Remove added questions from form

Author: suhailskhan

CHANGELOG.md

@@ -5,6 +5,51 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [v0.0.1-alpha.2] - 2025-06-02
+
+### Added
+- JWT-based authentication system with user login/logout functionality
+  - Protected edit and delete operations behind authentication
+- Personal "My Statistics" view for logged-in users to see their own usage data
+- Authentication middleware for secure session management
+- Entry management features in Raw Data tab:
+  - Inline editing of existing entries with pre-filled forms
+  - Entry duplication functionality for quick similar submissions
+  - Entry deletion with confirmation dialogs
+- Row selection capability in Raw Data tab
+- Infrastructure destroy workflow for automated cleanup
+- OpenTofu support as alternative to Terraform
+- Environment-aware JWT configuration for deployment security
+- AWS Secrets Manager integration for JWT secrets
+
+### Enhanced
+- Code organization with new utility modules:
+  - `analytics_utils.py` for shared data processing functions
+  - `form_utils.py` for survey form-related functionality
+  - `visualization_utils.py` for data visualization components
+  - `auth_middleware.py` for authentication handling
+- Tab structure and navigation:
+  - Renamed tabs for better clarity
+  - "Past Submissions" tab now requires authentication
+- Form validation and error handling across all features
+- Seed scripts now automatically create data directory if missing
+- Eliminated code duplication through shared utilities (~200 lines reduced)
+- Import consolidation and code organization
+
+### Security
+- Secure cookie settings for production deployment
+- JWT audience configuration with environment awareness
+- Environment variable validation for deployed environments
+
+### Infrastructure
+- Moved from `terraform/` to `infra/` directory structure
+- Added OpenTofu configuration files alongside Terraform
+- Enhanced GitHub Actions workflow with OpenTofu support
+- Infrastructure destroy automation with safety validations
+
+### Dependencies
+- Added PyJWT for JSON Web Token authentication
+
 ## [v0.0.1-alpha.1] - 2025-05-23
 
 ### Added
@@ -55,4 +100,5 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - SQLite database/CSV
 - Environment variable configuration system
 
+[v0.0.1-alpha.2]: https://github.com/suhailskhan/ai-usage-log/releases/tag/v0.0.1-alpha.2
 [v0.0.1-alpha.1]: https://github.com/suhailskhan/ai-usage-log/releases/tag/v0.0.1-alpha.1

README.md

@@ -16,13 +16,10 @@ The application provides the following analytics features:
 
 - **Purpose Distribution:** Visualize the distribution of AI tool usage purposes using pie charts.
 - **Duration Analysis:** Analyze the total and average duration of tasks by AI tool.
-- **Time Saved Analysis:** Compare the time taken with and without AI assistance, including total and average time saved.
-- **Tool Effectiveness Benchmarking:** Evaluate AI tools based on average time saved, satisfaction, and workflow impact.
-- **Complexity vs Impact:** Understand the relationship between task complexity and workflow impact.
-- **Satisfaction vs Efficiency:** Explore the correlation between user satisfaction and time saved.
-- **Manager/Team Insights:** Gain insights into team performance, including average time saved and satisfaction by manager.
-- **Purpose-based Use Cases:** Analyze average time saved, satisfaction, and workflow impact for different purposes.
-- **Trend & Seasonality Analysis:** Identify trends in AI tool usage over time, including daily and weekly patterns.
+- **Tool Effectiveness Benchmarking:** Evaluate AI tools based on average duration and task count.
+- **Manager/Team Insights:** Gain insights into team performance, including task count and average duration by manager.
+- **Purpose-based Use Cases:** Analyze task count and average duration for different purposes.
+- **Trend & Seasonality Analysis:** Identify trends in AI tool usage over time, including daily submissions and weekly duration patterns.
 
 ## Usage
 

analytics_utils.py

@@ -12,8 +12,8 @@ def prepare_dataframe(entries, workflow_impact_map=None, task_complexity_map=Non
     
     Args:
         entries: List of entry dictionaries
-        workflow_impact_map: Optional mapping for workflow impact reverse lookup
-        task_complexity_map: Optional mapping for task complexity reverse lookup
+        workflow_impact_map: Optional mapping for workflow impact reverse lookup (kept for backwards compatibility)
+        task_complexity_map: Optional mapping for task complexity reverse lookup (kept for backwards compatibility)
     
     Returns:
         Cleaned pandas DataFrame
@@ -22,14 +22,13 @@ def prepare_dataframe(entries, workflow_impact_map=None, task_complexity_map=Non
     if df.empty:
         return df
 
-    # Apply reverse mappings if provided
+    # Legacy field handling for backwards compatibility with old data
     if workflow_impact_map and 'Workflow Impact' in df.columns:
         df['Workflow Impact'] = df['Workflow Impact'].map(workflow_impact_map).fillna(df['Workflow Impact'])
     if task_complexity_map and 'Task Complexity' in df.columns:
         df['Task Complexity'] = df['Task Complexity'].map(task_complexity_map).fillna(df['Task Complexity'])
 
