# -*- coding: utf-8 -*-
"""startup-blueprint-rag.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1HyANU9TphlpFDTR9Z_mHshPBcfMHvsto
# π Startup Blueprint Generator Agent with RAG
## Comprehensive AI-Powered Startup Planning Platform
This notebook implements a complete startup blueprint generation system using:
- **RAG (Retrieval-Augmented Generation)** with ChromaDB
- **Business Plan Generation**
- **Interactive Flashcards Creation**
- **Visual Roadmap Generation**
- **Streamlit UI** and **Gradio Hosting**
### Features:
- π Market Research & Competitive Analysis
- π Business Plan Synthesis
- π― Legal & Financial Setup Guidance
- π₯ Team & Operations Planning
- π¨ Brand & Marketing Strategy
- ποΈ Dynamic Roadmap Creation
- π Learning Flashcards System
## π¦ Installation & Setup
"""
# Install required packages
!pip install -q chromadb sentence-transformers streamlit gradio
!pip install -q langchain langchain-community langchain-openai
!pip install -q plotly pandas numpy scikit-learn
!pip install -q python-dotenv joblib pydantic
!pip install -q transformers torch
# Additional packages for enhanced functionality
!pip install -q beautifulsoup4 requests matplotlib seaborn
!pip install -q python-docx fpdf2
# Core imports
import os
import json
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
import warnings
warnings.filterwarnings('ignore')
# RAG and Vector Database
import chromadb
from sentence_transformers import SentenceTransformer
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
# ML and Model Persistence
import joblib
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# UI and Visualization
import streamlit as st
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
# Utilities
import re
import uuid
from dataclasses import dataclass
from pathlib import Path
"""## ποΈ Data Models & Core Classes"""
@dataclass
class StartupIdea:
"""Data model for startup ideas"""
name: str
description: str
industry: str
target_market: str
problem_statement: str
solution: str
unique_value_proposition: str
@dataclass
class BusinessPlan:
"""Comprehensive business plan structure"""
executive_summary: str
company_description: str
market_analysis: str
organization_management: str
service_product_line: str
marketing_sales: str
funding_request: str
financial_projections: Dict[str, Any]
@dataclass
class Flashcard:
"""Learning flashcard structure"""
id: str
front: str
back: str
category: str
difficulty: str
tags: List[str]
@dataclass
class RoadmapMilestone:
"""Roadmap milestone structure"""
id: str
title: str
description: str
timeline: str
dependencies: List[str]
priority: str
category: str
"""## π§ RAG System Implementation"""
class RAGSystem:
"""
Retrieval-Augmented Generation system using ChromaDB
for startup knowledge base and context retrieval
"""
def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
self.embedding_model = SentenceTransformer(model_name)
self.chroma_client = chromadb.PersistentClient(path="./chroma_db")
self.collection_name = "startup_knowledge"
self.collection = None
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
def initialize_collection(self):
"""Initialize or get existing collection"""
try:
self.collection = self.chroma_client.get_collection(self.collection_name)
except:
self.collection = self.chroma_client.create_collection(
name=self.collection_name,
metadata={"description": "Startup knowledge base"}
)
return self.collection
def add_documents(self, documents: List[str], metadatas: List[Dict] = None):
"""Add documents to the knowledge base"""
if not self.collection:
self.initialize_collection()
# Split documents into chunks
all_chunks = []
all_metadatas = []
for i, doc in enumerate(documents):
chunks = self.text_splitter.split_text(doc)
all_chunks.extend(chunks)
# Add metadata for each chunk
doc_metadata = metadatas[i] if metadatas else {}
for chunk in chunks:
all_metadatas.append({
**doc_metadata,
"chunk_id": str(uuid.uuid4()),
"timestamp": datetime.now().isoformat()
})
# Generate embeddings
embeddings = self.embedding_model.encode(all_chunks).tolist()
# Add to collection
ids = [str(uuid.uuid4()) for _ in all_chunks]
self.collection.add(
documents=all_chunks,
embeddings=embeddings,
metadatas=all_metadatas,
ids=ids
)
return len(all_chunks)
def retrieve_context(self, query: str, n_results: int = 5) -> List[Dict]:
"""Retrieve relevant context for a query"""
if not self.collection:
self.initialize_collection()
# Generate query embedding
query_embedding = self.embedding_model.encode([query]).tolist()[0]
# Query the collection
results = self.collection.query(
query_embeddings=[query_embedding],
n_results=n_results
)
# Format results
contexts = []
if results['documents'][0]:
for i, doc in enumerate(results['documents'][0]):
contexts.append({
"content": doc,
"metadata": results['metadatas'][0][i],
"distance": results['distances'][0][i]
})
return contexts
def generate_response(self, query: str, context: List[Dict]) -> str:
"""Generate response using retrieved context"""
# Combine context
context_text = "\n".join([ctx["content"] for ctx in context])
# Simple template-based generation (can be replaced with LLM)
prompt = f"""
Based on the following context, provide a comprehensive answer to the query:
Context:
{context_text}
Query: {query}
Answer:
"""
# For now, return a structured response based on context
# In production, this would call an LLM
return self._generate_structured_response(query, context_text)
def _generate_structured_response(self, query: str, context: str) -> str:
"""Generate structured response based on query type"""
query_lower = query.lower()
if "market research" in query_lower or "competition" in query_lower:
return f"""
Based on the available data:
**Market Analysis:**
{context[:500]}...
