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app(4).py

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+"""
+Digital Forensics Model Card Generator
+A tool for creating standardized model cards for digital forensics AI/ML models
+Compatible with Gradio 5.x
+"""
+
+import gradio as gr
+import json
+from datetime import datetime
+from utils.generator import generate_json_output, generate_markdown_output
+from utils.validators import validate_mmcid
+
+# Version
+GENERATOR_VERSION = "1.0.0"
+
+# Controlled Vocabularies
+CV_USE_CONTEXT = ["Standalone", "Integrated", "Hybrid (both standalone and integrated)"]
+
+CV_CLASSIFICATION = [
+    "Computer Forensics",
+    "Network Forensics", 
+    "Mobile Device Forensics",
+    "Cloud Forensics",
+    "Database Forensics",
+    "Memory Forensics",
+    "Digital Image Forensics",
+    "Digital Video/Audio Forensics",
+    "IoT Forensics",
+    "Multi-domain (covers multiple types)"
+]
+
+CV_REASONING = [
+    "Deductive Reasoning (from general to specific)",
+    "Inductive Reasoning (from specific to general)",
+    "Abductive Reasoning (inference to best explanation)",
+    "Retroductive Reasoning (hypothesis refinement)",
+    "Hybrid/Mixed Reasoning"
+]
+
+CV_BIAS = [
+    "Data Bias (historical, sampling, selection)",
+    "Algorithmic Bias (model architecture, optimization)",
+    "Human Bias (cognitive, confirmation, implicit)",
+    "Deployment Bias (context mismatch)",
+    "Reporting Bias (documentation gaps)",
+    "Measurement Bias (proxy variables)",
+    "Stereotyping Bias (reinforcing stereotypes)",
+    "Automation Bias (over-reliance on automated results)",
+    "No Identified Bias",
+    "Multiple Bias Types"
+]
+
+CV_CAUSE_OF_BIAS = [
+    "Unrepresentative Training Data",
+    "Historical Inequities in Data",
+    "Feature Selection Issues",
+    "Labeling Inconsistencies",
+    "Optimization Objective Mismatch",
+    "Insufficient Diversity in Development Team",
+    "Lack of Domain Expertise",
+    "Temporal Drift (data age/staleness)",
+    "Geographic/Cultural Limitations",
+    "Tool/Method Limitations",
+    "Multiple Causes",
+    "Unknown/Under Investigation"
+]
+
+CV_CAUSE_OF_ERROR = [
+    "Training Error (underfitting)",
+    "Validation Error (model selection issues)",
+    "Testing Error (generalization failure)",
+    "Overfitting (high variance)",
+    "Underfitting (high bias)",
+    "Data Quality Issues (noise, outliers, mislabeling)",
+    "Insufficient Training Data",
+    "Class Imbalance",
+    "Feature Engineering Issues",
+    "Hyperparameter Misconfiguration",
+    "Model Complexity Mismatch",
+    "Adversarial Attack (poisoning, evasion)",
+    "Concept Drift",
+    "Tool Calibration Error",
+    "Human Error in Analysis",
+    "Chain of Custody Issues",
+    "Multiple Error Sources",
+    "Unknown/Under Investigation"
+]
+
+def save_to_file(content, filename):
+    """Helper to save content to a file and return the path"""
+    filepath = f"/tmp/{filename}"
+    with open(filepath, 'w') as f:
+        f.write(content)
+    return filepath
+
+def generate_model_card(*args):
+    """Generate model card outputs from form inputs"""
+    
+    # Parse arguments (metadata section)
+    (mmcid, version, owner, use_context, case_statement, hypothesis,
+     classification, reasoning_type, bias, cause_of_bias, error, cause_of_error, layer_n,
+     classification_other, reasoning_other, bias_other, cause_bias_other, cause_error_other) = args[:18]
+    
+    # Validate MMCID if provided
+    if mmcid and not validate_mmcid(mmcid):
+        return "❌ Invalid MMCID format. Please use format: DF-MC-YYYY-NNN (e.g., DF-MC-2025-001)", None, None
+    
+    # Top level elements (15 pairs of checkbox + text)
+    top_level_args = args[18:48]  # 15 * 2 = 30 args
+    
+    # Process elements (19 pairs of checkbox + text)
+    process_args = args[48:86]  # 19 * 2 = 38 args
+    
+    # Build metadata
+    metadata = {
+        "mmcid": mmcid or "Not specified",
+        "version": version or "N/A",
+        "owner": owner or "Not specified",
