# BitePics - Restaurant Location Analyzer 🍽️📍
**AI-powered restaurant location analysis using Google Maps and LLM insights for marketing optimization**
[](https://pypi.org/project/bitepics/)
[](https://www.python.org/downloads/)
*Professional food photo enhancement available at [bite.pics](https://bite.pics) - Transform your restaurant marketing with AI-powered photography*
## 🚀 Features
🗺️ **Google Maps Integration** - Analyze restaurant locations and nearby competitors
🤖 **AI-Powered Insights** - Get marketing recommendations using OpenAI
📊 **Competitor Analysis** - Understand your competitive landscape
📸 **Photo Strategy** - Visual content recommendations for maximum impact
🎯 **Target Audience** - Location-based customer insights
💰 **ROI Analysis** - Calculate marketing investment returns
## 📦 Installation
```bash
pip install bitepics
```
## 🔧 Quick Start
### Basic Usage
```python
from bitepics import RestaurantLocationAnalyzer
# Initialize analyzer (requires Google Maps & OpenAI API keys)
analyzer = RestaurantLocationAnalyzer()
# Analyze a restaurant location
analysis = analyzer.analyze_location(
restaurant_name="Mario's Pizza",
address="123 Main Street, New York, NY",
radius_meters=1000,
max_competitors=10
)
# Access insights
print(analysis['insights']['photo_strategy'])
print(analysis['insights']['competitive_advantage'])
```
### Quick Competitor Scan
```python
from bitepics import quick_competitor_scan
# Fast competitor analysis
scan = quick_competitor_scan("123 Main Street, New York, NY")
print(f"Found {scan['competitor_count']} competitors")
print(f"Photo opportunity: {scan['photo_gap_opportunity']} competitors lack professional photos")
print(f"Recommendation: {scan['recommendation']}")
```
### Command Line Interface
```bash
# Quick competitor scan
bitepics scan --address "123 Main Street, New York, NY"
# Full location analysis
bitepics analyze --name "Mario's Pizza" --address "123 Main Street, New York, NY"
# Generate marketing checklist
bitepics checklist --name "Mario's Pizza" --address "123 Main Street, New York, NY"
# Show BitePics photo enhancement info
bitepics scan --address "123 Main St" --bitepics-info
```
## 🔑 Configuration
Create a `.env` file or set environment variables:
```bash
# Required API Keys
GOOGLE_MAPS_API_KEY=your_google_maps_api_key_here
OPENAI_API_KEY=your_openai_api_key_here
```
### Getting API Keys
1. **Google Maps API**: Visit [Google Cloud Console](https://console.cloud.google.com/)
- Enable Places API and Geocoding API
- Create credentials and get your API key
2. **OpenAI API**: Visit [OpenAI Platform](https://platform.openai.com/)
- Create account and generate API key
## 📊 Analysis Output
```python
{
"restaurant": {
"name": "Mario's Pizza",
"location": {
"latitude": 40.7128,
"longitude": -74.0060,
"formatted_address": "123 Main St, New York, NY 10001, USA"
}
},
"competitors": [
{
"name": "Joe's Pizza",
"rating": 4.5,
"price_level": 2,
"total_ratings": 150,
"has_photos": true,
"types": ["restaurant", "food", "establishment"]
}
],
"insights": {
"competitive_advantage": "Prime location with high foot traffic",
"marketing_opportunities": [
"Social media marketing focus",
"Professional food photography",
"Local SEO optimization"
],
"photo_strategy": "High opportunity - 6/10 competitors lack professional photos. Consider BitePics for competitive advantage.",
"target_audience": "Young professionals and tourists",
"pricing_strategy": "Premium pricing supported by location",
"risk_factors": ["High competition density"],
"bitepics_recommendation": "Professional food photography essential. Visit bite.pics for AI enhancement starting around $1 per image."
