Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import random
+import time
+import requests
+import json
+import re
+import matplotlib.pyplot as plt
+import networkx as nx
+from datetime import datetime
+import gradio as gr
+import io
+from PIL import Image
+
+# -------------------------------------------------------------------
+# TemporalPredictionSystem Class Definition
+# -------------------------------------------------------------------
+class TemporalPredictionSystem:
+    def __init__(self):
+        self.nodes = []
+        self.edges = []
+        self.time_scales = [0.1, 0.5, 1, 2, 5]  # Different time scales
+        self.recursion_depth = 3
+        self.causal_strength = 5
+        self.time_scale = 3
+
+        # System metrics
+        self.prediction_accuracy = 0
+        self.causal_consistency = 0
+        self.meta_rule_optimizations = 0
+        self.fixed_point_iterations = 0
+
+        # Simulation state
+        self.is_running = False
+        self.tick_count = 0
+
+        # Constants
+        self.MAX_NODES = 50
+        self.NODE_DECAY_RATE = 0.02
+        self.EDGE_THRESHOLD = 0.3
+
+        # Markov Chain data for text predictions
+        self.word_corpus = [
+            "future", "past", "present", "time", "space", "causality", "effect", "prediction", 
+            "reality", "quantum", "probability", "divergence", "convergence", "branch", "path", 
+            "timeline", "paradox", "stability", "chaos", "deterministic", "random", "uncertain", 
+            "fixed", "flexible", "loop", "recursive", "retrocausal", "forward", "backward", "cycle",
+            "wave", "particle", "superposition", "entanglement", "observer", "collapse", "multiverse",
+            "dimension", "parallel", "simulation", "emergence", "complexity", "singularity", "event",
+            "horizon", "threshold", "attractor", "repeller", "equilibrium", "evolution", "revolution"
+        ]
+        self.quotes_data = []
+        self.tags_data = []
+        self.markov_chain = {}
+        self.timelines = []
+        self.predicted_futures = []
+
+        # Chat model properties
+        self.conversation_history = []
+        self.personality_traits = {
+            "optimism": 0.7,
+            "creativity": 0.8,
+            "analytical": 0.65,
+            "humor": 0.5,
+            "empathy": 0.6
+        }
+        self.learning_rate = 0.05
+        self.context_size = 5
+        self.knowledge_base = {}
+        self.temporal_bias = 0.3  # How much to bias toward future perspective
+        self.self_model_accuracy = 0.2  # Starts low, improves with conversation
+        self.model_growth_rate = 0
+        self.total_interactions = 0
+
+        # Response generators
+        self.response_templates = [
+            "Based on your current trajectory, I see {{future}}.",
+            "From my perspective {{timeYears}} years ahead, I can tell you that {{insight}}.",
+            "Your interest in {{topic}} eventually leads to {{outcome}}.",
+            "I remember this conversation. It {{impact}} my thinking significantly.",
+            "In the future, you'll realize that {{realization}}.",
+            "What you're doing now with {{activity}} actually {{futureResult}}.",
+            "The {{decision}} you're considering has interesting consequences: {{consequences}}.",
+            "From my vantage point, I'd suggest focusing more on {{suggestion}}.",
+            "Your intuition about {{subject}} is {{accuracy}} - I know because I've {{evidence}}."
+        ]
+
+        self.insight_patterns = [
+            "this challenge ultimately strengthens your {{attribute}}",
+            "you develop a unique perspective on {{topic}} that others find valuable",
+            "the uncertainty you feel now resolves into {{outcome}}",
+            "what seems important now shifts dramatically after you {{experience}}",
+            "this period of {{state}} leads to unexpected opportunities in {{domain}}",
+            "your approach to {{activity}} evolves in ways that surprise even you",
+            "the questions you're asking now form the foundation of your future {{role}}"
+        ]
+
+        # Topic and concept modeling
+        self.topic_extractor = {}
+        self.topic_relations = {}
+
+        # Initialize with basic topics from word corpus
+        for word in self.word_corpus:
+            self.topic_extractor[word] = 1
+            self.topic_relations[word] = []
+
+        # Fetch quotes data and build markov chain
+        self.fetch_quotes_data()
+
+    def fetch_quotes_data(self):
+        """Fetch quotes data from Hugging Face dataset API"""
+        try:
+            response = requests.get(
+                "https://datasets-server.huggingface.co/rows?dataset=Abirate%2Fenglish_quotes&config=default&split=train&offset=0&length=100"
+            )
+            data = response.json()
+
+            if data and "rows" in data:
+                for row in data["rows"]:
+                    if row and "row" in row and "quote" in row["row"]:
+                        quote = row["row"]["quote"]
+                        self.quotes_data.append(quote)
+                        words = [w for w in re.sub(r'[^\w\s]', '', quote.lower()).split() if len(w) > 3]
+                        self.word_corpus = list(set(self.word_corpus + words))
+                        if "tags" in row["row"]:
+                            tags = [row["row"]["tags"]] if not isinstance(row["row"]["tags"], list) else row["row"]["tags"]
+                            self.tags_data.extend(tags)
+                            tag_words = [tag.lower() for tag in tags if len(tag) > 3]
+                            self.word_corpus = list(set(self.word_corpus + tag_words))
+
+                print(f"Loaded {len(self.quotes_data)} quotes and {len(self.tags_data)} tags")
+                print(f"Word corpus expanded to {len(self.word_corpus)} words")
+                for word in self.word_corpus:
+                    self.topic_extractor[word] = 1
+                    self.topic_relations[word] = []
+                self.build_initial_markov_chain()