+        "use_context": use_context or "Not specified",
+        "case_statement": case_statement,
+        "hypothesis": hypothesis,
+        "classification": list(classification) + ([classification_other] if classification_other else []),
+        "reasoning_type": list(reasoning_type) + ([reasoning_other] if reasoning_other else []),
+        "bias": list(bias) + ([bias_other] if bias_other else []),
+        "cause_of_bias": list(cause_of_bias) + ([cause_bias_other] if cause_bias_other else []),
+        "error": error,
+        "cause_of_error": list(cause_of_error) + ([cause_error_other] if cause_error_other else []),
+        "layer_n": layer_n or "N/A"
+    }
+    
+    # Build top level elements
+    top_level_keys = [
+        "type_of_reasoning", "cause_of_error", "algorithm", "inference", "confounder",
+        "classification", "evaluation", "hypothesis", "tool", "bias_debiasing",
+        "case_statement", "evidence_mc1", "file_type", "data_structure", "degree_of_confidence"
+    ]
+    
+    top_level = {}
+    for i, key in enumerate(top_level_keys):
+        check_val = top_level_args[i*2]
+        desc_val = top_level_args[i*2 + 1]
+        top_level[key] = {
+            "applicable": check_val,
+            "description": desc_val if check_val else ""
+        }
+    
+    # Build process elements
+    process_keys = [
+        "event_data", "parse_raw_data", "validate", "identify_partitions",
+        "process_file_system", "identify_content_carving", "file_type_identification",
+        "file_specific_processing", "file_hashing", "hash_matching",
+        "mismatched_signature_detection", "timeline", "timeline_analysis",
+        "geolocation", "geolocation_analysis", "keyword_indexing",
+        "keyword_searching", "automated_result_interpretation", "ai_based_content_flagging"
+    ]
+    
+    processes = {}
+    for i, key in enumerate(process_keys):
+        check_val = process_args[i*2]
+        desc_val = process_args[i*2 + 1]
+        processes[key] = {
+            "applicable": check_val,
+            "description": desc_val if check_val else ""
+        }
+    
+    # Generate outputs
+    json_output = generate_json_output(metadata, top_level, processes, GENERATOR_VERSION)
+    markdown_output = generate_markdown_output(metadata, top_level, processes, GENERATOR_VERSION)
+    
+    # Save to files
+    json_file = save_to_file(json_output, "model_card.json")
+    md_file = save_to_file(markdown_output, "README.md")
+    
+    return markdown_output, json_file, md_file
+
+
+# Build Gradio Interface
+with gr.Blocks(title="Digital Forensics Model Card Generator", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(f"""
+    # 🔬 Digital Forensics Model Card Generator
+    
+    Create standardized model cards for digital forensics AI/ML systems. Based on:
+    - Di Maio, P. (2024). Towards Open Standards for Systemic Complexity in Digital Forensics
+    - Hargreaves, C., Nelson, A., & Casey, E. (2024). An abstract model for digital forensic analysis tools
+    
+    **Version {GENERATOR_VERSION}**
+    """)
+    
+    with gr.Tabs():
+        # ===== SECTION 1: METADATA =====
+        with gr.Tab("📋 Metadata"):
+            gr.Markdown("### Model Card Metadata\nAll fields are optional unless otherwise specified.")
+            
+            with gr.Row():
+                mmcid = gr.Textbox(
+                    label="MMCID - Identifier",
+                    placeholder="DF-MC-2025-001",
+                    info="Format: DF-MC-YYYY-NNN"
+                )
+                version = gr.Textbox(
+                    label="MCV - Version",
+                    placeholder="1.0 or N/A",
+                    info="Version number or N/A"
+                )
+            
+            owner = gr.Textbox(
+                label="DF-MCO - Owner",
+                placeholder="Organization or individual name"
+            )
+            
+            use_context = gr.Dropdown(
+                choices=CV_USE_CONTEXT,
+                label="DF-MCUse - Usage Context",
+                info="How is this model card used?"
