strip()
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# Remove trailing quotes that might be left over
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if cleaned_json.endswith(("'", '"')):
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cleaned_json = cleaned_json[:-1].strip()
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# Try to find JSON boundaries if there's extra data
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# Look for the first { and the last }
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start_idx = cleaned_json.find("{")
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if start_idx == -1:
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print(" ⚠️ No opening brace found - not valid JSON")
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return None
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# Find the matching closing brace
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# We need to count braces to find the actual end
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brace_count = 0
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end_idx = -1
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for i in range(start_idx, len(cleaned_json)):
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if cleaned_json[i] == "{":
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brace_count += 1
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elif cleaned_json[i] == "}":
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brace_count -= 1
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if brace_count == 0:
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end_idx = i + 1
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break
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if end_idx == -1:
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print(" ⚠️ No matching closing brace found")
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return None
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# Extract just the JSON part
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cleaned_json = cleaned_json[start_idx:end_idx]
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# Parse JSON string
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profile = json.loads(json_str)
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profile = json.loads(cleaned_json)
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return profile
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except json.JSONDecodeError as e:
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print(f"Error parsing JSON: {e}")
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print(f" ❌ JSON parsing error: {e}")
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# Print first 200 chars for debugging
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preview = json_str[:200] if len(json_str) > 200 else json_str
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print(f" Preview: {preview}...")
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return None
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except Exception as e:
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print(f" ❌ Unexpected error: {e}")
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return None
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async def process_investor_profile(
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@@ -338,34 +396,45 @@ Return the lower and upper bounds in USD."""
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if existing_company:
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# Update only founded_year on existing company
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company = existing_company
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updated_fields = []
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if company_data.get("founded_year"):
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company.founded_year = company_data["founded_year"]
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updated_fields.append(
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f"founded_year: {company_data['founded_year']}"
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)
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# Add/update company members (key executives)
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# First, remove existing members if updating
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db.query(CompanyMember).filter_by(company_id=company.id).delete()
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exec_count = 0
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for exec_data in company_data.get("key_executives", []):
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member = CompanyMember(
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name=exec_data.get("name"),
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role=exec_data.get("title"),
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linkedin=exec_data.get(
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"source_url"
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), # Store source URL in linkedin field
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company_id=company.id,
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)
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db.add(member)
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exec_count += 1
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if exec_count > 0:
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updated_fields.append(f"{exec_count} executives")
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if updated_fields:
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print(f" 📝 Updated: {', '.join(updated_fields)}")
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return company
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else:
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# Company should already be in base database, but if not found, skip
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print(
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f"⚠️ Company '{company_data['name']}' not found in base database - skipping"
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)
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# Company not found in base database, skip
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print(" ⚠️ Not in database - skipping")
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return None
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# Add/update company members (key executives)
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# First, remove existing members if updating
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db.query(CompanyMember).filter_by(company_id=company.id).delete()
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for exec_data in company_data.get("key_executives", []):
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member = CompanyMember(
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name=exec_data.get("name"),
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role=exec_data.get("title"),
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linkedin=exec_data.get(
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"source_url"
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), # Store source URL in linkedin field
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company_id=company.id,
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)
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db.add(member)
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return company
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except Exception as e:
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print(f"Error saving company to database: {e}")
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print(f" ❌ Error saving: {e}")
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db.rollback()
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return None
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@@ -789,8 +858,11 @@ Return the lower and upper bounds in USD."""
