import pandas as pd
from datetime import datetime
import hashlib
import os

class MemoryEngine:
    def __init__(self,
                 memory_path="SegmentMemory.csv",
                 short_term_path="ShortTermMemory.csv",
                 history_path="SegmentHistory.csv",
                 conflict_path="SegmentConflictMap.csv"):
        self.memory_path = memory_path
        self.short_term_path = short_term_path
        self.history_path = history_path
        self.conflict_path = conflict_path

        self.memory_df = self._load_csv(memory_path)
        self.short_term_df = self._load_csv(short_term_path)
        self.history_df = self._load_csv(history_path)
        self.conflict_df = self._load_csv(conflict_path)

    def _load_csv(self, path):
        return pd.read_csv(path) if os.path.exists(path) else pd.DataFrame()

    def _generate_segment_id(self):
        existing_ids = self.memory_df['SegmentID'].astype(int) if not self.memory_df.empty else []
        return str(max(existing_ids) + 1 if len(existing_ids) > 0 else 1).zfill(4)

    def _hash_text(self, text):
        return hashlib.sha256(text.encode()).hexdigest()[:12]

    def insert_segment(self, raw_text, concepts, terms, structure, datapoints, comparisons, applications, agent_id="system"):
        segment_id = self._generate_segment_id()
        hash_val = self._hash_text(raw_text)

        # Check for duplicates
        if hash_val in self.memory_df.get("HashChecksum", []):
            print("Duplicate detected. Skipping.")
            return

        # Trust & entropy logic
        trust_score = 0.75 if "error" not in raw_text.lower() else -0.4
        entropy = 0.5
        relevance = 0.6

        # Conflict detection
        conflict_rows = self.memory_df[self.memory_df["RawText"].str.contains(raw_text.split(" ")[0], case=False, na=False)]
        if not conflict_rows.empty:
            for _, row in conflict_rows.iterrows():
                self._add_conflict(row["SegmentID"], segment_id, agent_id)
            return

        # Accept segment
        new_row = {
            "SegmentID": segment_id,
            "RawText": raw_text,
            "Concepts": concepts,
            "Terms": terms,
            "Structure": structure,
            "DataPoints": datapoints,
            "Comparisons": comparisons,
            "Applications": applications,
            "Links": "",
            "Entropy": entropy,
            "TrustScore": trust_score,
            "RelevanceScore": relevance,
            "RecallCount": 0,
            "LastUsed": "",
            "ConflictsWith": "",
            "Verified": trust_score > 0.5,
            "HashChecksum": hash_val
        }

        self.memory_df = pd.concat([self.memory_df, pd.DataFrame([new_row])], ignore_index=True)
        self.memory_df.to_csv(self.memory_path, index=False)

        self._log_history(agent_id, segment_id, "insert", trust_score, trust_score, relevance, relevance, entropy, entropy, "manual_insert", False)
        self._add_to_short_term(agent_id, segment_id, raw_text, relevance, trust_score)

        print(f"Segment {segment_id} inserted.")

    def _log_history(self, agent_id, seg_id, action, c_before, c_after, r_before, r_after, e_before, e_after, context, conflict):
        log_entry = {
            "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "AgentID": agent_id,
            "SegmentID": seg_id,
            "ActionType": action,
            "ConfidenceBefore": c_before,
            "ConfidenceAfter": c_after,
            "RelevanceBefore": r_before,
            "RelevanceAfter": r_after,
            "EntropyBefore": e_before,
            "EntropyAfter": e_after,
            "UsedInContext": context,
            "ConflictTriggered": conflict,
            "Notes": ""
        }
        self.history_df = pd.concat([self.history_df, pd.DataFrame([log_entry])], ignore_index=True)
        self.history_df.to_csv(self.history_path, index=False)

    def _add_conflict(self, seg_a, seg_b, agent_id):
        entry = {
            "SegmentA": seg_a,
            "SegmentB": seg_b,
            "ConflictType": "Concept Overlap",
            "ConflictScore": 0.8,
            "ResolutionStrategy": "Manual Review",
            "WinningSegment": "",
            "LastChecked": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "ResolutionStatus": "Pending Review",
            "AgentTrigger": agent_id,
            "Notes": "Auto conflict detection"
        }
        self.conflict_df = pd.concat([self.conflict_df, pd.DataFrame([entry])], ignore_index=True)
        self.conflict_df.to_csv(self.conflict_path, index=False)
        print(f"Conflict detected between {seg_a} and {seg_b}. Routed to conflict map.")

    def _add_to_short_term(self, agent_id, seg_id, raw_text, relevance, confidence):
        row = {
            "AgentID": agent_id,
            "SegmentID": seg_id,
            "RawText": raw_text,
            "Priority": relevance,
            "ActivationTime": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "ExpiresAt": "",
            "DecayRate": 0.03,
            "RecencyScore": 1.0,
            "FocusLock": False,
            "UsageContext": "manual_insert",
            "ConfidenceScore": confidence,
            "LastInteraction": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }
        self.short_term_df = pd.concat([self.short_term_df, pd.DataFrame([row])], ignore_index=True)
        self.short_term_df.to_csv(self.short_term_path, index=False)

# Example usage
if __name__ == "__main__":
    brain = MemoryEngine()
    brain.insert_segment(
        "Hydrogen is the first element on the periodic table.",
        "hydrogen, atomic number 1",
        "hydrogen, element, periodic",
        "Chemistry > Elements",
        "Atomic number: 1",
        "Compared with helium",
        "Used in gas balloons"
    )