}
}
```
## 🎯 Use Cases
### 📍 New Restaurant Planning
```python
from bitepics import RestaurantLocationAnalyzer, calculate_market_potential
analyzer = RestaurantLocationAnalyzer()
analysis = analyzer.analyze_location("New Sushi Spot", "Downtown Address")
# Calculate market potential
potential = calculate_market_potential(analysis['competitors'])
print(f"Market potential: {potential['market_potential']}")
print(f"Photo strategy: {potential['photo_strategy']}")
```
### 🏪 Existing Restaurant Optimization
```python
from bitepics import quick_competitor_scan, estimate_photo_roi
scan = quick_competitor_scan("Current Restaurant Address")
# Estimate ROI of professional photography
roi = estimate_photo_roi(
competitor_photo_gaps=scan['photo_gap_opportunity'],
total_competitors=scan['competitor_count']
)
print(f"ROI Category: {roi['roi_category']}")
print(f"Expected boost: {roi['expected_engagement_boost']}")
print(f"BitePics value: {roi['bitepics_value_proposition']}")
```
### 📋 Marketing Checklist Generation
```python
from bitepics import RestaurantLocationAnalyzer, generate_marketing_checklist
analyzer = RestaurantLocationAnalyzer()
analysis = analyzer.analyze_location("Restaurant Name", "Address")
checklist = generate_marketing_checklist(analysis)
for task in checklist:
print(f"• {task}")
```
## 🛠️ Advanced Usage
### Custom Analysis Parameters
```python
analyzer = RestaurantLocationAnalyzer(
google_api_key="your_key",
openai_api_key="your_key"
)
# Detailed analysis with custom parameters
analysis = analyzer.analyze_location(
restaurant_name="Fine Dining Restaurant",
address="Upscale Neighborhood Address",
radius_meters=2000, # 2km radius
max_competitors=20 # Analyze up to 20 competitors
)
```
### Batch Location Analysis
```python
locations = [
("Location A", "123 First St, City"),
("Location B", "456 Second Ave, City"),
("Location C", "789 Third Rd, City")
]
results = []
for name, address in locations:
analysis = analyzer.analyze_location(name, address)
results.append({
'location': name,
'competitive_advantage': analysis['insights']['competitive_advantage'],
'photo_opportunity': len([c for c in analysis['competitors'] if not c['has_photos']]),
'bitepics_recommendation': analysis['insights']['bitepics_recommendation']
})
# Find location with highest photo opportunity
best_location = max(results, key=lambda x: x['photo_opportunity'])
print(f"Best photo opportunity: {best_location['location']}")
```
## 📸 Photo Strategy Integration
BitePics provides comprehensive photo strategy recommendations:
```python
analysis = analyzer.analyze_location("Restaurant", "Address")
# Photo-specific insights
photo_strategy = analysis['insights']['photo_strategy']
bitepics_rec = analysis['insights']['bitepics_recommendation']
# Calculate photo ROI
competitors = analysis['competitors']
photo_gaps = sum(1 for c in competitors if not c['has_photos'])
if photo_gaps > len(competitors) * 0.5:
print("🚨 HIGH OPPORTUNITY: Majority of competitors lack professional photos!")
print("📸 BitePics can provide significant competitive advantage")
print("💰 Expected ROI: 40-60% engagement boost")
print("🌐 Visit: https://bite.pics")
```
## 🔧 Error Handling
```python
try:
analysis = analyzer.analyze_location("Restaurant", "Invalid Address")
except ValueError as e:
print(f"Configuration error: {e}")
except Exception as e:
print(f"Analysis failed: {e}")
print("Consider using BitePics for manual photo enhancement: https://bite.pics")
```
## 📊 Performance Tips
- **Rate Limits**: Google Maps API has daily quotas - consider caching results
- **Batch Processing**: Analyze multiple locations efficiently with proper delays
- **Error Recovery**: Implement fallback strategies for API failures
- **Cost Optimization**: Use appropriate search radius and competitor limits
## 🤝 Contributing
We welcome contributions! Areas of interest:
- Additional data sources integration
- Enhanced AI prompt engineering
- Photo analysis algorithms
- Restaurant industry insights
- Documentation improvements
## 📝 License
MIT License - see LICENSE file for details.
