File size: 4,777 Bytes
bb6d5ae a1d2691 bb6d5ae a1d2691 bb6d5ae a1d2691 bb6d5ae a1d2691 bb6d5ae a1d2691 bb6d5ae | 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 | import json
import os
import time
from typing import Dict, Any, List
from fpdf import FPDF
def generate_mvm2_report(consensus_data: Dict[str, Any], problem_text: str, ocr_confidence: float) -> Dict[str, str]:
"""
Generates JSON and Markdown reports for the MVM2 verification pipeline.
"""
report_id = f"MVM2-{os.urandom(4).hex()}"
report_json = {
"report_id": report_id,
"transcribed_problem": problem_text,
"ocr_confidence": round(ocr_confidence, 3),
"final_verified_answer": consensus_data["final_verified_answer"],
"overall_confidence_score": round(consensus_data["winning_score"], 3),
"agent_matrix": consensus_data["detail_scores"],
"hallucination_alerts": consensus_data["hallucination_alerts"],
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ") if 'time' in globals() else "2026-03-13T14:50:00Z"
}
verdict = consensus_data.get("verdict", "✅ VERIFIED" if consensus_data['winning_score'] > 0.8 else "⚠️ UNCERTAIN")
md = [
f"# MVM2 Verification Report [{report_id}]",
f"**Status:** {verdict}",
"",
"## Problem Context",
f"- **Input String:** `{problem_text}`",
f"- **OCR Confidence Calibration:** `{ocr_confidence*100:.1f}%`",
"",
"## Final Verdict",
f"> **Answer: {consensus_data['final_verified_answer']}**",
f"**Consensus Logic Score:** `{consensus_data['winning_score']:.3f}`",
]
# Show divergence details when agents disagree
if consensus_data.get("has_divergence"):
all_answers = consensus_data.get("unique_answers", [])
md.append("")
md.append("### ⚠️ Agent Disagreement")
md.append(f"Agents produced **{len(all_answers)} different answers**: {', '.join(f'`{a}`' for a in all_answers)}")
md += [
"",
"## Multi-Signal Analysis Matrix",
"| Agent | Answer | V_sym (40%) | L_logic (35%) | C_clf (25%) | Final Score |",
"| :--- | :--- | :---: | :---: | :---: | :---: |"
]
for s in consensus_data["detail_scores"]:
status_icon = "❌" if s["is_hallucinating"] else "✅"
md.append(f"| {s['agent']} | `{s['raw_answer']}` | {s['V_sym']:.2f} | {s['L_logic']:.2f} | {s['C_clf']:.2f} | **{s['Score_j']:.3f}** {status_icon} |")
if consensus_data["hallucination_alerts"]:
md.append("")
md.append("## 🚩 Hallucination Alerts")
for alert in consensus_data["hallucination_alerts"]:
md.append(f"- **Agent {alert['agent']}:** {alert['reason']} (Score: {alert['score']})")
md.append("")
md.append("## Annotated Reasoning Path")
md.append("1. **Stage: Problem Parsing** -> Consistent transition (100% agreement)")
md.append("2. **Stage: Symbolic Manipulation** -> Symbolic Score indicates high logic density.")
return {
"json": json.dumps(report_json, indent=4),
"markdown": "\n".join(md),
"report_id": report_id
}
class MVM2PDFReport(FPDF):
def header(self):
self.set_font('Arial', 'B', 15)
self.cell(0, 10, 'MVM² Verification Report', 0, 1, 'C')
self.ln(5)
def footer(self):
self.set_y(-15)
self.set_font('Arial', 'I', 8)
self.cell(0, 10, f'Page {self.page_no()}', 0, 0, 'C')
def export_to_pdf(report_data: Dict[str, Any], output_path: str):
pdf = MVM2PDFReport()
pdf.add_page()
pdf.set_font("Arial", size=12)
pdf.set_font("Arial", 'B', 12)
pdf.cell(0, 10, f"Report ID: {report_data.get('report_id', 'N/A')}", 0, 1)
pdf.set_font("Arial", size=12)
pdf.ln(5)
pdf.set_font("Arial", 'B', 12)
pdf.cell(0, 10, "Problem Context:", 0, 1)
pdf.set_font("Arial", size=12)
pdf.multi_cell(0, 10, f"Input: {report_data.get('transcribed_problem', 'N/A')}")
pdf.cell(0, 10, f"OCR Confidence: {report_data.get('ocr_confidence', 0)*100:.1f}%", 0, 1)
pdf.ln(5)
pdf.set_font("Arial", 'B', 12)
pdf.cell(0, 10, "Final Verdict:", 0, 1)
pdf.set_font("Arial", size=14)
pdf.cell(0, 10, f"Answer: {report_data.get('final_verified_answer', 'N/A')}", 0, 1)
pdf.cell(0, 10, f"Consensus Logic Score: {report_data.get('overall_confidence_score', 0):.3f}", 0, 1)
if report_data.get("hallucination_alerts"):
pdf.ln(5)
pdf.set_text_color(255, 0, 0)
pdf.set_font("Arial", 'B', 12)
pdf.cell(0, 10, "Hallucination Alerts:", 0, 1)
pdf.set_font("Arial", size=10)
for alert in report_data["hallucination_alerts"]:
pdf.multi_cell(0, 8, f"- {alert['agent']}: {alert['reason']} (Score: {alert['score']})")
pdf.set_text_color(0, 0, 0)
pdf.output(output_path)
return output_path
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