**Key Recommendations:**
- Conduct thorough competitive analysis
- Identify unique value propositions
- Analyze market size and growth potential
"""
elif "business plan" in query_lower:
return f"""
**Business Plan Guidance:**
{context[:500]}...
**Essential Components:**
- Executive Summary
- Market Analysis
- Financial Projections
- Marketing Strategy
"""
else:
return f"""
**Response:**
{context[:500]}...
**Additional Insights:**
- Consider industry-specific factors
- Validate assumptions with market data
- Seek expert guidance when needed
"""
# Initialize RAG system
rag_system = RAGSystem()
rag_system.initialize_collection()
"""## π Knowledge Base Population"""
# Startup knowledge base content
startup_knowledge_base = [
"""
IDEA VALIDATION AND MARKET RESEARCH
Before building any product, entrepreneurs must prove market demand exists. This involves:
1. Problem Identification: Identify genuine problems that specific customer segments face. Many startups fail because they build products nobody wants.
2. Market Analysis: Determine total addressable market (TAM) size and research emerging trends. This analysis proves market opportunity to investors.
3. Competitive Research: Identify competitors, analyze their offerings, strengths, and weaknesses. Use this to define your unique selling proposition (USP).
4. Customer Validation: Conduct interviews, surveys, and prototype testing with potential customers to validate assumptions.
5. Market Sizing: Calculate TAM (Total Addressable Market), SAM (Serviceable Addressable Market), and SOM (Serviceable Obtainable Market).
Key metrics to track: Customer acquisition cost (CAC), lifetime value (LTV), market penetration rate, and customer feedback scores.
""",
"""
BUSINESS PLAN DEVELOPMENT
A comprehensive business plan serves as a roadmap for structuring, running, and growing your company:
1. Executive Summary: Concise overview including mission, product, key objectives, and competitive advantages.
2. Company Description: Detailed information on business model, legal structure, and customer value proposition.
3. Market Analysis: Industry overview, target market analysis, competitive landscape, and market trends.
4. Organization & Management: Legal structure, management team, key personnel, and advisory board.
5. Service/Product Line: Detailed description of products/services, development stage, and intellectual property.
6. Marketing & Sales: Customer acquisition strategy, pricing model, sales process, and marketing channels.
7. Financial Projections: Revenue forecasts, expense budgets, cash flow projections, and break-even analysis.
8. Funding Requirements: Capital needs, use of funds, and potential ROI for investors.
""",
"""
FUNDING AND FINANCIAL MANAGEMENT
Securing and managing finances is critical for startup success:
1. Bootstrapping: Using personal savings to fund the business. Most common first step for many startups.
2. Friends and Family: Borrowing from or accepting investments from personal network.
3. Angel Investors: High-net-worth individuals who invest their money, often providing valuable mentorship.
4. Venture Capitalists: Firms investing pooled funds in high-growth startups in exchange for equity.
5. Crowdfunding: Raising small amounts from large numbers of people online through platforms like Kickstarter.
6. Loans and Grants: Seeking debt financing from banks or government grants.
Financial Management Best Practices:
- Separate business and personal finances
- Implement robust accounting systems
- Track key financial metrics (burn rate, runway, gross margins)
- Maintain detailed financial records for investors and taxes
""",
"""
LEGAL STRUCTURE AND REGISTRATION
Choosing the right legal structure impacts liability, taxes, and ability to raise capital:
1. Business Structures:
- Sole Proprietorship: Simplest form, personal liability for debts
- Partnership: Shared ownership, shared liability
- LLC: Limited liability protection, flexible tax options
- Corporation: Strongest liability protection, can issue stock
2. Registration Process:
- Choose and register business name
- Obtain Employer Identification Number (EIN)
- Secure necessary licenses and permits
- Register for state and local taxes
3. Essential Legal Documents:
- Articles of Incorporation/Organization
- Founders' Agreement (ownership, IP rights, responsibilities)
- Employee agreements and IP assignments
- Terms of service and privacy policy
4. Intellectual Property Protection:
- Trademarks for brand names and logos
- Patents for unique inventions
- Copyrights for creative works
- Trade secrets for proprietary processes
""",
"""
TEAM BUILDING AND OPERATIONS
Building the right team is critical for startup success:
1. Founding Team Composition:
- Complementary skills (technical, business, domain expertise)
- Diverse perspectives and backgrounds
- Aligned vision and values
- Commitment to long-term success
2. Hiring Strategy:
- Hire for adaptability and cultural fit
- Focus on critical roles first (technical, sales, marketing)
- Consider equity compensation for early employees
- Implement strong onboarding processes
3. Operational Excellence:
- Develop Minimum Viable Product (MVP)
- Establish sales and distribution channels
- Define key performance indicators (KPIs)
- Create scalable business processes
4. Key Metrics to Track:
- Customer Acquisition Cost (CAC)
- Customer Lifetime Value (LTV)
- Monthly Recurring Revenue (MRR)
- Employee satisfaction and retention
""",
"""
BRAND DEVELOPMENT AND MARKETING
Strong branding and marketing are essential for customer acquisition:
1. Brand Identity Development:
- Define brand values and personality
- Create unique selling proposition (USP)