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if pd.notna(row.get("Investor"))
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else None
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)
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# Try both column names for flexibility
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profile_json = (
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row.get("Final Investor Profile", "")
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row.get("Perplexity Gap Output", "")
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if pd.notna(row.get("Perplexity Gap Output"))
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else row.get("Final Investor Profile", "")
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if pd.notna(row.get("Final Investor Profile"))
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else None
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)
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+137
-61
@@ -80,34 +80,70 @@ class ReportGenerator:
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"thesis": 5,
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}
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# Aggregate data from all funds
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all_sectors = set(investor_data.get("sectors", []))
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all_stages = set()
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all_geographies = []
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check_ranges = []
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for fund in investor_data.get("funds", []):
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all_sectors.update(fund.get("sectors", []))
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all_stages.update(fund.get("investment_stages", []))
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if fund.get("geographic_focus"):
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all_geographies.append(fund["geographic_focus"])
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if fund.get("check_size_lower") and fund.get("check_size_upper"):
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check_ranges.append(
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{
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"lower": fund["check_size_lower"],
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"upper": fund["check_size_upper"],
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}
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)
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# Sector match
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investor_sectors = set(investor_data.get("sectors", []))
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project_sectors = set(project_data.get("sectors", []))
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if investor_sectors and project_sectors:
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if investor_sectors & project_sectors:
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if all_sectors and project_sectors:
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if all_sectors & project_sectors:
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score += weights["sector"]
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# Stage match
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investor_stages = set(investor_data.get("investment_stages", []))
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# Stage match - case insensitive comparison
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project_stage = project_data.get("stage")
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if project_stage and project_stage in investor_stages:
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score += weights["stage"]
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if project_stage and all_stages:
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# Normalize stage names for comparison (case-insensitive)
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normalized_stages = {
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stage.lower().replace("_", " ") for stage in all_stages
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}
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project_stage_normalized = project_stage.lower().replace("_", " ")
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if project_stage_normalized in normalized_stages:
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score += weights["stage"]
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# Geography match
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investor_geo = (investor_data.get("geographic_focus") or "").lower()
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# Geography match - check if any fund matches
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project_geo = (project_data.get("location") or "").lower()
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if investor_geo and project_geo and investor_geo in project_geo:
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geo_match = False
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if all_geographies:
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for geo in all_geographies:
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if geo:
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geo_lower = geo.lower()
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# Match if investor geography is "global" or if there's a location overlap
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if "global" in geo_lower or "worldwide" in geo_lower:
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geo_match = True
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break
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if project_geo and (
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geo_lower in project_geo or project_geo in geo_lower
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):
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geo_match = True
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break
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if geo_match:
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score += weights["geography"]
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# Check size match
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# Check size match - check if any fund's range matches
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project_valuation = project_data.get("valuation", 0)
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check_lower = investor_data.get("check_size_lower") or 0
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check_upper = investor_data.get("check_size_upper") or float("inf")
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if (
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check_lower
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and check_upper
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and check_lower <= project_valuation <= check_upper
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):
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check_match = False
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if project_valuation and check_ranges:
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for check_range in check_ranges:
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if check_range["lower"] <= project_valuation <= check_range["upper"]:
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check_match = True
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break
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if check_match:
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score += weights["check_size"]
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# Thesis alignment (simplified)
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@@ -121,86 +157,126 @@ class ReportGenerator:
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"""Generate detailed match criteria table"""
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criteria = []
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# Aggregate data from all funds
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all_sectors = set(investor_data.get("sectors", []))
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all_stages = set()
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all_geographies = []
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check_ranges = []
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for fund in investor_data.get("funds", []):
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all_sectors.update(fund.get("sectors", []))
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all_stages.update(fund.get("investment_stages", []))
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if fund.get("geographic_focus"):
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all_geographies.append(fund["geographic_focus"])
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if fund.get("check_size_lower") and fund.get("check_size_upper"):
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check_ranges.append(
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{
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"lower": fund["check_size_lower"],
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"upper": fund["check_size_upper"],
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"fund_name": fund.get("fund_name", "Unnamed Fund"),
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}
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)
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# Sector criterion
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investor_sectors = investor_data.get("sectors", [])
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project_sectors = project_data.get("sectors", [])
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sector_match = (
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"Perfect" if set(investor_sectors) & set(project_sectors) else "Mismatch"
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)
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sector_match = "Perfect" if all_sectors & set(project_sectors) else "Mismatch"
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criteria.append(
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{