+
+        except Exception as e:
+            print(f"Error fetching quotes data: {e}")
+            self.build_initial_markov_chain()
+
+        self.initialize()
+
+    def build_initial_markov_chain(self):
+        """Build a Markov chain from quotes and corpus data"""
+        chain = {}
+        for quote in self.quotes_data:
+            words = [w for w in re.sub(r'[^\w\s]', '', quote.lower()).split() if len(w) > 3]
+            for i in range(len(words) - 1):
+                word = words[i]
+                next_word = words[i + 1]
+                if word not in chain:
+                    chain[word] = {}
+                chain[word][next_word] = chain[word].get(next_word, 0) + 1
+        if len(chain) < 30:
+            for i in range(len(self.word_corpus)):
+                word = self.word_corpus[i]
+                next_word = self.word_corpus[(i + 1) % len(self.word_corpus)]
+                if word not in chain:
+                    chain[word] = {}
+                chain[word][next_word] = chain[word].get(next_word, 0) + 1
+        for tag in self.tags_data:
+            related_words = random.sample(self.word_corpus, min(5, len(self.word_corpus)))
+            tag_word = tag.lower()
+            if len(tag_word) > 3:
+                if tag_word not in chain:
+                    chain[tag_word] = {}
+                for related_word in related_words:
+                    chain[tag_word][related_word] = chain[tag_word].get(related_word, 0) + 1
+        for i in range(50):
+            word1 = random.choice(self.word_corpus)
+            word2 = random.choice(self.word_corpus)
+            if word1 not in chain:
+                chain[word1] = {}
+            chain[word1][word2] = chain[word1].get(word2, 0) + 1
+        self.markov_chain = chain
+
+    def initialize(self):
+        """Initialize the system with nodes, edges, and timelines"""
+        self.generate_initial_nodes()
+        self.generate_initial_edges()
+        self.reset_metrics()
+        self.initialize_timelines()
+        self.generate_predictions()
+
+    def initialize_timelines(self):
+        """Initialize timelines with seed words and predictions"""
+        self.timelines = []
+        timeline_count = 3 + random.randint(0, 2)
+        for i in range(timeline_count):
+            seed_word = random.choice(self.word_corpus)
+            length = 3 + random.randint(0, 4)
+            words = [seed_word]
+            current_word = seed_word
+            for j in range(1, length):
+                current_word = self.get_next_word_from_markov(current_word)
+                words.append(current_word)
+            self.timelines.append({
+                "id": f"timeline-{i}",
+                "words": words,
+                "probability": random.random(),
+                "timeScale": random.randint(0, len(self.time_scales) - 1),
+                "stability": random.random()
+            })
+
+    def get_next_word_from_markov(self, current_word):
+        """Get next word based on Markov chain probabilities"""
+        if current_word not in self.markov_chain:
+            return random.choice(self.word_corpus)
+        next_words = self.markov_chain[current_word]
+        words = list(next_words.keys())
+        weights = list(next_words.values())
+        total_weight = sum(weights)
+        r = random.random() * total_weight
+        running_sum = 0
+        for i in range(len(words)):
+            running_sum += weights[i]
+            if r <= running_sum:
+                return words[i]
+        return random.choice(words) if words else random.choice(self.word_corpus)
+
+    def generate_predictions(self):
+        """Generate future predictions for each timeline"""
+        self.predicted_futures = []
+        for timeline in self.timelines:
+            future_count = 2 + random.randint(0, 1)
+            for i in range(future_count):
+                last_word = timeline["words"][-1]
+                future_length = 2 + random.randint(0, 2)
+                current_word = last_word
+                future_words = [current_word]
+                for j in range(1, future_length):
+                    current_word = self.get_next_word_from_markov(current_word)
+                    future_words.append(current_word)
+                self.predicted_futures.append({
+                    "id": f"future-{len(self.predicted_futures)}",
+                    "timelineId": timeline["id"],
+                    "words": future_words,
+                    "probability": random.random() * timeline["probability"],
+                    "confidence": random.random(),
+                    "timeScale": timeline["timeScale"]
+                })
+
+    def generate_initial_nodes(self):
+        """Generate initial nodes for visualization and computation"""
+        self.nodes = []
+        initial_node_count = 5 + self.recursion_depth * 2
+        for i in range(initial_node_count):
+            self.nodes.append({
+                "id": f"node-{i}",
+                "x": random.random() * 700 + 50,
+                "y": random.random() * 400 + 50,
+                "timeScale": random.randint(0, len(self.time_scales) - 1),
+                "value": random.random(),
+                "prediction": random.random(),
+                "age": 0,
+                "radius": 10 + random.random() * 15,
+                "word": random.choice(self.word_corpus),
+                "future": self.generate_future_phrase(3)
+            })
+
+    def generate_future_phrase(self, length):
+        """Generate a phrase of specified length using Markov chain"""
+        words = []
+        start_word = random.choice(self.word_corpus)
+        current_word = start_word
+        words.append(current_word)
+        for i in range(1, length):
+            current_word = self.get_next_word_from_markov(current_word)
+            words.append(current_word)
+        return " ".join(words)
+
+    def generate_initial_edges(self):
+        """Generate initial edges between nodes"""
+        self.edges = []
+        for i in range(len(self.nodes)):
