File size: 8,367 Bytes
1f2d50a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
# MVP 3 Sprint 1: Interactive Prompt Filling & Dynamic UI Generation
**Sprint Goal:** Transform the current display-only input variable system into interactive input fields that allow users to provide values for prompt variables, preparing for simulated execution.
**Duration:** 2-3 hours
**Priority:** HIGH (New MVP phase)
**Dependencies:** MVP 2 completion (current status)
---
## π― Sprint 1 Objectives
- Implement dynamic generation of interactive `gr.Textbox` components based on selected prompt variables
- Enable users to provide actual input values for prompt templates
- Prepare UI foundation for MVP 3 Sprint 2 (simulated execution)
- Maintain backward compatibility with existing MVP 2 functionality
---
## π Task Breakdown
### **Task 26: Design Strategy for Dynamic UI Components**
- **Task ID:** 26
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 45-60 minutes
- **Dependencies:** Task 25 (Sprint 5 completion)
- **Description:** Research and finalize the strategy for dynamically creating interactive input components in Gradio based on prompt variables. Document the chosen approach and implementation plan.
**Strategy Options Analysis:**
1. **Pre-defined Max Inputs (RECOMMENDED):** Create fixed number of `gr.Textbox` components, show/hide based on prompt variables
2. **Dynamic Group Rendering:** Use `gr.Group` with conditional updates (more complex)
3. **HTML/JS Injection:** Less ideal for Gradio's reactive model
**Decision Criteria:**
- Implementation speed for hackathon timeline
- Gradio state management simplicity
- Maintenance and debugging ease
- User experience quality
**Deliverables:**
- Strategy documentation with chosen approach
- Implementation plan outline
- UI/UX mockup or description
- Technical requirements specification
**Acceptance Criteria:**
1. Clear strategy chosen and documented with rationale
2. Maximum number of input fields determined (recommendation: 5)
3. UI flow and user interaction pattern defined
4. Technical implementation approach detailed
5. Integration points with existing code identified
---
### **Task 27: Implement Dynamic Input Fields in Gradio UI**
- **Task ID:** 27
- **Status:** Todo
- **Priority:** HIGH
- **Estimated Time:** 90-120 minutes
- **Dependencies:** Task 26 completion
- **Description:** Implement the dynamic input field generation system in `app.py`, modify the Gradio UI layout, and update the `handle_find_tools` function to manage input field state.
**Implementation Plan:**
**Phase 1: UI Layout Modification**
- Add predefined `gr.Textbox` components to `gr.Blocks()` layout
- Create container group for input fields with visibility control
- Add "Execute Plan" button (initially hidden)
- Add execution results display area
**Phase 2: Backend Logic Update**
- Modify `handle_find_tools` return signature for multiple outputs
- Implement input field state management logic
- Create `gr.update()` dictionaries for dynamic field control
- Handle edge cases (no variables, too many variables, etc.)
**Phase 3: Event Wiring**
- Update `find_button.click()` event outputs mapping
- Ensure proper state synchronization
- Test Gradio reactive updates
**Technical Specifications:**
```python
# Configuration
MAX_PROMPT_INPUTS = 5 # Recommended maximum
# New UI Components to Add:
- prompt_inputs_group (gr.Column with visibility control)
- prompt_input_fields[] (list of gr.Textbox components)
- execute_button (gr.Button for future sprint)
- execution_output_display (gr.Markdown for results)
# Updated Return Signature:
handle_find_tools() -> Tuple[
dict, # results_output_json (existing)
gr.update, # prompt_inputs_group visibility
*[gr.update], # individual textbox updates (MAX_PROMPT_INPUTS)
gr.update, # execute_button visibility
gr.update # execution_output_display (clear)
]
```
**Acceptance Criteria:**
1. UI dynamically shows correct number of input fields based on selected prompt
2. Input fields have appropriate labels, placeholders, and validation
3. Unused input fields are properly hidden
4. Execute button appears when plan is selected
5. All existing MVP 2 functionality preserved
6. No Gradio errors or state inconsistencies
7. Responsive design maintained across different screen sizes
---
### **Task 28: Manual UI Testing & Integration Validation**
- **Task ID:** 28
- **Status:** Todo
- **Priority:** MEDIUM
- **Estimated Time:** 60-75 minutes
- **Dependencies:** Task 27 completion
- **Description:** Comprehensive testing of the dynamic input field system, validation of user interactions, and final sprint quality assurance.
