#!/usr/bin/env python3
"""
Complete Workflow: From Requirements to Perfect Print
This script demonstrates the full end-to-end workflow:
1. Parse natural language requirements
2. Optionally analyze a reference image for dimensions
3. Generate a 3D model (template or parametric)
4. Scale if needed
5. Slice with optimized parameters
6. Print and monitor
7. Detect defects and iterate
Example use case: Creating a tube squeezer for a Gold Bond lotion bottle
based on the reference image showing the bottle dimensions.
"""
import asyncio
from pathlib import Path
async def complete_workflow():
"""
Full workflow from "I need X" to perfect printed object.
"""
# =========================================================================
# PHASE 1: UNDERSTAND REQUIREMENTS
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 1: UNDERSTANDING REQUIREMENTS")
print("=" * 70)
# User's natural language request
user_request = """
I need a tube squeezer for my Gold Bond lotion bottle.
The bottle is about 65mm diameter. I want it to be heavy duty
since lotion is thicker than toothpaste and needs more force.
"""
print(f"\nUser Request:\n{user_request.strip()}")
from bambustudio_mcp.generator.requirements import RequirementsParser
parser = RequirementsParser()
requirements = parser.parse(user_request)
print(f"""
Parsed Requirements:
- Category: {requirements.category.value}
- Primary dimension: {requirements.get_primary_dimension_mm()}mm
- Needs strength: {requirements.needs_strength}
- Fit type: {requirements.fit_type.value}
- Wall thickness: {requirements.wall_thickness_mm}mm
- Reference object: {requirements.reference_object}
""")
# =========================================================================
# PHASE 2: ANALYZE REFERENCE IMAGE (if provided)
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 2: REFERENCE IMAGE ANALYSIS")
print("=" * 70)
# In real usage, this would analyze the user's uploaded image
# showing the lotion bottle next to a ruler
print("""
If user provides a reference image:
- Detect ruler/scale markings for accurate calibration
- Measure bottle diameter from the image
- Extract any design features from similar products
Example analysis from the provided image:
- Detected ruler: Yes (10cm visible)
- Measured bottle width: ~65mm
- Existing squeezer visible: ~38mm (for smaller tube)
- Scale factor needed: 65/25 = 2.6x
""")
# =========================================================================
# PHASE 3: GENERATE 3D MODEL
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 3: GENERATING 3D MODEL")
print("=" * 70)
from bambustudio_mcp.generator.templates import TubeSqueezerTemplate
template = TubeSqueezerTemplate()
# Show available parameters
print("\nTube Squeezer Template Parameters:")
for param in template.parameters:
print(f" - {param.name}: {param.description}")
print(f" Default: {param.default_value}{param.unit}, Range: {param.min_value}-{param.max_value}")
# Generate with custom parameters
custom_params = {
"tube_diameter": 65.0, # For Gold Bond lotion bottle
"clearance": 1.5, # Extra clearance for thick lotion residue
"wall_thickness": 3.0, # Heavy duty
"handle_width": 18.0, # Wider handles for better grip
"add_grip_texture": 1.0, # Add grip ridges
}
print(f"\nGenerating with custom parameters:")
for k, v in custom_params.items():
print(f" {k}: {v}")
# In real usage:
# output_path = Path("/tmp/lotion_squeezer_65mm.stl")
# template.generate(custom_params, output_path)
# print(f"\nGenerated: {output_path}")
print("""
Generated Model Dimensions:
- Slot width: 66.5mm (65mm + 1.5mm clearance)
- Body width: 102.5mm (slot + 2 handles)
- Body depth: 48.75mm (75% of tube diameter)
- Body height: 71.5mm
- Method: CadQuery parametric generation
- Output: /tmp/bambustudio-mcp/generated/lotion_squeezer_65mm.stl
""")
# =========================================================================
# PHASE 4: OPTIMIZE SLICING PARAMETERS
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 4: OPTIMIZING SLICING PARAMETERS")
print("=" * 70)
from bambustudio_mcp.slicer.parameters import (
SlicingParameters,
PRESET_TUBE_SQUEEZER_STRONG,
adjust_for_scale,
)
# Start with heavy-duty preset
base_params = PRESET_TUBE_SQUEEZER_STRONG.parameters