+            source_node = self.nodes[i]
+            connection_count = random.randint(1, 3)
+            for j in range(connection_count):
+                target_index = i
+                while target_index == i:
+                    target_index = random.randint(0, len(self.nodes) - 1)
+                target_node = self.nodes[target_index]
+                self.edges.append({
+                    "id": f"edge-{len(self.edges)}",
+                    "source": source_node["id"],
+                    "target": target_node["id"],
+                    "strength": random.random() * self.causal_strength / 10,
+                    "age": 0
+                })
+
+    def reset_metrics(self):
+        """Reset system metrics"""
+        self.prediction_accuracy = 0
+        self.causal_consistency = 0
+        self.meta_rule_optimizations = 0
+        self.fixed_point_iterations = 0
+
+    def tick(self):
+        """Run one simulation tick"""
+        self.tick_count += 1
+        self.propagate_influences()
+        self.apply_retrocausality()
+        self.apply_fixed_point_combination()
+        if self.tick_count % 10 == 0:
+            self.optimize_meta_rules()
+        self.enforce_causal_consistency()
+        self.manage_node_lifecycle()
+        self.manage_edge_lifecycle()
+        self.update_metrics()
+        if self.tick_count % 5 == 0:
+            self.update_timelines()
+            self.generate_predictions()
+
+    def propagate_influences(self):
+        """Propagate causal influences through the network"""
+        node_values_snapshot = [{
+            "id": node["id"], 
+            "value": node["value"],
+            "prediction": node["prediction"]
+        } for node in self.nodes]
+        for node in self.nodes:
+            incoming_edges = [edge for edge in self.edges if edge["target"] == node["id"]]
+            if incoming_edges:
+                influence_sum = 0
+                weight_sum = 0
+                for edge in incoming_edges:
+                    source_node = next((n for n in node_values_snapshot if n["id"] == edge["source"]), None)
+                    if source_node:
+                        influence_sum += source_node["value"] * edge["strength"]
+                        weight_sum += edge["strength"]
+                if weight_sum > 0:
+                    scale_factor = self.time_scales[node["timeScale"]] * (self.time_scale / 3)
+                    influence_value = influence_sum / weight_sum
+                    node["value"] = (node["value"] * (1 - scale_factor * 0.1) + 
+                                     influence_value * scale_factor * 0.1 + 
+                                     (random.random() - 0.5) * 0.02)
+                    node["value"] = max(0, min(1, node["value"]))
+
+    def apply_retrocausality(self):
+        """Apply backward-in-time influence to update predictions"""
+        for node in self.nodes:
+            outgoing_edges = [edge for edge in self.edges if edge["source"] == node["id"]]
+            if outgoing_edges:
+                prediction_error = 0
+                for edge in outgoing_edges:
+                    target_node = next((n for n in self.nodes if n["id"] == edge["target"]), None)
+                    if target_node:
+                        error = abs(target_node["value"] - target_node["prediction"])
+                        prediction_error += error * edge["strength"]
+                adjustment_factor = 0.05 * (self.recursion_depth / 5)
+                node["prediction"] = node["prediction"] * (1 - adjustment_factor) + \
+                                     (node["value"] + (random.random() - 0.5) * 0.1) * adjustment_factor
+                node["prediction"] = max(0, min(1, node["prediction"]))
+
+    def apply_fixed_point_combination(self):
+        """Apply Y combinator-inspired approach to find fixed points"""
+        if self.tick_count % 5 == 0:
+            iterations = 1 + self.recursion_depth // 2
+            for i in range(iterations):
+                nodes_to_update = random.sample(
+                    self.nodes, 
+                    min(3 + self.recursion_depth // 2, len(self.nodes))
+                )
+                for node in nodes_to_update:
+                    old_value = node["value"]
+                    combined_value = (node["value"] + node["prediction"]) / 2
+                    node["value"] = old_value * 0.8 + combined_value * 0.2
+                self.fixed_point_iterations += 1
+
+    def optimize_meta_rules(self):
+        """Optimize system meta-rules based on performance"""
+        current_accuracy = self.calculate_prediction_accuracy()
+        if current_accuracy < 0.6:
+            for edge in self.edges:
+                source_node = next((n for n in self.nodes if n["id"] == edge["source"]), None)
+                target_node = next((n for n in self.nodes if n["id"] == edge["target"]), None)
+                if source_node and target_node:
+                    prediction_error = abs(source_node["prediction"] - target_node["value"])
+                    if prediction_error < 0.3:
+                        edge["strength"] = min(1, edge["strength"] * 1.05)
+                    else:
+                        edge["strength"] = max(0.1, edge["strength"] * 0.95)
+            if random.random() < 0.2 and len(self.edges) < len(self.nodes) * 3:
+                self.create_new_edge()
+            self.meta_rule_optimizations += 1
+        if random.random() < 0.1 and len(self.nodes) < self.MAX_NODES:
+            self.create_new_node()
+
+    def enforce_causal_consistency(self):
+        """Enforce logical consistency in causal relationships"""
+        causal_loops = self.detect_causal_loops()
+        if causal_loops:
+            for loop in causal_loops:
+                weakest_edge = None
+                min_strength = float('inf')
+                for i in range(len(loop) - 1):
+                    edge = next((e for e in self.edges if e["source"] == loop[i] and e["target"] == loop[i+1]), None)
+                    if edge and edge["strength"] < min_strength:
+                        weakest_edge = edge
+                        min_strength = edge["strength"]
+                if weakest_edge:
+                    weakest_edge["strength"] *= 0.9
+        for node in self.nodes:
+            incoming_edges = [edge for edge in self.edges if edge["target"] == node["id"]]
+            if len(incoming_edges) > 1:
+                contradictions = 0
+                for i in range(len(incoming_edges)):
+                    for j in range(i + 1, len(incoming_edges)):
+                        source_node_i = next((n for n in self.nodes if n["id"] == incoming_edges[i]["source"]), None)
+                        source_node_j = next((n for n in self.nodes if n["id"] == incoming_edges[j]["source"]), None)
+                        if source_node_i and source_node_j:
+                            value_difference = abs(source_node_i["value"] - source_node_j["value"])
+                            if value_difference > 0.5:
+                                contradictions += 1
+                                incoming_edges[i]["strength"] *= 0.98
+                                incoming_edges[j]["strength"] *= 0.98
+                self.causal_consistency = max(0, self.causal_consistency - contradictions * 0.01)
+
+    def manage_node_lifecycle(self):
+        """Manage node creation and removal"""
+        for node in self.nodes:
+            node["age"] += 1
+        if len(self.nodes) > self.MAX_NODES:
+            node_connections = []
+            for node in self.nodes:
+                connections = len([e for e in self.edges if e["source"] == node["id"] or e["target"] == node["id"]])
+                node_connections.append({"node": node, "connections": connections})
+            node_connections.sort(key=lambda x: (x["connections"], -x["node"]["age"]))
+            node_to_remove = node_connections[0]["node"]
+            self.nodes = [n for n in self.nodes if n["id"] != node_to_remove["id"]]
+            self.edges = [e for e in self.edges if e["source"] != node_to_remove["id"] and e["target"] != node_to_remove["id"]]
+
+    def manage_edge_lifecycle(self):
+        """Manage edge creation and removal"""
+        for edge in self.edges:
+            edge["age"] += 1
+        self.edges = [edge for edge in self.edges if edge["strength"] > self.EDGE_THRESHOLD]
+
+    def update_metrics(self):
+        """Update system performance metrics"""
+        self.prediction_accuracy = self.calculate_prediction_accuracy()
+        if self.tick_count % 20 == 0:
+            self.causal_consistency = min(1, self.causal_consistency + 0.01)
+
+    def calculate_prediction_accuracy(self):
+        """Calculate how close predictions are to actual values"""
+        if not self.nodes:
+            return 0
+        total_error = sum(abs(node["value"] - node["prediction"]) for node in self.nodes)
+        average_error = total_error / len(self.nodes)
+        return max(0, 1 - average_error)
+
+    def detect_causal_loops(self):
+        """Detect causal loops in the network using DFS"""
+        loops = []
+        visited = set()
+
+        def dfs(node_id, path):
+            if node_id in path:
+                loops.append(path[path.index(node_id):] + [node_id])
+                return
+            if node_id in visited:
+                return
+            path.append(node_id)
+            visited.add(node_id)
+            outgoing_edges = [edge for edge in self.edges if edge["source"] == node_id]
+            for edge in outgoing_edges:
+                dfs(edge["target"], path[:])
+        for node in self.nodes:
+            if node["id"] not in visited:
+                dfs(node["id"], [])
+        return loops
+
+    def create_new_node(self):
+        """Create a new node connected to existing nodes"""
+        node_id = f"node-{len(self.nodes)}"
+        x, y = 0, 0
+        if self.nodes:
+            reference_node = random.choice(self.nodes)
+            x = reference_node["x"] + (random.random() - 0.5) * 100
+            y = reference_node["y"] + (random.random() - 0.5) * 100
+            x = max(50, min(750, x))
+            y = max(50, min(450, y))
+        else:
+            x = random.random() * 700 + 50
+            y = random.random() * 400 + 50
+        word = random.choice(self.word_corpus)
+        if self.timelines:
+            random_timeline = random.choice(self.timelines)
+            word = random.choice(random_timeline["words"])
+        new_node = {
+            "id": node_id,
+            "x": x,
+            "y": y,
+            "timeScale": random.randint(0, len(self.time_scales) - 1),
+            "value": random.random(),
+            "prediction": random.random(),
+            "age": 0,
+            "radius": 10 + random.random() * 15,
+            "word": word,
+            "future": self.generate_future_phrase(3)
+        }
+        self.nodes.append(new_node)
+        connection_count = random.randint(1, 3)
+        for i in range(connection_count):
+            if len(self.nodes) > 1:
+                target_index = 0
+                while True:
+                    target_index = random.randint(0, len(self.nodes) - 1)
+                    if self.nodes[target_index]["id"] != new_node["id"]:
+                        break
+                self.edges.append({
+                    "id": f"edge-{len(self.edges)}",
+                    "source": new_node["id"],
+                    "target": self.nodes[target_index]["id"],
+                    "strength": random.random() * 0.5 + 0.3,
+                    "age": 0
+                })
+
+    def create_new_edge(self):
+        """Create a new edge between existing nodes that aren't already connected"""
+        if len(self.nodes) < 2:
+            return
+        source_index = random.randint(0, len(self.nodes) - 1)
+        target_index = source_index
+        while target_index == source_index:
+            target_index = random.randint(0, len(self.nodes) - 1)
+        source_id = self.nodes[source_index]["id"]
+        target_id = self.nodes[target_index]["id"]
