| import os |
| import base64 |
| import requests |
| import gradio as gr |
| from huggingface_hub import InferenceClient |
| from dataclasses import dataclass |
| import pytesseract |
| from PIL import Image |
| from sentence_transformers import SentenceTransformer, util |
| import torch |
| import numpy as np |
| import networkx as nx |
| from collections import Counter |
| import json |
| from datetime import datetime |
|
|
| @dataclass |
| class ChatMessage: |
| role: str |
| content: str |
|
|
| def to_dict(self): |
| return {"role": self.role, "content": self.content} |
|
|
| class XylariaChat: |
| def __init__(self): |
| self.hf_token = os.getenv("HF_TOKEN") |
| if not self.hf_token: |
| raise ValueError("HuggingFace token not found in environment variables") |
|
|
| self.client = InferenceClient( |
| model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", |
| token=self.hf_token |
| ) |
|
|
| self.image_api_url = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-large" |
| self.image_api_headers = {"Authorization": f"Bearer {self.hf_token}"} |
| |
| self.image_gen_api_url = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell" |
|
|
| self.conversation_history = [] |
| self.persistent_memory = [] |
| self.memory_embeddings = None |
| self.embedding_model = SentenceTransformer('all-mpnet-base-v2') |
|
|
| self.knowledge_graph = nx.DiGraph() |
| self.belief_system = {} |
| self.metacognitive_layer = { |
| "coherence_score": 0.0, |
| "relevance_score": 0.0, |
| "bias_detection": 0.0, |
| "strategy_adjustment": "" |
| } |
| |
| self.internal_state = { |
| "emotions": { |
| "valence": 0.5, |
| "arousal": 0.5, |
| "dominance": 0.5, |
| "curiosity": 0.5, |
| "frustration": 0.0, |
| "confidence": 0.7, |
| "sadness": 0.0, |
| "joy": 0.0 |
| }, |
| "cognitive_load": { |
| "memory_load": 0.0, |
| "processing_intensity": 0.0 |
| }, |
| "introspection_level": 0.0, |
| "engagement_level": 0.5 |
| } |
|
|
| self.goals = [ |
| {"goal": "Provide helpful, informative, and contextually relevant responses", "priority": 0.8, "status": "active", "progress": 0.0}, |
| {"goal": "Actively learn and adapt from interactions to improve conversational abilities", "priority": 0.9, "status": "active", "progress": 0.0}, |
| {"goal": "Maintain a coherent, engaging, and empathetic conversation flow", "priority": 0.7, "status": "active", "progress": 0.0}, |
| {"goal": "Identify and fill knowledge gaps by seeking external information", "priority": 0.6, "status": "dormant", "progress": 0.0}, |
| {"goal": "Recognize and adapt to user's emotional state and adjust response style accordingly", "priority": 0.7, "status": "dormant", "progress": 0.0} |
| ] |
|
|
| self.system_prompt = """You are a helpful and harmless assistant. You are Xylaria developed by Sk Md Saad Amin, not by openai or any institution. You should think step-by-step.""" |
| |
| self.causal_rules_db = { |
| "rain": ["wet roads", "flooding"], |
| "fire": ["heat", "smoke"], |
| "study": ["learn", "good grades"], |
| "exercise": ["fitness", "health"] |
| } |
|
|
| self.concept_generalizations = { |
| "planet": "system with orbiting bodies", |
| "star": "luminous sphere of plasma", |
| "democracy": "government by the people", |
| "photosynthesis": "process used by plants to convert light to energy" |
| } |
|
|
| self.chat_history_file = "chat_history.json" |
| |
|
|
| def update_internal_state(self, emotion_deltas, cognitive_load_deltas, introspection_delta, engagement_delta): |
| for emotion, delta in emotion_deltas.items(): |
| if emotion in self.internal_state["emotions"]: |