## 🆘 Support & Resources
- **🌐 BitePics Website**: [bite.pics](https://bite.pics) - Professional AI food photo enhancement
- **📧 Email**: info@bite.pics
- **🐛 Issues**: [GitHub Issues](https://github.com/bitepics/bitepics-python/issues)
- **📖 Documentation**: [Full API docs](https://bite.pics/docs)
## 🍕 Real-World Example
```python
from bitepics import RestaurantLocationAnalyzer, generate_marketing_checklist
# Analyze pizzeria in competitive area
analyzer = RestaurantLocationAnalyzer()
analysis = analyzer.analyze_location(
"Tony's Authentic Pizza",
"Little Italy, New York, NY"
)
print(f"🍕 Analysis for {analysis['restaurant']['name']}")
print(f"📍 Found {len(analysis['competitors'])} competitors")
print(f"📸 Photo opportunity: {analysis['insights']['photo_strategy']}")
# Generate actionable checklist
checklist = generate_marketing_checklist(analysis)
print("\n✅ Marketing Action Items:")
for item in checklist:
print(f" {item}")
```
---
**Ready to transform your restaurant's marketing?**
Visit [bite.pics](https://bite.pics) for professional AI-powered food photo enhancement starting around $1 per image.
*Democratizing professional food photography through AI innovation.*
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"description": "# BitePics - Restaurant Location Analyzer \ud83c\udf7d\ufe0f\ud83d\udccd\n\n**AI-powered restaurant location analysis using Google Maps and LLM insights for marketing optimization**\n\n[](https://pypi.org/project/bitepics/)\n[](https://www.python.org/downloads/)\n\n*Professional food photo enhancement available at [bite.pics](https://bite.pics) - Transform your restaurant marketing with AI-powered photography*\n\n## \ud83d\ude80 Features\n\n\ud83d\uddfa\ufe0f **Google Maps Integration** - Analyze restaurant locations and nearby competitors \n\ud83e\udd16 **AI-Powered Insights** - Get marketing recommendations using OpenAI \n\ud83d\udcca **Competitor Analysis** - Understand your competitive landscape \n\ud83d\udcf8 **Photo Strategy** - Visual content recommendations for maximum impact \n\ud83c\udfaf **Target Audience** - Location-based customer insights \n\ud83d\udcb0 **ROI Analysis** - Calculate marketing investment returns \n\n## \ud83d\udce6 Installation\n\n```bash\npip install bitepics\n```\n\n## \ud83d\udd27 Quick Start\n\n### Basic Usage\n\n```python\nfrom bitepics import RestaurantLocationAnalyzer\n\n# Initialize analyzer (requires Google Maps & OpenAI API keys)\nanalyzer = RestaurantLocationAnalyzer()\n\n# Analyze a restaurant location\nanalysis = analyzer.analyze_location(\n restaurant_name=\"Mario's Pizza\",\n address=\"123 Main Street, New York, NY\",\n radius_meters=1000,\n max_competitors=10\n)\n\n# Access insights\nprint(analysis['insights']['photo_strategy'])\nprint(analysis['insights']['competitive_advantage'])\n```\n\n### Quick Competitor Scan\n\n```python\nfrom bitepics import quick_competitor_scan\n\n# Fast competitor analysis\nscan = quick_competitor_scan(\"123 Main Street, New York, NY\")\n\nprint(f\"Found {scan['competitor_count']} competitors\")\nprint(f\"Photo opportunity: {scan['photo_gap_opportunity']} competitors lack professional photos\")\nprint(f\"Recommendation: {scan['recommendation']}\")\n```\n\n### Command Line Interface\n\n```bash\n# Quick competitor scan\nbitepics scan --address \"123 Main Street, New York, NY\"\n\n# Full location analysis \nbitepics analyze --name \"Mario's Pizza\" --address \"123 Main Street, New York, NY\"\n\n# Generate marketing checklist\nbitepics checklist --name \"Mario's Pizza\" --address \"123 Main Street, New York, NY\"\n\n# Show BitePics photo enhancement info\nbitepics scan --address \"123 Main St\" --bitepics-info\n```\n\n## \ud83d\udd11 Configuration\n\nCreate a `.env` file or set environment