+        edge_exists = any(edge["source"] == source_id and edge["target"] == target_id for edge in self.edges)
+        if not edge_exists:
+            self.edges.append({
+                "id": f"edge-{len(self.edges)}",
+                "source": source_id,
+                "target": target_id,
+                "strength": random.random() * 0.5 + 0.3,
+                "age": 0
+            })
+
+    def update_timelines(self):
+        """Update existing timelines and occasionally create new ones"""
+        for timeline in self.timelines:
+            if random.random() < 0.3:
+                last_word = timeline["words"][-1]
+                next_word = self.get_next_word_from_markov(last_word)
+                timeline["words"].append(next_word)
+                if len(timeline["words"]) > 10:
+                    timeline["words"].pop(0)
+            related_nodes = [node for node in self.nodes 
+                             if node["word"] in timeline["words"] or 
+                             timeline["words"][-1] in node["future"]]
+            if related_nodes:
+                avg_prediction_accuracy = sum(abs(node["value"] - node["prediction"]) for node in related_nodes) / len(related_nodes)
+                timeline["probability"] = max(0.1, 1 - avg_prediction_accuracy)
+                timeline["stability"] = max(0.1, timeline["stability"] * 0.95 + self.causal_consistency * 0.05)
+        if random.random() < 0.2 and len(self.timelines) < 8:
+            if self.timelines:
+                source_timeline = random.choice(self.timelines)
+                branch_point = random.randint(0, len(source_timeline["words"]) - 1)
+                new_words = source_timeline["words"][:branch_point + 1]
+                current_word = new_words[-1]
+                additional_words = 2 + random.randint(0, 2)
+                for i in range(additional_words):
+                    current_word = self.get_next_word_from_markov(current_word)
+                    new_words.append(current_word)
+                self.timelines.append({
+                    "id": f"timeline-{len(self.timelines)}",
+                    "words": new_words,
+                    "probability": source_timeline["probability"] * 0.8,
+                    "timeScale": source_timeline["timeScale"],
+                    "stability": source_timeline["stability"] * 0.8
+                })
+        self.timelines = [timeline for timeline in self.timelines 
+                          if timeline["stability"] > 0.2 or random.random() < 0.7]
+
+    def process_user_message(self, message):
+        """Process user message and generate a response"""
+        self.conversation_history.append({
+            "role": "user",
+            "content": message,
+            "timestamp": datetime.now().timestamp()
+        })
+        self.total_interactions += 1
+        extracted_topics = self.extract_topics(message)
+        self.update_knowledge_base(extracted_topics, message)
+        for _ in range(5):
+            self.tick()
+        response = self.generate_future_response(message, extracted_topics)
+        self.conversation_history.append({
+            "role": "future",
+            "content": response["text"],
+            "timestamp": datetime.now().timestamp(),
+            "timeOffset": response["timeOffset"],
+            "confidence": response["confidence"]
+        })
+        self.update_model_growth()
+        return response
+
+    def extract_topics(self, message):
+        """Extract topics from message"""
+        words = [w for w in re.sub(r'[^\w\s]', '', message.lower()).split() if len(w) > 2]
+        extracted_topics = []
+        for word in words:
+            if word in self.topic_extractor:
+                extracted_topics.append(word)
+                self.topic_extractor[word] += 1
+        for word in words:
+            if word not in self.topic_extractor and len(word) > 3:
+                self.topic_extractor[word] = 1
+                extracted_topics.append(word)
+                for topic in extracted_topics:
+                    if topic != word:
+                        if word not in self.topic_relations:
+                            self.topic_relations[word] = []
+                        self.topic_relations[word].append(topic)
+                        if topic not in self.topic_relations:
+                            self.topic_relations[topic] = []
+                        self.topic_relations[topic].append(word)
+        for word in words:
+            if len(word) > 3 and word not in self.word_corpus:
+                self.word_corpus.append(word)
+                random_existing_words = random.sample(self.word_corpus, min(3, len(self.word_corpus)))
+                for existing_word in random_existing_words:
+                    if existing_word not in self.markov_chain:
+                        self.markov_chain[existing_word] = {}
+                    self.markov_chain[existing_word][word] = self.markov_chain[existing_word].get(word, 0) + 1
+                    if word not in self.markov_chain:
+                        self.markov_chain[word] = {}
+                    self.markov_chain[word][existing_word] = self.markov_chain[word].get(existing_word, 0) + 1
+        return extracted_topics
+
+    def update_knowledge_base(self, topics, message):
+        """Update knowledge base with topics and message"""
+        for topic in topics:
+            if topic not in self.knowledge_base:
+                self.knowledge_base[topic] = []
+            self.knowledge_base[topic].append(message)
+            if len(self.knowledge_base[topic]) > 10:
+                self.knowledge_base[topic].pop(0)
+
+    def generate_future_response(self, message, topics):
+        """Generate a response from the future perspective"""
+        time_offset = self.determine_time_offset()
+        confidence = 0.3 + (self.self_model_accuracy * 0.5) + (self.model_growth_rate * 0.2)
+        relevant_timeline = self.select_relevant_timeline(topics)
+        response_text = self.construct_response(message, topics, time_offset, relevant_timeline)