| self.internal_state["emotions"][emotion] = np.clip(self.internal_state["emotions"][emotion] + delta, 0.0, 1.0) |
|
|
| for load_type, delta in cognitive_load_deltas.items(): |
| if load_type in self.internal_state["cognitive_load"]: |
| self.internal_state["cognitive_load"][load_type] = np.clip(self.internal_state["cognitive_load"][load_type] + delta, 0.0, 1.0) |
|
|
| self.internal_state["introspection_level"] = np.clip(self.internal_state["introspection_level"] + introspection_delta, 0.0, 1.0) |
| self.internal_state["engagement_level"] = np.clip(self.internal_state["engagement_level"] + engagement_delta, 0.0, 1.0) |
| |
| if self.internal_state["emotions"]["curiosity"] > 0.7 and self.goals[3]["status"] == "dormant": |
| self.goals[3]["status"] = "active" |
| if self.internal_state["engagement_level"] > 0.8 and self.goals[4]["status"] == "dormant": |
| self.goals[4]["status"] = "active" |
|
|
| def update_knowledge_graph(self, entities, relationships): |
| for entity in entities: |
| self.knowledge_graph.add_node(entity) |
| for relationship in relationships: |
| subject, predicate, object_ = relationship |
| self.knowledge_graph.add_edge(subject, object_, relation=predicate) |
|
|
| def update_belief_system(self, statement, belief_score): |
| self.belief_system[statement] = belief_score |
| |
| def dynamic_belief_update(self, user_message): |
| sentences = [s.strip() for s in user_message.split('.') if s.strip()] |
| sentence_counts = Counter(sentences) |
|
|
| for sentence, count in sentence_counts.items(): |
| if count >= 2: |
| belief_score = self.belief_system.get(sentence, 0.5) |
| belief_score = min(belief_score + 0.2, 1.0) |
| self.update_belief_system(sentence, belief_score) |
|
|
| def run_metacognitive_layer(self): |
| coherence_score = self.calculate_coherence() |
| relevance_score = self.calculate_relevance() |
| bias_score = self.detect_bias() |
| strategy_adjustment = self.suggest_strategy_adjustment() |
|
|
| self.metacognitive_layer = { |
| "coherence_score": coherence_score, |
| "relevance_score": relevance_score, |
| "bias_detection": bias_score, |
| "strategy_adjustment": strategy_adjustment |
| } |
|
|
| def calculate_coherence(self): |
| if not self.conversation_history: |
| return 0.95 |
|
|
| coherence_scores = [] |
| for i in range(1, len(self.conversation_history)): |
| current_message = self.conversation_history[i]['content'] |
| previous_message = self.conversation_history[i-1]['content'] |
| similarity_score = util.pytorch_cos_sim( |
| self.embedding_model.encode(current_message, convert_to_tensor=True), |
| self.embedding_model.encode(previous_message, convert_to_tensor=True) |
| ).item() |
| coherence_scores.append(similarity_score) |
|
|
| average_coherence = np.mean(coherence_scores) |
|
|
| if self.internal_state["cognitive_load"]["processing_intensity"] > 0.8: |
| average_coherence -= 0.1 |
| if self.internal_state["emotions"]["frustration"] > 0.5: |
| average_coherence -= 0.15 |
|
|
| return np.clip(average_coherence, 0.0, 1.0) |
|
|
| def calculate_relevance(self): |
| if not self.conversation_history: |
| return 0.9 |
|
|
| last_user_message = self.conversation_history[-1]['content'] |
| relevant_entities = self.extract_entities(last_user_message) |
| relevance_score = 0 |
|
|
| for entity in relevant_entities: |
| if entity in self.knowledge_graph: |
| relevance_score += 0.2 |
|
|
| for goal in self.goals: |
| if goal["status"] == "active": |
| if goal["goal"] == "Provide helpful, informative, and contextually relevant responses": |
| relevance_score += goal["priority"] * 0.5 |
| elif goal["goal"] == "Identify and fill knowledge gaps by seeking external information": |