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"name": "Sector",
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"requirement": "Cybersecurity, B2B SaaS" if project_sectors else "N/A",
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"evidence": ", ".join(investor_sectors[:3])
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if investor_sectors
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else "N/A",
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"requirement": ", ".join(project_sectors) if project_sectors else "N/A",
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"evidence": ", ".join(list(all_sectors)[:3]) if all_sectors else "N/A",
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"match": sector_match,
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"weight": "30%",
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}
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)
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# Stage criterion
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investor_stages = investor_data.get("investment_stages", [])
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# Stage criterion - case insensitive comparison
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project_stage = project_data.get("stage", "N/A")
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stage_match = "Perfect" if project_stage in investor_stages else "Mismatch"
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stage_match = "Mismatch"
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if project_stage != "N/A" and all_stages:
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# Normalize stage names for comparison
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normalized_stages = {
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stage.lower().replace("_", " ") for stage in all_stages
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}
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project_stage_normalized = project_stage.lower().replace("_", " ")
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stage_match = (
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"Perfect"
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if project_stage_normalized in normalized_stages
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else "Mismatch"
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)
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elif project_stage == "N/A":
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stage_match = "N/A"
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criteria.append(
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{
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"name": "Stage",
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"requirement": str(project_stage),
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"evidence": ", ".join(investor_stages) if investor_stages else "N/A",
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"evidence": ", ".join(all_stages) if all_stages else "N/A",
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"match": stage_match,
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"weight": "30%",
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}
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)
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# Geography criterion
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investor_geo = investor_data.get("geographic_focus") or "N/A"
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project_geo = project_data.get("location") or "N/A"
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investor_geo_display = ", ".join(all_geographies) if all_geographies else "N/A"
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# Safe comparison handling None values and "Global" matches
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geo_match = "Mismatch"
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if project_geo != "N/A" and all_geographies:
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for geo in all_geographies:
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if geo:
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geo_lower = geo.lower()
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# Match if investor geography is "global" or if there's a location overlap
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if "global" in geo_lower or "worldwide" in geo_lower:
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geo_match = "Perfect"
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break
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if (
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geo_lower in project_geo.lower()
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or project_geo.lower() in geo_lower
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):
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geo_match = "Strong"
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break
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elif not all_geographies and project_geo == "N/A":
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geo_match = "N/A"
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# Safe comparison handling None values
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if investor_geo == "N/A" or project_geo == "N/A":
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geo_match = (
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"N/A" if investor_geo == "N/A" and project_geo == "N/A" else "Mismatch"
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)
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else:
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investor_geo_lower = investor_geo.lower()
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project_geo_lower = project_geo.lower()
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geo_match = (
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"Strong"
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if investor_geo_lower in project_geo_lower
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or project_geo_lower in investor_geo_lower
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else "Mismatch"
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)
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criteria.append(
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{
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"name": "Geography",
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"requirement": project_geo,
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"evidence": investor_geo,
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"evidence": investor_geo_display,
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"match": geo_match,
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"weight": "20%",
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}
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)
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# Check Size criterion
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check_lower = investor_data.get("check_size_lower") or 0
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check_upper = investor_data.get("check_size_upper") or 0
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project_val = project_data.get("valuation", 0)
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# Build evidence string from all fund ranges
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check_evidence = "N/A"
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if check_lower and check_upper:
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check_evidence = (
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f"€{check_lower / 1000000:.0f}M - €{check_upper / 1000000:.0f}M"
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)
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elif check_lower:
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check_evidence = f"€{check_lower / 1000000:.0f}M+"
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if check_ranges:
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evidence_parts = []
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for cr in check_ranges[:3]: # Show up to 3 funds
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range_str = (
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f"€{cr['lower'] / 1000000:.0f}M - €{cr['upper'] / 1000000:.0f}M"
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)
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if cr["fund_name"]:
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evidence_parts.append(f"{cr['fund_name']}: {range_str}")
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else:
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evidence_parts.append(range_str)
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check_evidence = "; ".join(evidence_parts)
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# Check if project valuation matches any fund
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check_match = "N/A"
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if project_val > 0 and check_ranges:
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match_found = any(
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cr["lower"] <= project_val <= cr["upper"] for cr in check_ranges
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)
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check_match = "Perfect" if match_found else "Mismatch"
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elif project_val > 0:
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check_match = "Strong"
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check_match = (
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"Perfect"
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if check_lower and check_upper and check_lower <= project_val <= check_upper
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else "Strong"
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if project_val > 0
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else "N/A"
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)
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criteria.append(
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{
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"name": "Check Size",
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