# Adjust for larger size
scale_factor = 65.0 / 25.0 # From standard toothpaste to lotion
adjusted = adjust_for_scale(base_params, scale_factor)
print(f"""
Slicing Parameters (optimized for {scale_factor:.1f}x scale):
Structure:
- Layer height: {adjusted.layer_height}mm
- Wall loops: {adjusted.wall_loops}
- Infill: {adjusted.sparse_infill_density}% {adjusted.sparse_infill_pattern.value}
Speed:
- Outer wall: {adjusted.outer_wall_speed}mm/s
- Inner wall: {adjusted.inner_wall_speed}mm/s
Adhesion:
- Brim width: {adjusted.brim_width}mm
- First layer speed: {adjusted.initial_layer_speed}mm/s
""")
# =========================================================================
# PHASE 5: CREATE ITERATION RECORD
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 5: TRACKING ITERATION")
print("=" * 70)
from bambustudio_mcp.iteration.tracker import IterationTracker
tracker = IterationTracker()
await tracker.initialize()
iteration = await tracker.create_iteration(
model_name="gold_bond_lotion_squeezer",
model_path="/tmp/lotion_squeezer_65mm.stl",
scale_factor=scale_factor,
preset_name="tube_squeezer_strong_adjusted",
)
print(f"""
Iteration Created:
- ID: {iteration.iteration_id}
- Model: {iteration.model_name}
- Scale: {iteration.scale_factor:.2f}x
- Status: {iteration.status}
This will track the print outcome and enable learning for future prints.
""")
# =========================================================================
# PHASE 6: PRINT MONITORING (simulated)
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 6: PRINT MONITORING")
print("=" * 70)
print("""
During printing, the MCP continuously:
1. Monitors printer status via MQTT:
- Nozzle temp: 220°C / 220°C ✓
- Bed temp: 60°C / 60°C ✓
- Progress: 45% (layer 78/175)
- Time remaining: ~1h 30m
2. Captures camera frames (1 FPS):
- Analyzing frame 2847...
- No defects detected
- Quality score: 95/100
3. Watches for issues:
✓ No layer shifts
✓ No stringing
✓ No warping
✓ Good bed adhesion
""")
# =========================================================================
# PHASE 7: RECORD OUTCOME & GET RECOMMENDATIONS
# =========================================================================
print("\n" + "=" * 70)
print("PHASE 7: OUTCOME & RECOMMENDATIONS")
print("=" * 70)
# Simulate completed print with minor stringing
updated = await tracker.record_outcome(
iteration_id=iteration.iteration_id,
status="completed",
quality_score=88.0,
defects=["stringing"],
notes="Successful print. Minor stringing on travel moves.",
print_time_minutes=175,
)
print(f"""
Print Completed:
- Quality Score: {updated.quality_score}/100
- Print Time: {updated.print_time_minutes} minutes
- Defects: {', '.join(updated.defects_detected)}
Improvement Suggestions for Next Print:
""")
for i, suggestion in enumerate(updated.improvement_suggestions, 1):
print(f" {i}. {suggestion}")
# Get parameter recommendations
from bambustudio_mcp.iteration.recommender import ParameterRecommender
recommender = ParameterRecommender()
recommendations = recommender.get_recommendations(
current_params=adjusted,
defects=updated.defects_detected,
iterations=[updated],
)
print("\nParameter Adjustments for Next Iteration:")
for rec in recommendations[:3]:
print(f" - {rec.parameter}: {rec.current_value} → {rec.suggested_value}")
print(f" Reason: {rec.reason}")
# =========================================================================
# SUMMARY
# =========================================================================
print("\n" + "=" * 70)
print("WORKFLOW COMPLETE")
print("=" * 70)
print("""
Summary of what was accomplished:
1. ✅ Parsed "I need a tube squeezer for my lotion bottle" into structured requirements
2. ✅ Determined tube_squeezer category, 65mm diameter, heavy-duty needs
3. ✅ Generated parametric 3D model using CadQuery
4. ✅ Optimized slicing parameters for 2.6x scaled print
5. ✅ Created iteration record for tracking
6. ✅ (Simulated) Monitored print with camera-based quality analysis
7. ✅ Recorded outcome with 88% quality score
8. ✅ Generated recommendations for next iteration
The lotion bottle squeezer is ready! For the next print:
- Increase retraction by 0.5mm to reduce stringing
- Lower nozzle temp by 5°C
""")
if __name__ == "__main__":
asyncio.run(complete_workflow())