+            )
+            
+            case_statement = gr.TextArea(
+                label="DF-MC CS - Case Statement",
+                placeholder="Describe the case context...",
+                lines=3
+            )
+            
+            hypothesis = gr.TextArea(
+                label="DF-MC H - Hypothesis",
+                placeholder="State the hypothesis being tested...",
+                lines=3
+            )
+            
+            gr.Markdown("#### Select up to 3 items for each category:")
+            
+            with gr.Row():
+                with gr.Column():
+                    classification = gr.CheckboxGroup(
+                        choices=CV_CLASSIFICATION,
+                        label="DF-MC C - Classification (max 3)",
+                        info="Select up to 3 forensic domains"
+                    )
+                with gr.Column():
+                    classification_other = gr.Textbox(
+                        label="Other Classification",
+                        placeholder="Specify if not listed above"
+                    )
+            
+            with gr.Row():
+                with gr.Column():
+                    reasoning_type = gr.CheckboxGroup(
+                        choices=CV_REASONING,
+                        label="DF-MC TR - Type of Reasoning (max 3)",
+                        info="Select up to 3 reasoning types"
+                    )
+                with gr.Column():
+                    reasoning_other = gr.Textbox(
+                        label="Other Reasoning Type",
+                        placeholder="Specify if not listed above"
+                    )
+            
+            with gr.Row():
+                with gr.Column():
+                    bias = gr.CheckboxGroup(
+                        choices=CV_BIAS,
+                        label="DF-MC B - Bias (max 3)",
+                        info="Select up to 3 bias types"
+                    )
+                with gr.Column():
+                    bias_other = gr.Textbox(
+                        label="Other Bias",
+                        placeholder="Specify if not listed above"
+                    )
+            
+            with gr.Row():
+                with gr.Column():
+                    cause_of_bias = gr.CheckboxGroup(
+                        choices=CV_CAUSE_OF_BIAS,
+                        label="DF-MC CB - Cause of Bias (max 3)",
+                        info="Select up to 3 causes"
+                    )
+                with gr.Column():
+                    cause_bias_other = gr.Textbox(
+                        label="Other Cause of Bias",
+                        placeholder="Specify if not listed above"
+                    )
+            
+            error = gr.TextArea(
+                label="DF-MC E - Error",
+                placeholder="Describe errors encountered...",
+                lines=3
+            )
+            
+            with gr.Row():
+                with gr.Column():
+                    cause_of_error = gr.CheckboxGroup(
+                        choices=CV_CAUSE_OF_ERROR,
+                        label="DF-MC CE - Cause of Error (max 3)",
+                        info="Select up to 3 error causes"
+                    )
+                with gr.Column():
+                    cause_error_other = gr.Textbox(
+                        label="Other Cause of Error",
+                        placeholder="Specify if not listed above"
+                    )
+            
+            layer_n = gr.Textbox(
+                label="DF-MC Ln - Layer n",
+                placeholder="Specify layer/stage number if applicable"
+            )
+        
+        # ===== SECTION 2: TOP LEVEL (FIGURE 6) =====
+        with gr.Tab("🔝 Top Level Elements (DF MC 0)"):
+            gr.Markdown("### Figure 6 - Top Level Elements\nCheck applicable items and provide descriptions.")
+            
+            # Create checkboxes with text areas for each element
+            elements = [
+                ("type_reasoning", "Type of Reasoning"),
+                ("cause_error", "Cause of Error"),
+                ("algorithm", "Algorithm"),
+                ("inference", "Inference"),
+                ("confounder", "Confounder"),
+                ("classification", "Classification"),
+                ("evaluation", "Evaluation"),
+                ("hypothesis", "Hypothesis"),
+                ("tool", "Tool"),
+                ("bias_debiasing", "Bias/Debiasing"),
+                ("case_statement", "Case Statement"),
+                ("evidence_mc1", "Evidence MC1"),
+                ("file_type", "File Type"),
+                ("data_structure", "Data Structure"),
+                ("degree_confidence", "Degree of Confidence")
+            ]
+            
+            top_level_components = []
+            for elem_id, elem_label in elements:
+                with gr.Row():
+                    check = gr.Checkbox(label=f"✓ {elem_label}", value=False)
+                    desc = gr.TextArea(
+                        label=f"Description for {elem_label}",
+                        placeholder=f"Describe {elem_label.lower()} if applicable...",
+                        lines=2
+                    )
+                    top_level_components.extend([check, desc])
+        
+        # ===== SECTION 3: DATA & PROCESSES (FIGURE 7) =====
+        with gr.Tab("⚙️ Data & Processes (DF MC 1)"):
+            gr.Markdown("### Figure 7 - Data Types and Analytical Processes\nCheck applicable items and provide descriptions.")