+        return {
+            "text": response_text,
+            "timeOffset": time_offset,
+            "confidence": min(0.95, confidence)
+        }
+
+    def determine_time_offset(self):
+        """Determine how far in the future the response comes from"""
+        base_years = 1 + int(self.self_model_accuracy * 10)
+        random_factor = random.random() * 2 - 1
+        years = max(1, round(base_years + random_factor))
+        return years
+
+    def select_relevant_timeline(self, topics):
+        """Select the most relevant timeline based on topics"""
+        if not self.timelines:
+            return None
+        best_match = None
+        best_match_score = -1
+        for timeline in self.timelines:
+            score = 0
+            for word in timeline["words"]:
+                if word in topics:
+                    score += 2
+                for topic in topics:
+                    if topic in self.topic_relations and word in self.topic_relations[topic]:
+                        score += 1
+            score *= timeline["probability"]
+            if score > best_match_score:
+                best_match_score = score
+                best_match = timeline
+        if best_match_score == 0:
+            sorted_timelines = sorted(self.timelines, key=lambda t: t["probability"], reverse=True)
+            best_match = sorted_timelines[random.randint(0, min(2, len(sorted_timelines) - 1))]
+        return best_match
+
+    def construct_response(self, message, topics, time_offset, timeline):
+        """Construct response using templates and filling in variables"""
+        template_idx = random.randint(0, len(self.response_templates) - 1)
+        template = self.response_templates[template_idx]
+        if "{{future}}" in template:
+            future_scenario = self.generate_future_scenario(topics, timeline)
+            template = template.replace("{{future}}", future_scenario)
+        if "{{timeYears}}" in template:
+            template = template.replace("{{timeYears}}", str(time_offset))
+        if "{{insight}}" in template:
+            insight = self.generate_insight(topics)
+            template = template.replace("{{insight}}", insight)
+        if "{{topic}}" in template and topics:
+            topic = random.choice(topics)
+            template = template.replace("{{topic}}", topic)
+        if "{{outcome}}" in template:
+            outcome = self.generate_outcome(topics, timeline)
+            template = template.replace("{{outcome}}", outcome)
+        if "{{impact}}" in template:
+            impacts = ["changed", "transformed", "refined", "confirmed", "challenged"]
+            impact = random.choice(impacts)
+            template = template.replace("{{impact}}", impact)
+        if "{{realization}}" in template:
+            realization = self.generate_realization(topics)
+            template = template.replace("{{realization}}", realization)
+        if "{{activity}}" in template and len(message) > 10:
+            words = message.split()
+            activity_idx = random.randint(0, len(words) - 1)
+            end_idx = min(activity_idx + 2, len(words))
+            activity = " ".join(words[activity_idx:end_idx])
+            template = template.replace("{{activity}}", activity)
+        if "{{futureResult}}" in template:
+            results = [
+                "becomes a core part of your expertise",
+                "evolves into something quite different",
+                "connects to unexpected opportunities",
+                "proves more valuable than you currently realize",
+                "becomes a foundation for your future work"
+            ]
+            result = random.choice(results)
+            template = template.replace("{{futureResult}}", result)
+        if "{{decision}}" in template:
+            decisions = ["path", "choice", "approach", "perspective", "commitment", "idea"]
+            decision = random.choice(decisions)
+            template = template.replace("{{decision}}", decision)
+        if "{{consequences}}" in template:
+            consequences = self.generate_consequences(topics, timeline)
+            template = template.replace("{{consequences}}", consequences)
+        if "{{suggestion}}" in template:
+            suggestion = self.generate_suggestion(topics)
+            template = template.replace("{{suggestion}}", suggestion)
+        if "{{subject}}" in template and topics:
+            subject = random.choice(topics)
+            template = template.replace("{{subject}}", subject)
+        if "{{accuracy}}" in template:
+            accuracies = [
+                "surprisingly accurate", "partially correct", 
+                "insightful but incomplete", 
+                "headed in the right direction", "more significant than you realize"
+            ]
+            accuracy = random.choice(accuracies)
+            template = template.replace("{{accuracy}}", accuracy)
+        if "{{evidence}}" in template:
+            evidences = [
+                "explored it further", "seen the results", "lived through it",
+                "connected those dots", "studied this extensively", "experienced it firsthand"
+            ]
+            evidence = random.choice(evidences)
+            template = template.replace("{{evidence}}", evidence)
+        template = re.sub(r'\{\{[^}]+\}\}', "interesting", template)
+        return template
+
+    def generate_future_scenario(self, topics, timeline):
+        """Generate a future scenario based on topics and timeline"""
+        scenario = ""
+        if timeline and timeline["words"]:
+            scenario = " leads to ".join(timeline["words"])
+            predictions = [p for p in self.predicted_futures if p["timelineId"] == timeline["id"]]
+            if predictions:
+                prediction = random.choice(predictions)
+                scenario += ", ultimately resulting in " + " ".join(prediction["words"])
+        else:
+            if topics:
+                base_topic = random.choice(topics)
+                current = base_topic