-    # Calculate time saved and ensure timestamp is datetime
-    df["Time Saved"] = df["Time Without AI"] - df["Duration"]
+    # Ensure timestamp is datetime
     df["Timestamp"] = pd.to_datetime(df["Timestamp"], errors="coerce")
 
     return df
@@ -68,9 +67,7 @@ def calculate_basic_stats(df):
 
     stats = {
         'total_entries': len(df),
-        'avg_time_saved': df["Time Saved"].mean() if "Time Saved" in df.columns else 0,
         'avg_duration': df["Duration"].mean() if "Duration" in df.columns else 0,
-        'avg_satisfaction': df["Satisfaction"].mean() if "Satisfaction" in df.columns else 0,
     }
 
     # Tool-specific stats
@@ -125,62 +122,29 @@ def calculate_tool_effectiveness(df):
     if df.empty or "AI Tool" not in df.columns:
         return pd.DataFrame()
 
-    agg_dict = {}
-    if "Time Saved" in df.columns:
-        agg_dict["Time Saved"] = "mean"
-    if "Satisfaction" in df.columns:
-        agg_dict["Satisfaction"] = "mean"
-    if "Workflow Impact" in df.columns:
-        agg_dict["Workflow Impact"] = lambda x: x.value_counts().index[0] if not x.empty else None
-    
-    if not agg_dict:
-        return pd.DataFrame()
+    agg_dict = {
+        "Duration": ["mean", "count"]  # Average duration and task count
+    }
 
     tool_stats = df.groupby("AI Tool").agg(agg_dict).reset_index()
 
-    # Rename columns for clarity
-    rename_dict = {
-        "Time Saved": "Avg Time Saved",
-        "Satisfaction": "Avg Satisfaction",
-        "Workflow Impact": "Most Common Workflow Impact"
-    }
-    tool_stats.rename(columns=rename_dict, inplace=True)
+    # Flatten column names
+    tool_stats.columns = ["AI Tool", "Avg Duration", "# Tasks"]
 
     return tool_stats
 
 
 def calculate_complexity_analysis(df):
     """
-    Calculate task complexity analysis.
+    Calculate task complexity analysis (legacy function - returns empty for backwards compatibility).
     
     Args:
         df: pandas DataFrame with usage data
     
     Returns:
-        DataFrame with complexity analysis
+        Empty DataFrame (complexity analysis no longer supported)
     """
-    if df.empty or "Task Complexity" not in df.columns:
-        return pd.DataFrame()
-    
-    agg_dict = {}
-    if "Time Saved" in df.columns:
-        agg_dict["Time Saved"] = "mean"
-    if "Satisfaction" in df.columns:
-        agg_dict["Satisfaction"] = "mean"
-    
-    if not agg_dict:
-        return pd.DataFrame()
-    
-    complexity_stats = df.groupby("Task Complexity").agg(agg_dict).reset_index()
-    
-    # Rename columns for clarity
-    rename_dict = {
-        "Time Saved": "Avg Time Saved",
-        "Satisfaction": "Avg Satisfaction"
-    }
-    complexity_stats.rename(columns=rename_dict, inplace=True)
-    
-    return complexity_stats
+    return pd.DataFrame()
 
 
 def calculate_manager_insights(df):
@@ -196,21 +160,14 @@ def calculate_manager_insights(df):
     if df.empty or "Manager" not in df.columns:
         return pd.DataFrame()
 
-    agg_dict = {"Duration": "count"}  # Count of tasks
-    if "Time Saved" in df.columns:
-        agg_dict["Time Saved"] = "mean"
-    if "Satisfaction" in df.columns:
-        agg_dict["Satisfaction"] = "mean"
+    agg_dict = {
+        "Duration": ["count", "mean"]  # Count of tasks and average duration
+    }
 
     manager_stats = df.groupby("Manager").agg(agg_dict).reset_index()
 
-    # Rename columns for clarity
-    rename_dict = {
-        "Time Saved": "Avg Time Saved",
-        "Satisfaction": "Avg Satisfaction",
-        "Duration": "# Tasks"
-    }
-    manager_stats.rename(columns=rename_dict, inplace=True)
+    # Flatten column names
+    manager_stats.columns = ["Manager", "# Tasks", "Avg Duration"]
 
     return manager_stats
 
@@ -228,23 +185,13 @@ def calculate_purpose_insights(df):
     if df.empty or "Purpose" not in df.columns:
         return pd.DataFrame()
 
-    agg_dict = {"Duration": "count"}  # Count of tasks
-    if "Time Saved" in df.columns:
-        agg_dict["Time Saved"] = "mean"
-    if "Satisfaction" in df.columns:
-        agg_dict["Satisfaction"] = "mean"
-    if "Workflow Impact" in df.columns:
-        agg_dict["Workflow Impact"] = lambda x: x.value_counts().index[0] if not x.empty else None
+    agg_dict = {
+        "Duration": ["count", "mean"]  # Count of tasks and average duration
+    }
 
     purpose_stats = df.groupby("Purpose").agg(agg_dict).reset_index()
 