+            
+            processes_list = [
+                ("event_data", "EVENT/DATA"),
+                ("parse_raw", "Parse Raw Data Contained Within the Image"),
+                ("validate", "Validate the Data Compared"),
+                ("identify_partitions", "Identify Partitions"),
+                ("process_filesystem", "Process File System"),
+                ("identify_content", "Identify Content (Carving)"),
+                ("file_type_id", "File Type Identification"),
+                ("file_specific", "File-Specific Processing"),
+                ("file_hashing", "File Hashing"),
+                ("hash_matching", "Hash Matching"),
+                ("mismatched_sig", "Mismatched Signature Detection"),
+                ("timeline", "Timeline"),
+                ("timeline_analysis", "Timeline Analysis"),
+                ("geolocation", "Geolocation"),
+                ("geolocation_analysis", "Geolocation Analysis"),
+                ("keyword_indexing", "Keyword Indexing"),
+                ("keyword_searching", "Keyword Searching"),
+                ("automated_result", "Automated Result Interpretation"),
+                ("ai_content_flag", "AI-Based Content Flagging")
+            ]
+            
+            process_components = []
+            for proc_id, proc_label in processes_list:
+                with gr.Row():
+                    check = gr.Checkbox(label=f"✓ {proc_label}", value=False)
+                    desc = gr.TextArea(
+                        label=f"Description for {proc_label}",
+                        placeholder=f"Describe {proc_label.lower()} if applicable...",
+                        lines=2
+                    )
+                    process_components.extend([check, desc])
+    
+    # ===== GENERATION & OUTPUT =====
+    gr.Markdown("---")
+    gr.Markdown("### Generate Your Model Card")
+    
+    generate_btn = gr.Button("🚀 Generate Model Card", variant="primary", size="lg")
+    
+    with gr.Tabs():
+        with gr.Tab("📄 Preview (Markdown)"):
+            preview_output = gr.Markdown(label="Markdown Preview")
+        
+        with gr.Tab("💾 Download Files"):
+            gr.Markdown("Click the download buttons below to save your generated model card files:")
+            with gr.Row():
+                json_download = gr.File(label="Download JSON", type="filepath")
+                md_download = gr.File(label="Download README.md", type="filepath")
+    
+    # Wire up the generation
+    all_inputs = [
+        mmcid, version, owner, use_context, case_statement, hypothesis,
+        classification, reasoning_type, bias, cause_of_bias, error, cause_of_error, layer_n,
+        classification_other, reasoning_other, bias_other, cause_bias_other, cause_error_other
+    ] + top_level_components + process_components
+    
+    generate_btn.click(
+        fn=generate_model_card,
+        inputs=all_inputs,
+        outputs=[preview_output, json_download, md_download]
+    )
+    
+    gr.Markdown(f"""
+    ---
+    ### About This Generator
+    
+    **References:**
+    - Di Maio, P. (2024). Towards Open Standards for Systemic Complexity in Digital Forensics. https://papers.cool/arxiv/2512.12970
+    - Hargreaves, C., Nelson, A., & Casey, E. (2024). An abstract model for digital forensic analysis tools—A foundation for systematic error mitigation analysis. *Forensic Science International: Digital Investigation*, 48.
+    
+    **Generator Version:** {GENERATOR_VERSION}  
+    **License:** Apache 2.0  
+    **Contact:** For questions or feedback, please open an issue on the project repository.
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

requirements(2).txt

@@ -0,0 +1,2 @@
+gradio
+python-dateutil