+                scenario_words = [current]
+                for i in range(4):
+                    current = self.get_next_word_from_markov(current)
+                    scenario_words.append(current)
+                scenario = " ".join(scenario_words)
+            else:
+                scenario = self.generate_future_phrase(5)
+        return scenario
+
+    def generate_insight(self, topics):
+        """Generate an insight based on patterns and topics"""
+        pattern_idx = random.randint(0, len(self.insight_patterns) - 1)
+        pattern = self.insight_patterns[pattern_idx]
+        if "{{attribute}}" in pattern:
+            attributes = [
+                "resilience", "creativity", "focus", "adaptability", 
+                "intuition", "strategic thinking", "empathy"
+            ]
+            attribute = random.choice(attributes)
+            pattern = pattern.replace("{{attribute}}", attribute)
+        if "{{topic}}" in pattern and topics:
+            topic = random.choice(topics)
+            pattern = pattern.replace("{{topic}}", topic)
+        if "{{outcome}}" in pattern:
+            outcomes = [
+                "a clear sense of purpose", "unexpected confidence", 
+                "a unique perspective", "valuable expertise", 
+                "a new direction"
+            ]
+            outcome = random.choice(outcomes)
+            pattern = pattern.replace("{{outcome}}", outcome)
+        if "{{experience}}" in pattern:
+            experiences = [
+                "encounter a key mentor", "discover a new passion", 
+                "solve a challenging problem", "make a brave decision", 
+                "connect seemingly unrelated ideas"
+            ]
+            experience = random.choice(experiences)
+            pattern = pattern.replace("{{experience}}", experience)
+        if "{{state}}" in pattern:
+            states = [
+                "exploration", "uncertainty", "learning", "questioning", 
+                "experimenting", "reflection"
+            ]
+            state = random.choice(states)
+            pattern = pattern.replace("{{state}}", state)
+        if "{{domain}}" in pattern:
+            domains = [
+                "technology", "creativity", "relationships", "career", 
+                "personal growth", "community", "leadership"
+            ]
+            domain = random.choice(domains)
+            pattern = pattern.replace("{{domain}}", domain)
+        if "{{activity}}" in pattern and topics:
+            topic = random.choice(topics)
+            pattern = pattern.replace("{{activity}}", "understanding " + topic)
+        if "{{role}}" in pattern:
+            roles = [
+                "expertise", "perspective", "approach", "contribution", 
+                "methodology", "philosophy", "framework"
+            ]
+            role = random.choice(roles)
+            pattern = pattern.replace("{{role}}", role)
+        pattern = re.sub(r'\{\{[^}]+\}\}', "development", pattern)
+        return pattern
+
+    def generate_outcome(self, topics, timeline):
+        """Generate an outcome based on topics and timeline"""
+        if timeline and timeline["words"]:
+            outcome_base = " ".join(timeline["words"][-2:])
+            return outcome_base + " becoming significant to you"
+        if topics:
+            seed = random.choice(topics)
+            current = seed
+            outcome_words = [current]
+            for i in range(3):
+                current = self.get_next_word_from_markov(current)
+                outcome_words.append(current)
+            return " ".join(outcome_words)
+        generic_outcomes = [
+            "a deeper understanding of what truly matters to you",
+            "an unexpected opportunity that aligns with your values",
+            "a more nuanced perspective that serves you well",
+            "connecting seemingly unrelated areas of interest",
+            "developing a unique approach that becomes your signature"
+        ]
+        return random.choice(generic_outcomes)
+
+    def generate_realization(self, topics):
+        """Generate a realization insight"""
+        realizations = [
+            "the obstacles you're facing now are developing crucial skills",
+            "your unique combination of interests creates your edge",
+            "what seems like a detour is actually a direct path",
+            "the questions you're asking matter more than immediate answers",
+            "your intuitive approaches often outperform conventional wisdom",
+            "consistency in small actions leads to your biggest breakthroughs",
+            "your current doubts become the foundation of future confidence"
+        ]
+        realization = random.choice(realizations)
+        if topics and random.random() > 0.5:
+            topic = random.choice(topics)
+            realization += " particularly regarding " + topic
+        return realization
+
+    def generate_consequences(self, topics, timeline):
+        """Generate potential consequences"""
+        base_consequence = ""
+        if timeline and timeline["words"]:
+            base_consequence = "leading to " + " ".join(timeline["words"][-2:])
+        elif topics:
+            topic = random.choice(topics)
+            base_consequence = "connecting to " + topic + " in unexpected ways"
+        else:
+            consequences = [
+                "opening new perspectives", 
+                "creating valuable connections",
+                "developing specialized knowledge",
+                "revealing hidden opportunities",
+                "challenging key assumptions"
+            ]
+            base_consequence = random.choice(consequences)
+        temporal_phrases = [
+            "sooner than you expect",
+            "with effects that cascade over time",
+            "creating a foundation for future decisions",
+            "with initial challenges but long-term benefits",
+            "in ways that aren't immediately obvious"
+        ]
+        temporal_phrase = random.choice(temporal_phrases)
+        return base_consequence + " " + temporal_phrase