-    # Rename columns for clarity
-    rename_dict = {
-        "Time Saved": "Avg Time Saved",
-        "Satisfaction": "Avg Satisfaction",
-        "Workflow Impact": "Most Common Workflow Impact",
-        "Duration": "# Tasks"
-    }
-    purpose_stats.rename(columns=rename_dict, inplace=True)
+    # Flatten column names
+    purpose_stats.columns = ["Purpose", "# Tasks", "Avg Duration"]
 
     return purpose_stats

app.py

@@ -199,15 +199,10 @@ def prepare_dataframe(entries):
         manager_val = form_data['manager'][0] if form_data['manager'] else ""
         ai_tool_val = form_data['ai_tool'][0] if form_data['ai_tool'] else ""
         purpose_val = form_data['purpose'][0] if form_data['purpose'] else ""
-        complexity_val = form_data['complexity'] if form_data['complexity'] != "(Select complexity)" else ""
-        complexity_num = TASK_COMPLEXITY_MAP.get(complexity_val, None)
-        workflow_impact_val = form_data['workflow_impact'] if form_data['workflow_impact'] != "(Select impact)" else ""
-        workflow_impact_num = WORKFLOW_IMPACT_MAP.get(workflow_impact_val, None)
 
         is_valid, error_message = validate_form_submission(
             form_data['name'], manager_val, ai_tool_val, purpose_val, form_data['result'], 
-            complexity_val, form_data['satisfaction'], form_data['time_without_ai'], 
-            workflow_impact_val, form_data['duration'], workflow_impact_num, complexity_num
+            form_data['duration']
         )
 
         if not is_valid:
@@ -216,8 +211,7 @@ def prepare_dataframe(entries):
         else:
             entry = create_entry_dict(
                 form_data['name'], manager_val, ai_tool_val, purpose_val, form_data['duration'], 
-                complexity_num, form_data['satisfaction'], form_data['time_without_ai'], 
-                workflow_impact_num, form_data['result'], form_data['notes']
+                form_data['result'], form_data['notes']
             )
             st.session_state.entries.append(entry)
             save_entries(st.session_state.entries)
@@ -298,10 +292,6 @@ def prepare_dataframe(entries):
                             'ai_tool': ai_tool_default,
                             'purpose': purpose_default,
                             'duration': original_entry['Duration'],
-                            'complexity': REVERSE_TASK_COMPLEXITY_MAP.get(original_entry['Task Complexity'], 'Easy'),
-                            'satisfaction': original_entry['Satisfaction'],
-                            'time_without_ai': original_entry['Time Without AI'],
-                            'workflow_impact': REVERSE_WORKFLOW_IMPACT_MAP.get(original_entry['Workflow Impact'], 'Little to none'),
                             'result': original_entry['Result/Outcome'],
                             'notes': original_entry.get('Notes', '')
                         }
@@ -355,10 +345,6 @@ def prepare_dataframe(entries):
                             'ai_tool': ai_tool_default,
                             'purpose': purpose_default,
                             'duration': original_entry['Duration'],
-                            'complexity': REVERSE_TASK_COMPLEXITY_MAP.get(original_entry['Task Complexity'], 'Easy'),
-                            'satisfaction': original_entry['Satisfaction'],
-                            'time_without_ai': original_entry['Time Without AI'],
-                            'workflow_impact': REVERSE_WORKFLOW_IMPACT_MAP.get(original_entry['Workflow Impact'], 'Little to none'),
                             'result': original_entry['Result/Outcome'],
                             'notes': original_entry.get('Notes', '')
                         }
@@ -457,15 +443,10 @@ def prepare_dataframe(entries):
                         manager_val = form_data['manager'][0] if form_data['manager'] else ""
                         ai_tool_val = form_data['ai_tool'][0] if form_data['ai_tool'] else ""
                         purpose_val = form_data['purpose'][0] if form_data['purpose'] else ""
-                        complexity_val = form_data['complexity'] if form_data['complexity'] != "(Select complexity)" else ""
-                        complexity_num = TASK_COMPLEXITY_MAP.get(complexity_val, None)
-                        workflow_impact_val = form_data['workflow_impact'] if form_data['workflow_impact'] != "(Select impact)" else ""
-                        workflow_impact_num = WORKFLOW_IMPACT_MAP.get(workflow_impact_val, None)
 
                         is_valid, error_message = validate_form_submission(
                             form_data['name'], manager_val, ai_tool_val, purpose_val, form_data['result'], 
-                            complexity_val, form_data['satisfaction'], form_data['time_without_ai'], 
-                            workflow_impact_val, form_data['duration'], workflow_impact_num, complexity_num
+                            form_data['duration']
                         )
 
                         if not is_valid:
@@ -478,8 +459,7 @@ def prepare_dataframe(entries):
                             # Update the entry
                             updated_entry = create_entry_dict(
                                 form_data['name'], manager_val, ai_tool_val, purpose_val, form_data['duration'], 
-                                complexity_num, form_data['satisfaction'], form_data['time_without_ai'], 
-                                workflow_impact_num, form_data['result'], form_data['notes']
+                                form_data['result'], form_data['notes']
                             )
                             # Preserve the original timestamp
                             updated_entry['Timestamp'] = original_entry['Timestamp']