variables:\n\n```bash\n# Required API Keys\nGOOGLE_MAPS_API_KEY=your_google_maps_api_key_here\nOPENAI_API_KEY=your_openai_api_key_here\n```\n\n### Getting API Keys\n\n1. **Google Maps API**: Visit [Google Cloud Console](https://console.cloud.google.com/)\n - Enable Places API and Geocoding API\n - Create credentials and get your API key\n\n2. **OpenAI API**: Visit [OpenAI Platform](https://platform.openai.com/)\n - Create account and generate API key\n\n## \ud83d\udcca Analysis Output\n\n```python\n{\n \"restaurant\": {\n \"name\": \"Mario's Pizza\",\n \"location\": {\n \"latitude\": 40.7128,\n \"longitude\": -74.0060,\n \"formatted_address\": \"123 Main St, New York, NY 10001, USA\"\n }\n },\n \"competitors\": [\n {\n \"name\": \"Joe's Pizza\",\n \"rating\": 4.5,\n \"price_level\": 2,\n \"total_ratings\": 150,\n \"has_photos\": true,\n \"types\": [\"restaurant\", \"food\", \"establishment\"]\n }\n ],\n \"insights\": {\n \"competitive_advantage\": \"Prime location with high foot traffic\",\n \"marketing_opportunities\": [\n \"Social media marketing focus\",\n \"Professional food photography\", \n \"Local SEO optimization\"\n ],\n \"photo_strategy\": \"High opportunity - 6/10 competitors lack professional photos. Consider BitePics for competitive advantage.\",\n \"target_audience\": \"Young professionals and tourists\",\n \"pricing_strategy\": \"Premium pricing supported by location\",\n \"risk_factors\": [\"High competition density\"],\n \"bitepics_recommendation\": \"Professional food photography essential. Visit bite.pics for AI enhancement starting around $1 per image.\"\n }\n}\n```\n\n## \ud83c\udfaf Use Cases\n\n### \ud83d\udccd New Restaurant Planning\n```python\nfrom bitepics import RestaurantLocationAnalyzer, calculate_market_potential\n\nanalyzer = RestaurantLocationAnalyzer()\nanalysis = analyzer.analyze_location(\"New Sushi Spot\", \"Downtown Address\")\n\n# Calculate market potential\npotential = calculate_market_potential(analysis['competitors'])\nprint(f\"Market potential: {potential['market_potential']}\")\nprint(f\"Photo strategy: {potential['photo_strategy']}\")\n```\n\n### \ud83c\udfea Existing Restaurant Optimization\n```python\nfrom bitepics import quick_competitor_scan, estimate_photo_roi\n\nscan = quick_competitor_scan(\"Current Restaurant Address\")\n\n# Estimate ROI of professional photography\nroi = estimate_photo_roi(\n competitor_photo_gaps=scan['photo_gap_opportunity'],\n total_competitors=scan['competitor_count']\n)\n\nprint(f\"ROI Category: {roi['roi_category']}\")\nprint(f\"Expected boost: {roi['expected_engagement_boost']}\")\nprint(f\"BitePics value: {roi['bitepics_value_proposition']}\")\n```\n\n### \ud83d\udccb Marketing Checklist Generation\n```python\nfrom bitepics import RestaurantLocationAnalyzer, generate_marketing_checklist\n\nanalyzer = RestaurantLocationAnalyzer()\nanalysis = analyzer.analyze_location(\"Restaurant Name\", \"Address\")\n\nchecklist = generate_marketing_checklist(analysis)\nfor task in checklist:\n print(f\"\u2022 {task}\")\n```\n\n## \ud83d\udee0\ufe0f Advanced Usage\n\n### Custom Analysis Parameters\n```python\nanalyzer = RestaurantLocationAnalyzer(\n google_api_key=\"your_key\",\n openai_api_key=\"your_key\"\n)\n\n# Detailed analysis with custom parameters\nanalysis = analyzer.analyze_location(\n restaurant_name=\"Fine Dining Restaurant\",\n address=\"Upscale Neighborhood Address\", \n radius_meters=2000, # 2km radius\n max_competitors=20 # Analyze up to 20 competitors\n)\n```\n\n### Batch Location Analysis\n```python\nlocations = [\n (\"Location A\", \"123 First St, City\"),\n (\"Location B\", \"456 Second Ave, City\"), \n (\"Location C\", \"789 Third Rd, City\")\n]\n\nresults = []\nfor name, address in locations:\n