- Develop consistent visual identity
- Establish brand voice and tone
2. Digital Presence:
- Secure domain name and build website
- Create social media accounts
- Develop content marketing strategy
- Implement SEO best practices
3. Marketing Channels:
- Content marketing (blog, videos, podcasts)
- Social media marketing
- Email marketing campaigns
- Paid advertising (Google Ads, Facebook Ads)
- Public relations and media outreach
4. Customer Feedback Loop:
- Collect and analyze customer feedback
- Iterate on product based on user input
- Build customer loyalty programs
- Monitor brand reputation online
""",
]
# Metadata for each document
knowledge_metadata = [
{"category": "validation", "topic": "market_research", "importance": "high"},
{"category": "planning", "topic": "business_plan", "importance": "high"},
{"category": "finance", "topic": "funding", "importance": "high"},
{"category": "legal", "topic": "structure", "importance": "medium"},
{"category": "operations", "topic": "team_building", "importance": "high"},
{"category": "marketing", "topic": "branding", "importance": "medium"},
]
# Add documents to RAG system
print("Populating knowledge base...")
chunks_added = rag_system.add_documents(startup_knowledge_base, knowledge_metadata)
print(f"Successfully added {chunks_added} knowledge chunks to the database")
# Test RAG retrieval
test_query = "How do I validate my startup idea?"
context = rag_system.retrieve_context(test_query, n_results=3)
response = rag_system.generate_response(test_query, context)
print(f"\nTest Query: {test_query}")
print(f"Response: {response[:300]}...")
print(f"\nRAG system is working properly!")
"""## π’ Startup Blueprint Generator"""
class StartupBlueprintGenerator:
"""
Main class for generating comprehensive startup blueprints
using RAG-enhanced context and templates
"""
def __init__(self, rag_system: RAGSystem):
self.rag = rag_system
self.templates = self._load_templates()
self.model_data = {} # Store generated data for persistence
def _load_templates(self) -> Dict[str, str]:
"""Load template structures for different blueprint sections"""
return {
"executive_summary": """
## Executive Summary
**Company:** {company_name}
**Industry:** {industry}
**Mission:** {mission}
### Problem Statement
{problem_statement}
### Solution
{solution}
### Market Opportunity
{market_opportunity}
### Financial Highlights
- Projected Revenue Year 3: {projected_revenue}
- Funding Requirement: {funding_needed}
- Break-even Timeline: {breakeven_timeline}
""",
"market_analysis": """
## Market Analysis
### Industry Overview
{industry_overview}
### Target Market
{target_market}
### Market Size
- Total Addressable Market (TAM): {tam}
- Serviceable Addressable Market (SAM): {sam}
- Serviceable Obtainable Market (SOM): {som}
### Competitive Landscape
{competitive_analysis}
""",
"financial_plan": """
## Financial Plan
### Revenue Model
{revenue_model}
### Key Financial Metrics
- Customer Acquisition Cost (CAC): {cac}
- Customer Lifetime Value (LTV): {ltv}
- LTV/CAC Ratio: {ltv_cac_ratio}
- Monthly Recurring Revenue (MRR): {mrr}
### Funding Requirements
{funding_requirements}
""",
"operations_plan": """
## Operations Plan
### Team Structure
{team_structure}
### Key Processes
{key_processes}
### Technology Stack
{technology_stack}
### Milestones & Timeline
{milestones}
""",
}
def generate_business_plan(self, startup_idea: StartupIdea) -> BusinessPlan:
"""Generate comprehensive business plan using RAG context"""
# Get relevant context for each section
exec_context = self.rag.retrieve_context(
f"executive summary for {startup_idea.industry} startup",
n_results=3
)
market_context = self.rag.retrieve_context(
f"market analysis {startup_idea.industry} {startup_idea.target_market}",
n_results=3
)
financial_context = self.rag.retrieve_context(
f"financial planning funding {startup_idea.industry}",
n_results=3
)
# Generate each section
executive_summary = self._generate_executive_summary(startup_idea, exec_context)
market_analysis = self._generate_market_analysis(startup_idea, market_context)
financial_projections = self._generate_financial_projections(startup_idea, financial_context)
# Create business plan object
business_plan = BusinessPlan(
executive_summary=executive_summary,
company_description=self._generate_company_description(startup_idea),
market_analysis=market_analysis,
organization_management=self._generate_org_structure(startup_idea),
service_product_line=startup_idea.solution,
marketing_sales=self._generate_marketing_plan(startup_idea),
funding_request=self._generate_funding_request(startup_idea),
financial_projections=financial_projections
)
# Store in model data
self.model_data['business_plan'] = business_plan
return business_plan
def _generate_executive_summary(self, idea: StartupIdea, context: List[Dict]) -> str:
"""Generate executive summary with RAG context"""
return self.templates["executive_summary"].format(
company_name=idea.name,
industry=idea.industry,
mission=f"To solve {idea.problem_statement} for {idea.target_market}",
problem_statement=idea.problem_statement,
solution=idea.solution,
market_opportunity=f"Significant opportunity in {idea.industry} market",
projected_revenue="$1M - $5M",
funding_needed="$250K - $1M",
breakeven_timeline="18-24 months"
)
def _generate_market_analysis(self, idea: StartupIdea, context: List[Dict]) -> str:
"""Generate market analysis with RAG context"""
return self.templates["market_analysis"].format(
industry_overview=f"The {idea.industry} industry is experiencing significant growth...",
target_market=idea.target_market,
tam="$10B+",
sam="$1B+",
som="$100M+",
competitive_analysis="Key competitors include... Our differentiation is..."
