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 # Offset for continuous quote expansion self.quote_offset = 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 HuggingFace dataset API with dynamic offset for continuous expansion.""" try: url = f"https://datasets-server.huggingface.co/rows?dataset=Abirate%2Fenglish_quotes&config=default&split=train&offset={self.quote_offset}&length=100" response = requests.get(url) 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) # Process quote into words for corpus 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)) # Extract tags if available if "tags" in row["row"]: tags = row["row"]["tags"] if 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") # Update topic extractor with any new words for word in self.word_corpus: if word not in self.topic_extractor: self.topic_extractor[word] = 1 self.topic_relations[word] = [] # Rebuild Markov chain with new data self.build_initial_markov_chain() # Increment offset for the next fetch self.quote_offset += 100 except Exception as e: print(f"Error fetching quotes data: {e}") self.build_initial_markov_chain() 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({"role": "user", "content": user_message}) history.append({"role": "assistant", "content": 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 def pre_train_fn(progress=gr.Progress()): steps = 20 for i in range(steps): system.run_simulation_steps(1) progress((i+1)/steps) yield f"

Pre-training in progress: {(i+1)/steps*100:.0f}% complete

", (i+1)/steps*100, gr.update(visible=False) time.sleep(0.2) yield "

Pre-training complete!

", 100, gr.update(visible=True) # ------------------------------------------------------------------- # Gradio App Layout (with custom CSS for mobile responsiveness) # ------------------------------------------------------------------- with gr.Blocks(title="Future Self Conversation System") as demo: # Custom CSS for chat height and mobile responsiveness gr.HTML(""" """) # Pre-training progress area (shown on page load) pretrain_status = gr.HTML("

Initializing pre-training...

") progress_bar = gr.Slider(minimum=0, maximum=100, value=0, interactive=False, label="Pre-training Progress") # Main interface (hidden until pre-training completes) main_interface = gr.Column(visible=False) with main_interface: with gr.Row(): with gr.Column(): # Use type="messages" to get openai-style dictionaries. chatbot = gr.Chatbot(label="Conversation", elem_id="chatbot", type="messages") txt = gr.Textbox(placeholder="Type your message here...", label="Your Message") send_btn = gr.Button("Send") with gr.Column(): network_image = gr.Image(label="Neural Network Visualization") # A hidden refresh button used to update network visualization automatically. refresh_btn = gr.Button("Refresh Network", visible=False, elem_id="refresh_network") refresh_btn.click(fn=update_network_visualization, inputs=[], outputs=network_image) # Inject custom JS to click the hidden refresh button every 5 seconds. gr.HTML(""" """) send_btn.click(fn=chat_fn, inputs=[txt, chatbot], outputs=chatbot).then(lambda: "", None, txt) # On page load, run pre-training and then reveal the main interface. demo.load(fn=pre_train_fn, outputs=[pretrain_status, progress_bar, main_interface]) # ------------------------------------------------------------------- # Launch the App # ------------------------------------------------------------------- if __name__ == "__main__": demo.launch()