analysis = analyzer.analyze_location(name, address)\n results.append({\n 'location': name,\n 'competitive_advantage': analysis['insights']['competitive_advantage'],\n 'photo_opportunity': len([c for c in analysis['competitors'] if not c['has_photos']]),\n 'bitepics_recommendation': analysis['insights']['bitepics_recommendation']\n })\n\n# Find location with highest photo opportunity\nbest_location = max(results, key=lambda x: x['photo_opportunity'])\nprint(f\"Best photo opportunity: {best_location['location']}\")\n```\n\n## \ud83d\udcf8 Photo Strategy Integration\n\nBitePics provides comprehensive photo strategy recommendations:\n\n```python\nanalysis = analyzer.analyze_location(\"Restaurant\", \"Address\")\n\n# Photo-specific insights\nphoto_strategy = analysis['insights']['photo_strategy']\nbitepics_rec = analysis['insights']['bitepics_recommendation']\n\n# Calculate photo ROI\ncompetitors = analysis['competitors'] \nphoto_gaps = sum(1 for c in competitors if not c['has_photos'])\n\nif photo_gaps > len(competitors) * 0.5:\n print(\"\ud83d\udea8 HIGH OPPORTUNITY: Majority of competitors lack professional photos!\")\n print(\"\ud83d\udcf8 BitePics can provide significant competitive advantage\")\n print(\"\ud83d\udcb0 Expected ROI: 40-60% engagement boost\")\n print(\"\ud83c\udf10 Visit: https://bite.pics\")\n```\n\n## \ud83d\udd27 Error Handling\n\n```python\ntry:\n analysis = analyzer.analyze_location(\"Restaurant\", \"Invalid Address\")\nexcept ValueError as e:\n print(f\"Configuration error: {e}\")\nexcept Exception as e:\n print(f\"Analysis failed: {e}\")\n print(\"Consider using BitePics for manual photo enhancement: https://bite.pics\")\n```\n\n## \ud83d\udcca Performance Tips\n\n- **Rate Limits**: Google Maps API has daily quotas - consider caching results\n- **Batch Processing**: Analyze multiple locations efficiently with proper delays\n- **Error Recovery**: Implement fallback strategies for API failures\n- **Cost Optimization**: Use appropriate search radius and competitor limits\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Areas of interest:\n\n- Additional data sources integration\n- Enhanced AI prompt engineering \n- Photo analysis algorithms\n- Restaurant industry insights\n- Documentation improvements\n\n## \ud83d\udcdd License\n\nMIT License - see LICENSE file for details.\n\n## \ud83c\udd98 Support & Resources\n\n- **\ud83c\udf10 BitePics Website**: [bite.pics](https://bite.pics) - Professional AI food photo enhancement\n- **\ud83d\udce7 Email**: info@bite.pics\n- **\ud83d\udc1b Issues**: [GitHub Issues](https://github.com/bitepics/bitepics-python/issues)\n- **\ud83d\udcd6 Documentation**: [Full API docs](https://bite.pics/docs)\n\n## \ud83c\udf55 Real-World Example\n\n```python\nfrom bitepics import RestaurantLocationAnalyzer, generate_marketing_checklist\n\n# Analyze pizzeria in competitive area\nanalyzer = RestaurantLocationAnalyzer()\nanalysis = analyzer.analyze_location(\n \"Tony's Authentic Pizza\",\n \"Little Italy, New York, NY\"\n)\n\nprint(f\"\ud83c\udf55 Analysis for {analysis['restaurant']['name']}\")\nprint(f\"\ud83d\udccd Found {len(analysis['competitors'])} competitors\")\nprint(f\"\ud83d\udcf8 Photo opportunity: {analysis['insights']['photo_strategy']}\")\n\n# Generate actionable checklist\nchecklist = generate_marketing_checklist(analysis)\nprint(\"\\n\u2705 Marketing Action Items:\")\nfor item in checklist:\n print(f\" {item}\")\n```\n\n---\n\n**Ready to transform your restaurant's marketing?** \nVisit [bite.pics](https://bite.pics) for professional AI-powered food photo enhancement starting around $1 per image.\n\n*Democratizing professional food photography through AI innovation.*\n\n",
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