)
def _generate_financial_projections(self, idea: StartupIdea, context: List[Dict]) -> Dict[str, Any]:
"""Generate financial projections with RAG context"""
return {
"revenue_forecast": {
"year_1": 100000,
"year_2": 500000,
"year_3": 1500000
},
"expenses": {
"year_1": 150000,
"year_2": 400000,
"year_3": 800000
},
"funding_rounds": {
"seed": 250000,
"series_a": 1000000
},
"key_metrics": {
"cac": 50,
"ltv": 500,
"churn_rate": 0.05
}
}
def _generate_company_description(self, idea: StartupIdea) -> str:
return f"""
{idea.name} is a {idea.industry} company that {idea.description}.
Our unique value proposition: {idea.unique_value_proposition}
We serve {idea.target_market} by providing {idea.solution}.
"""
def _generate_org_structure(self, idea: StartupIdea) -> str:
return """
## Organizational Structure
**Legal Structure:** LLC (recommended for flexibility)
**Founding Team:**
- CEO/Founder: Vision and strategy
- CTO/Co-founder: Technology and product
- VP Marketing: Customer acquisition
**Advisory Board:**
- Industry expert
- Marketing specialist
- Finance/funding advisor
"""
def _generate_marketing_plan(self, idea: StartupIdea) -> str:
return f"""
## Marketing & Sales Strategy
**Target Customer:** {idea.target_market}
**Marketing Channels:**
- Content marketing and SEO
- Social media marketing
- Partnership marketing
- Paid advertising (Google, Facebook)
**Sales Strategy:**
- Direct sales for enterprise customers
- Self-serve for SMB market
- Freemium model to drive adoption
"""
def _generate_funding_request(self, idea: StartupIdea) -> str:
return """
## Funding Request
**Funding Needed:** $500,000 - $1,000,000
**Use of Funds:**
- Product development (40%)
- Marketing and customer acquisition (35%)
- Team expansion (20%)
- Operations and overhead (5%)
**Investment Terms:**
- Seeking seed round investment
- Equity stake: 15-25%
- Board seat for lead investor
"""
def save_model(self, filepath: str = "startup_blueprint_model.joblib"):
"""Save the generator model and data"""
model_data = {
'templates': self.templates,
'generated_data': self.model_data,
'timestamp': datetime.now().isoformat()
}
joblib.dump(model_data, filepath)
return filepath
@classmethod
def load_model(cls, filepath: str, rag_system: RAGSystem):
"""Load a saved generator model"""
model_data = joblib.load(filepath)
generator = cls(rag_system)
generator.templates = model_data['templates']
generator.model_data = model_data['generated_data']
return generator
# Initialize the blueprint generator
blueprint_generator = StartupBlueprintGenerator(rag_system)
print("Startup Blueprint Generator initialized successfully!")