**Testing Scenarios:**
**Core Functionality Tests:**
1. **Zero Variables:** Query that selects prompt with no input variables
2. **Single Variable:** Prompt requiring one input (e.g., text content)
3. **Multiple Variables:** Prompt with 2-3 input variables
4. **Maximum Variables:** Prompt with 5+ variables (test overflow handling)
5. **Variable Switching:** Quick succession of different prompts
**Edge Case Tests:**
1. **Empty Query Handling:** Ensure input fields are properly hidden
2. **No Results:** Verify fields hidden when no tools found
3. **Error States:** Test behavior during agent errors
4. **Browser Compatibility:** Test in different browsers/window sizes
5. **Mobile Responsiveness:** Verify UI works on smaller screens
**User Experience Tests:**
1. **Label Clarity:** Verify input field labels are descriptive
2. **Placeholder Quality:** Check placeholder text is helpful
3. **Visual Design:** Ensure consistent styling with MVP 2
4. **Interaction Flow:** Test complete user journey from query to inputs
5. **Performance:** Verify smooth transitions and updates
**Quality Assurance:**
- `just install` - Dependencies current
- `just lint` - Code quality standards
- `just format` - Consistent formatting
- `just type-check` - Type safety validation
- `just test` - All existing tests pass
- Manual browser testing - UI functionality
**Sprint Completion:**
- Commit changes with conventional commit message
- Push to GitHub and verify CI pipeline
- Update task status and documentation
- Prepare handoff notes for Sprint 2
**Acceptance Criteria:**
1. All test scenarios pass successfully
2. UI provides smooth, intuitive user experience
3. No regressions in existing MVP 2 functionality
4. Performance remains optimal (<400ms response times)
5. Code quality gates pass (linting, type checking, tests)
6. Documentation updated with new features
7. Sprint 1 marked complete and ready for Sprint 2
---
## π― Sprint 1 Success Criteria
### **Technical Deliverables:**
- [ ] Dynamic input field generation system operational
- [ ] 5 predefined input fields with show/hide functionality
- [ ] Execute button infrastructure in place
- [ ] Backward compatibility with MVP 2 maintained
- [ ] All quality gates passing
### **User Experience Goals:**
- [ ] Intuitive input field interaction
- [ ] Clear labels and helpful placeholders
- [ ] Smooth visual transitions
- [ ] Consistent design language
- [ ] Mobile-friendly responsive design
### **Quality Standards:**
- [ ] No Gradio console errors
- [ ] All existing tests passing
- [ ] New code follows project standards
- [ ] Documentation updated appropriately
- [ ] CI/CD pipeline green
---
## π Next Steps (Sprint 2 Preview)
After Sprint 1 completion, MVP 3 Sprint 2 will focus on:
1. **Input Collection Logic** - Gather user input from dynamic fields
2. **ExecutorAgent Implementation** - Create simulated execution engine
3. **Output Generation** - Mock realistic tool execution results
4. **Result Display Enhancement** - Show simulated execution output
---
## π Risk Assessment
### **Low Risk:**
- UI component implementation (familiar with Gradio)
- Integration with existing codebase (well-structured)
- Testing methodology (established patterns)
### **Medium Risk:**
- Gradio state management complexity
- Performance with multiple dynamic updates
- Mobile responsiveness edge cases
### **Mitigation Strategies:**
- Thorough testing with different input combinations
- Performance monitoring during development
- Progressive enhancement approach for complex features
---
**Sprint Priority:** HIGH - Foundation for MVP 3 interactivity
**Confidence Level:** HIGH - Building on solid MVP 2 foundation
**Estimated Sprint Completion:** 95% confidence in 2-3 hours |