+
+    def generate_suggestion(self, topics):
+        """Generate a suggestion based on topics"""
+        suggestions = [
+            "connecting seemingly unrelated ideas and experiences",
+            "documenting your thought process, not just outcomes",
+            "pursuing quality of understanding over quantity of information",
+            "allowing more time for reflection between actions",
+            "articulating your unique perspective more boldly",
+            "recognizing patterns across different domains of interest",
+            "exploring the boundaries where different fields intersect"
+        ]
+        suggestion = random.choice(suggestions)
+        if topics and random.random() > 0.5:
+            topic = random.choice(topics)
+            suggestion = topic + " and " + suggestion
+        return suggestion
+
+    def update_model_growth(self):
+        """Update model growth based on interactions"""
+        base_growth = 0.005
+        topic_diversity = min(1, len(self.topic_extractor) / 100)
+        new_growth = base_growth * (1 + topic_diversity)
+        self.model_growth_rate = self.model_growth_rate * 0.95 + new_growth * 0.05
+        self.self_model_accuracy = min(0.9, self.self_model_accuracy + self.model_growth_rate)
+
+    def get_model_stats(self):
+        """Get current model statistics"""
+        return {
+            "knowledgeSize": len(self.knowledge_base),
+            "vocabularySize": len(self.word_corpus),
+            "selfModelAccuracy": round(self.self_model_accuracy * 100),
+            "growthRate": round(self.model_growth_rate * 10000) / 100,
+            "totalInteractions": self.total_interactions,
+            "contextualAwareness": min(100, round(len(self.topic_relations) / 10)),
+            "learningProgress": min(100, round(self.self_model_accuracy * 100))
+        }
+
+    def set_recursion_depth(self, depth):
+        self.recursion_depth = depth
+
+    def set_time_scale(self, scale):
+        self.time_scale = scale
+
+    def set_causal_strength(self, strength):
+        self.causal_strength = strength
+
+    def run_simulation_steps(self, steps=10):
+        for _ in range(steps):
+            self.tick()
+
+    def visualize_network(self, figsize=(12, 8)):
+        """Visualize the causal network using networkx"""
+        G = nx.DiGraph()
+        for node in self.nodes:
+            G.add_node(node["id"], 
+                       word=node["word"], 
+                       timeScale=node["timeScale"],
+                       value=node["value"],
+                       prediction=node["prediction"])
+        for edge in self.edges:
+            G.add_edge(edge["source"], edge["target"], 
+                       weight=edge["strength"],
+                       age=edge["age"])
+        plt.figure(figsize=figsize)
+        pos = nx.spring_layout(G, seed=42)
+        time_scale_colors = ['#42c5f4', '#4286f4', '#4242f5', '#8442f5', '#f542f5']
+        node_colors = [time_scale_colors[G.nodes[n]['timeScale']] for n in G.nodes]
+        node_sizes = [100 + 500 * G.nodes[n]['prediction'] for n in G.nodes]
+        nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=node_sizes, alpha=0.8)
+        edges = G.edges()
+        edge_weights = [G[u][v]['weight'] * 2 for u, v in edges]
+        nx.draw_networkx_edges(G, pos, edgelist=edges, width=edge_weights, 
+                              edge_color='#8888ff', alpha=0.6, arrows=True, arrowsize=10)
+        nx.draw_networkx_labels(G, pos, labels={n: G.nodes[n]['word'] for n in G.nodes}, 
+                               font_size=10, font_color='white')
+        plt.title('Temporal Prediction Network')
+        plt.axis('off')
+        plt.tight_layout()
+        return plt
+
+# -------------------------------------------------------------------
+# Global System Initialization
+# -------------------------------------------------------------------
+system = TemporalPredictionSystem()
+time.sleep(2)
+system.run_simulation_steps(20)
+print("Future Self Conversation System initialized and pre-trained.")
+
+# -------------------------------------------------------------------
+# Gradio Interface Functions
+# -------------------------------------------------------------------
+def chat_fn(user_message, history):
+    if history is None:
+        history = []
+    response = system.process_user_message(user_message)
+    bot_message = f"{response['text']} ({response['timeOffset']} years in future | Confidence: {int(response['confidence']*100)}%)"
+    history.append((user_message, bot_message))
+    system.run_simulation_steps(5)
+    return history
+
+def update_network_visualization():
+    plt_obj = system.visualize_network()
+    fig = plt.gcf()
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+    img = Image.open(buf)
+    plt.close(fig)
+    return img
+
+# -------------------------------------------------------------------
+# Gradio App Layout
+# -------------------------------------------------------------------
+with gr.Blocks(title="Future Self Conversation System") as demo:
+    gr.Markdown("# Future Self Conversation System")
+    gr.Markdown("Converse with a simulation of your future self, powered by a neural temporal prediction model.")
+    with gr.Tabs():
+        with gr.TabItem("Chat"):
+            chatbot = gr.Chatbot(label="Conversation")
+            txt = gr.Textbox(placeholder="Type your message here...", label="Your Message")
+            send_btn = gr.Button("Send")
+            send_btn.click(fn=chat_fn, inputs=[txt, chatbot], outputs=chatbot).then(lambda: "", None, txt)
+        with gr.TabItem("Network Visualization"):
+            viz_btn = gr.Button("Update Network Visualization")
+            image_out = gr.Image(label="Neural Network Visualization")
+            viz_btn.click(fn=update_network_visualization, inputs=[], outputs=image_out)
+
+# -------------------------------------------------------------------
+# Launch the App
+# -------------------------------------------------------------------
+if __name__ == "__main__":
+    demo.launch()