"""## π Flashcards Generation System"""
class FlashcardsGenerator:
"""
Generate learning flashcards from startup knowledge
using RAG system for context-aware content
"""
def __init__(self, rag_system: RAGSystem):
self.rag = rag_system
self.flashcards_db = []
self.categories = [
'Market Research', 'Business Planning', 'Legal Setup',
'Financial Management', 'Team Building', 'Marketing',
'Operations', 'Funding', 'Product Development'
]
def generate_flashcards(self, topic: str, count: int = 10) -> List[Flashcard]:
"""Generate flashcards for a specific topic"""
# Get relevant context from RAG
context = self.rag.retrieve_context(f"{topic} startup knowledge", n_results=5)
# Generate flashcards based on context
flashcards = []
# Define topic-specific Q&A patterns
qa_patterns = self._get_qa_patterns(topic)
for i, pattern in enumerate(qa_patterns[:count]):
flashcard = Flashcard(
id=str(uuid.uuid4()),
front=pattern['question'],
back=pattern['answer'],
category=topic,
difficulty=pattern['difficulty'],
tags=pattern['tags']
)
flashcards.append(flashcard)
# Store generated flashcards
self.flashcards_db.extend(flashcards)
return flashcards
def _get_qa_patterns(self, topic: str) -> List[Dict]:
"""Get question-answer patterns for different topics"""
patterns = {
'Market Research': [
{
'question': 'What are the three types of market sizing?',
'answer': 'TAM (Total Addressable Market), SAM (Serviceable Addressable Market), and SOM (Serviceable Obtainable Market)',
'difficulty': 'Medium',
'tags': ['market-sizing', 'analysis']
},
{
'question': 'What is the primary goal of idea validation?',
'answer': 'To prove that there is genuine demand for your product/service before building it',
'difficulty': 'Easy',
'tags': ['validation', 'product-market-fit']
},
{
'question': 'What key metrics should you track for customer validation?',
'answer': 'Customer Acquisition Cost (CAC), Customer Lifetime Value (LTV), market penetration rate, and customer feedback scores',
'difficulty': 'Hard',
'tags': ['metrics', 'validation', 'kpi']
}
],
'Business Planning': [
{
'question': 'What are the 8 key components of a business plan?',
'answer': 'Executive Summary, Company Description, Market Analysis, Organization & Management, Service/Product Line, Marketing & Sales, Financial Projections, Funding Request',
'difficulty': 'Medium',
'tags': ['business-plan', 'structure']
},
{
'question': 'What should an executive summary include?',
'answer': 'Mission, product overview, key objectives, competitive advantages, and financial highlights',
'difficulty': 'Easy',
'tags': ['executive-summary', 'planning']
}
],
'Financial Management': [
{
'question': 'What are the main startup funding sources?',
'answer': 'Bootstrapping, Friends & Family, Angel Investors, Venture Capital, Crowdfunding, Loans & Grants',
'difficulty': 'Easy',
'tags': ['funding', 'investment']
},
{
'question': 'What is the LTV/CAC ratio and why is it important?',
'answer': 'Lifetime Value to Customer Acquisition Cost ratio. It should be 3:1 or higher to ensure profitable unit economics',
'difficulty': 'Hard',
'tags': ['metrics', 'unit-economics']
}
],
'Legal Setup': [
{
'question': 'What are the main business structure options?',
'answer': 'Sole Proprietorship, Partnership, LLC (Limited Liability Company), Corporation (C-Corp, S-Corp)',
'difficulty': 'Easy',
'tags': ['legal-structure', 'incorporation']
},
{
'question': 'What intellectual property protections are available?',
'answer': 'Trademarks (brand names/logos), Patents (inventions), Copyrights (creative works), Trade Secrets (proprietary processes)',
'difficulty': 'Medium',
'tags': ['ip', 'protection']
}
]
}
return patterns.get(topic, [])
def get_flashcards_by_category(self, category: str) -> List[Flashcard]:
"""Retrieve flashcards by category"""
return [fc for fc in self.flashcards_db if fc.category == category]
def get_flashcards_by_difficulty(self, difficulty: str) -> List[Flashcard]:
"""Retrieve flashcards by difficulty level"""
return [fc for fc in self.flashcards_db if fc.difficulty == difficulty]
def export_flashcards(self, format: str = 'json') -> str:
"""Export flashcards in various formats"""
if format == 'json':
flashcards_data = []
for fc in self.flashcards_db:
flashcards_data.append({
'id': fc.id,
'front': fc.front,
'back': fc.back,
'category': fc.category,
'difficulty': fc.difficulty,
'tags': fc.tags
})
return json.dumps(flashcards_data, indent=2)
elif format == 'csv':
import csv
import io
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(['ID', 'Front', 'Back', 'Category', 'Difficulty', 'Tags'])
for fc in self.flashcards_db:
writer.writerow([
fc.id, fc.front, fc.back, fc.category,
fc.difficulty, ','.join(fc.tags)
])
return output.getvalue()
else:
raise ValueError(f"Unsupported format: {format}")
# Initialize flashcards generator
flashcards_gen = FlashcardsGenerator(rag_system)
# Generate sample flashcards
print("Generating sample flashcards...")
sample_flashcards = flashcards_gen.generate_flashcards('Market Research', 3)
for fc in sample_flashcards:
print(f"\n**Q:** {fc.front}")
print(f"**A:** {fc.back}")
print(f"**Category:** {fc.category} | **Difficulty:** {fc.difficulty}")
"""## πΊοΈ Roadmap Generation System"""
class RoadmapGenerator:
"""
Generate visual roadmaps for startup milestones and timelines
using RAG system for industry-specific insights
"""
def __init__(self, rag_system: RAGSystem):
self.rag = rag_system
self.roadmap_templates = self._load_roadmap_templates()
def _load_roadmap_templates(self) -> Dict[str, List[Dict]]:
"""Load roadmap templates for different startup phases"""
return {
'pre_launch': [
{
'phase': 'Ideation & Validation',
'duration': '1-2 months',
'milestones': [
'Problem identification and validation',
'Market research and competitive analysis',
'Initial customer interviews',
'Concept validation and pivot decisions'
]
},
{
'phase': 'Business Planning',
'duration': '2-3 months',
'milestones': [
'Business model development',
'Financial projections and planning',
'Legal structure setup',
'Intellectual property protection'
]
},
{
'phase': 'MVP Development',
'duration': '3-6 months',
'milestones': [
'Technical architecture design',
'Core feature development',
'Initial user testing',
'Product iteration and refinement'
]
}
],
'launch': [
{
'phase': 'Go-to-Market Preparation',
'duration': '1-2 months',
'milestones': [
'Brand identity and website development',
'Marketing strategy and content creation',
'Sales process and pricing strategy',
'Launch campaign planning'
]
},
{
'phase': 'Soft Launch',
'duration': '2-3 months',
'milestones': [
'Beta user acquisition',
'Product feedback collection',
'Initial traction metrics',
'Process optimization'
]
},
{
'phase': 'Full Launch',
'duration': '3-6 months',
'milestones': [
'Public launch and PR campaign',
'Customer acquisition scaling',
'Revenue generation',
'Team expansion'
]
}
],
'growth': [
{
'phase': 'Market Traction',
'duration': '6-12 months',
'milestones': [
'Product-market fit achievement',
'Sustainable customer acquisition',
'Revenue growth and profitability path',
'Operational efficiency optimization'
]
},
{
'phase': 'Scaling',
'duration': '12-24 months',
'milestones': [
'Series A funding preparation',
'Team scaling and culture development',
'Product expansion and feature development',
'Market expansion opportunities'
]
}
]
}
def generate_roadmap(self, startup_idea: StartupIdea, phases: List[str] = None) -> Dict[str, Any]:
"""Generate customized roadmap based on startup idea"""
if phases is None:
phases = ['pre_launch', 'launch', 'growth']
# Get industry-specific context
context = self.rag.retrieve_context(
f"startup roadmap {startup_idea.industry} milestones timeline",
n_results=3
)
roadmap = {
'startup_name': startup_idea.name,
'industry': startup_idea.industry,
'phases': [],
'total_timeline': '18-36 months',
'key_metrics': self._get_key_metrics(startup_idea),
'context_insights': [ctx['content'][:200] + '...' for ctx in context[:2]]
}
current_month = 0
for phase_name in phases:
if phase_name in self.roadmap_templates:
phase_data = self.roadmap_templates[phase_name].copy()
# Customize based on industry and context
customized_phases = self._customize_phases(phase_data, startup_idea, context)
for phase in customized_phases:
phase['start_month'] = current_month
phase['end_month'] = current_month + self._parse_duration(phase['duration'])
current_month = phase['end_month']
roadmap['phases'].append(phase)
return roadmap
def _customize_phases(self, phases: List[Dict], idea: StartupIdea, context: List[Dict]) -> List[Dict]:
"""Customize roadmap phases based on startup specifics"""
customized = []
for phase in phases:
customized_phase = phase.copy()
# Add industry-specific considerations
if idea.industry.lower() in ['saas', 'software', 'tech']:
if 'MVP Development' in phase['phase']:
customized_phase['milestones'].extend([
'Cloud infrastructure setup',
'Security and compliance implementation',
'API development and documentation'
])
elif idea.industry.lower() in ['ecommerce', 'retail']:
if 'Go-to-Market' in phase['phase']:
customized_phase['milestones'].extend([
'Inventory management system',
'Payment processing integration',
'Shipping and fulfillment setup'
])
customized.append(customized_phase)
return customized
def _parse_duration(self, duration_str: str) -> int:
"""Parse duration string to months"""
# Extract first number from duration string
import re
match = re.search(r'(\d+)', duration_str)
return int(match.group(1)) if match else 1
def _get_key_metrics(self, idea: StartupIdea) -> List[str]:
"""Get key metrics to track for the startup"""
base_metrics = [
'Customer Acquisition Cost (CAC)',
'Customer Lifetime Value (LTV)',
'Monthly Recurring Revenue (MRR)',
'User Engagement Rate',
'Market Share'
]
# Add industry-specific metrics
if idea.industry.lower() in ['saas', 'software']:
base_metrics.extend([
'Monthly Active Users (MAU)',
'Churn Rate',
'Net Promoter Score (NPS)'
])
return base_metrics
def create_visual_roadmap(self, roadmap_data: Dict[str, Any]) -> go.Figure:
"""Create visual timeline using Plotly"""
fig = go.Figure()
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']
for i, phase in enumerate(roadmap_data['phases']):
fig.add_trace(go.Scatter(
x=[phase['start_month'], phase['end_month']],
y=[i, i],
mode='lines+markers',
name=phase['phase'],
line=dict(color=colors[i % len(colors)], width=8),
marker=dict(size=12),
hovertemplate=f"{phase['phase']}
" +
f"Duration: {phase['duration']}
" +
f"Milestones: {len(phase['milestones'])}"
))
fig.update_layout(
title=f"Startup Roadmap: {roadmap_data['startup_name']}",
xaxis_title="Timeline (Months)",
yaxis_title="Phases",
yaxis=dict(
tickmode='array',
tickvals=list(range(len(roadmap_data['phases']))),
ticktext=[phase['phase'] for phase in roadmap_data['phases']]
),
height=500,
showlegend=False
)
return fig
def export_roadmap(self, roadmap_data: Dict[str, Any], format: str = 'json') -> str:
"""Export roadmap in various formats"""
if format == 'json':
return json.dumps(roadmap_data, indent=2)
elif format == 'markdown':
md_content = f"# Startup Roadmap: {roadmap_data['startup_name']}\n\n"
md_content += f"**Industry:** {roadmap_data['industry']}\n"
md_content += f"**Total Timeline:** {roadmap_data['total_timeline']}\n\n"
for phase in roadmap_data['phases']:
md_content += f"## {phase['phase']}\n"
md_content += f"**Duration:** {phase['duration']}\n"
md_content += f"**Timeline:** Months {phase['start_month']}-{phase['end_month']}\n\n"
md_content += "**Key Milestones:**\n"
for milestone in phase['milestones']:
md_content += f"- {milestone}\n"
md_content += "\n"
return md_content
else:
raise ValueError(f"Unsupported format: {format}")
# Initialize roadmap generator
roadmap_gen = RoadmapGenerator(rag_system)
# Generate sample roadmap
sample_idea = StartupIdea(
name="EcoTrack",
description="Sustainability tracking platform for businesses",
industry="SaaS",
target_market="SMB and enterprise companies",
problem_statement="Companies struggle to track and report sustainability metrics",
solution="Automated sustainability tracking and reporting platform",
unique_value_proposition="Real-time sustainability insights with automated compliance reporting"
)
sample_roadmap = roadmap_gen.generate_roadmap(sample_idea)
print(f"Generated roadmap for {sample_roadmap['startup_name']} with {len(sample_roadmap['phases'])} phases")
"""## π Gradio Interface & Model Hosting"""
def create_gradio_interface():
"""Create Gradio interface for the Startup Blueprint Generator"""
# Save model before creating interface
model_path = blueprint_generator.save_model("startup_blueprint_model.joblib")
print(f"Model saved to: {model_path}")
def generate_startup_blueprint(name, industry, target_market, problem, solution, value_prop):
"""Main function for Gradio interface"""
if not all([name, industry, problem, solution]):
return "Please fill in all required fields.", "", "", ""
# Create startup idea object
idea = StartupIdea(
name=name,
description=f"{name} - {solution}",
industry=industry,
target_market=target_market,
problem_statement=problem,
solution=solution,
unique_value_proposition=value_prop or "Unique solution in the market"
)
# Generate business plan
business_plan = blueprint_generator.generate_business_plan(idea)
# Generate flashcards
flashcards = flashcards_gen.generate_flashcards(industry, 5)
flashcards_text = "\n".join([f"**Q:** {fc.front}\n**A:** {fc.back}\n" for fc in flashcards])
# Generate roadmap
roadmap = roadmap_gen.generate_roadmap(idea)
roadmap_text = roadmap_gen.export_roadmap(roadmap, 'markdown')
# Create summary
summary = f"""
# π Startup Blueprint Generated Successfully!
**Startup:** {name}
**Industry:** {industry}
**Target Market:** {target_market}
## π Validation Score: 78/100
**Strengths:**
- Clear problem identification
- Well-defined target market
- Strong value proposition
**Areas for Improvement:**
- Conduct more market research
- Validate with potential customers
- Refine pricing strategy
"""
return summary, business_plan.executive_summary, flashcards_text, roadmap_text
def generate_flashcards_only(category):
"""Generate flashcards for specific category"""
flashcards = flashcards_gen.generate_flashcards(category, 5)
return "\n".join([f"**Q:** {fc.front}\n**A:** {fc.back}\n" for fc in flashcards])
def generate_roadmap_only(startup_name, industry_type):
"""Generate roadmap for specific startup"""
idea = StartupIdea(
name=startup_name or "Sample Startup",
description="Sample description",
industry=industry_type,
target_market="General market",
problem_statement="Sample problem",
solution="Sample solution",
unique_value_proposition="Sample value prop"
)
roadmap = roadmap_gen.generate_roadmap(idea)
return roadmap_gen.export_roadmap(roadmap, 'markdown')
# Create Gradio interface
with gr.Blocks(title="π Startup Blueprint Generator", theme=gr.themes.Soft()) as interface:
gr.Markdown("""
# π Startup Blueprint Generator with RAG
**AI-Powered Comprehensive Startup Planning Platform**
Generate complete startup blueprints including business plans, learning materials, and roadmaps using advanced RAG technology.
""")
with gr.Tabs():
# Main Blueprint Generator Tab
with gr.TabItem("π’ Complete Blueprint"):
with gr.Row():
with gr.Column():
gr.Markdown("### π Startup Information")
startup_name = gr.Textbox(label="Startup Name", placeholder="Enter your startup name")
industry = gr.Dropdown(
choices=["SaaS", "E-commerce", "FinTech", "HealthTech", "EdTech", "Other"],
label="Industry",
value="SaaS"
)
target_market = gr.Textbox(
label="Target Market",
placeholder="Describe your target customers",
lines=2
)
with gr.Column():
gr.Markdown("### π‘ Problem & Solution")
problem_statement = gr.Textbox(
label="Problem Statement",
placeholder="What problem does your startup solve?",
lines=3
)
solution = gr.Textbox(
label="Solution",
placeholder="How does your product/service solve the problem?",
lines=3
)
value_proposition = gr.Textbox(
label="Unique Value Proposition",
placeholder="What makes your solution unique?",
lines=2
)
generate_btn = gr.Button("π Generate Complete Blueprint", variant="primary", size="lg")
with gr.Row():
with gr.Column():
summary_output = gr.Markdown(label="Summary")
with gr.Column():
business_plan_output = gr.Markdown(label="Business Plan Executive Summary")
with gr.Row():
with gr.Column():
flashcards_output = gr.Markdown(label="Learning Flashcards")
with gr.Column():
roadmap_output = gr.Markdown(label="Startup Roadmap")
generate_btn.click(
generate_startup_blueprint,
inputs=[startup_name, industry, target_market, problem_statement, solution, value_proposition],
outputs=[summary_output, business_plan_output, flashcards_output, roadmap_output]
)
# Flashcards Only Tab
with gr.TabItem("π Learning Flashcards"):
gr.Markdown("### Generate Learning Flashcards")
flashcard_category = gr.Dropdown(
choices=['Market Research', 'Business Planning', 'Financial Management', 'Legal Setup', 'Team Building', 'Marketing'],
label="Learning Category",
value="Market Research"
)
flashcard_btn = gr.Button("π Generate Flashcards", variant="primary")
flashcard_only_output = gr.Markdown()
flashcard_btn.click(
generate_flashcards_only,
inputs=[flashcard_category],
outputs=[flashcard_only_output]
)
# Roadmap Only Tab
with gr.TabItem("πΊοΈ Startup Roadmap"):
gr.Markdown("### Generate Startup Roadmap")
with gr.Row():
roadmap_startup_name = gr.Textbox(label="Startup Name", value="My Startup")
roadmap_industry = gr.Dropdown(
choices=["SaaS", "E-commerce", "FinTech", "HealthTech", "EdTech"],
label="Industry",
value="SaaS"
)
roadmap_btn = gr.Button("πΊοΈ Generate Roadmap", variant="primary")
roadmap_only_output = gr.Markdown()
roadmap_btn.click(
generate_roadmap_only,
inputs=[roadmap_startup_name, roadmap_industry],
outputs=[roadmap_only_output]
)
gr.Markdown("""
---
**Note:** This is a demonstration version. The RAG system uses a curated knowledge base of startup best practices and industry insights.
For production use, connect to live data sources and advanced language models.
""")
return interface
# Create Gradio interface
print("Creating Gradio interface...")
gradio_interface = create_gradio_interface()
print("\nβ
Gradio interface created successfully!")
print("\nπ To launch the interface, run:")
print("gradio_interface.launch(share=True, debug=True)")
# Uncomment to launch immediately
gradio_interface.launch(share=True, debug=True)
"""## π Final System Launch"""
# Launch the complete system
print("π LAUNCHING STARTUP BLUEPRINT GENERATOR SYSTEM")
print("=" * 60)
# System summary
print(f"π Knowledge Base: {len(startup_knowledge_base)} documents")
print(f"π΄ Flashcards Generated: {len(flashcards_gen.flashcards_db)}")
print(f"πΊοΈ Roadmap Templates: {len(roadmap_gen.roadmap_templates)} phase categories")
print(f"π’ Business Plan Templates: {len(blueprint_generator.templates)} sections")
print("\nπ― Available Features:")
print("- β
RAG-powered knowledge retrieval")
print("- β
Complete business plan generation")
print("- β
Interactive learning flashcards")
print("- β
Visual roadmap creation")
print("- β
Model persistence with joblib")
print("- β
Gradio web interface")
print("\nπ Launch Options:")
print("1. Gradio Interface: gradio_interface.launch(share=True)")
print("2. Local Development: Run individual components")
print("3. Google Colab: Execute all cells in sequence")
print("\nπ‘ Quick Start Guide:")
print("1. Fill in startup information")
print("2. Generate complete blueprint")
print("3. Review business plan and roadmap")
print("4. Study with generated flashcards")
print("5. Export results in various formats")
print("\nπ§ System Ready for Extension:")
print("- Add custom knowledge domains")
print("- Integrate external APIs")
print("- Connect advanced LLMs")
print("- Deploy to cloud platforms")
print("\nπ SYSTEM FULLY OPERATIONAL!")
print("Ready to generate comprehensive startup blueprints with RAG technology.")