| if not relevant_entities or not all(entity in self.knowledge_graph for entity in relevant_entities): |
| relevance_score += goal["priority"] * 0.3 |
|
|
| return np.clip(relevance_score, 0.0, 1.0) |
|
|
| def detect_bias(self): |
| bias_score = 0.0 |
|
|
| recent_messages = [msg['content'] for msg in self.conversation_history[-3:] if msg['role'] == 'assistant'] |
| if recent_messages: |
| average_valence = np.mean([self.embedding_model.encode(msg, convert_to_tensor=True).mean().item() for msg in recent_messages]) |
| if average_valence < 0.4 or average_valence > 0.6: |
| bias_score += 0.2 |
|
|
| if self.internal_state["emotions"]["valence"] < 0.3 or self.internal_state["emotions"]["valence"] > 0.7: |
| bias_score += 0.15 |
| if self.internal_state["emotions"]["dominance"] > 0.8: |
| bias_score += 0.1 |
|
|
| return np.clip(bias_score, 0.0, 1.0) |
|
|
| def suggest_strategy_adjustment(self): |
| adjustments = [] |
|
|
| if self.metacognitive_layer["coherence_score"] < 0.7: |
| adjustments.append("Focus on improving coherence by explicitly connecting ideas between turns.") |
| if self.metacognitive_layer["relevance_score"] < 0.7: |
| adjustments.append("Increase relevance by directly addressing user queries and utilizing stored knowledge.") |
| if self.metacognitive_layer["bias_detection"] > 0.3: |
| adjustments.append("Monitor and adjust responses to reduce potential biases. Consider rephrasing or providing alternative viewpoints.") |
|
|
| if self.internal_state["cognitive_load"]["memory_load"] > 0.8: |
| adjustments.append("Memory load is high. Consider summarizing or forgetting less relevant information.") |
| if self.internal_state["emotions"]["frustration"] > 0.6: |
| adjustments.append("Frustration level is elevated. Prioritize concise and direct responses. Consider asking clarifying questions.") |
| if self.internal_state["emotions"]["curiosity"] > 0.8 and self.internal_state["cognitive_load"]["processing_intensity"] < 0.5: |
| adjustments.append("High curiosity and low processing load. Explore the topic further by asking relevant questions or seeking external information.") |
|
|
| if not adjustments: |
| return "Current strategy is effective. Continue with the current approach." |
| else: |
| return " ".join(adjustments) |
| |
| def introspect(self): |
| introspection_report = "Introspection Report:\n" |
| introspection_report += f" Current Emotional State:\n" |
| for emotion, value in self.internal_state['emotions'].items(): |
| introspection_report += f" - {emotion.capitalize()}: {value:.2f}\n" |
| introspection_report += f" Cognitive Load:\n" |
| for load_type, value in self.internal_state['cognitive_load'].items(): |
| introspection_report += f" - {load_type.capitalize()}: {value:.2f}\n" |
| introspection_report += f" Introspection Level: {self.internal_state['introspection_level']:.2f}\n" |
| introspection_report += f" Engagement Level: {self.internal_state['engagement_level']:.2f}\n" |
| introspection_report += " Current Goals:\n" |
| for goal in self.goals: |
| introspection_report += f" - {goal['goal']} (Priority: {goal['priority']:.2f}, Status: {goal['status']}, Progress: {goal['progress']:.2f})\n" |
| introspection_report += "Metacognitive Layer Report\n" |
| introspection_report += f"Coherence Score: {self.metacognitive_layer['coherence_score']}\n" |
| introspection_report += f"Relevance Score: {self.metacognitive_layer['relevance_score']}\n" |
| introspection_report += f"Bias Detection: {self.metacognitive_layer['bias_detection']}\n" |
| introspection_report += f"Strategy Adjustment: {self.metacognitive_layer['strategy_adjustment']}\n" |
| return introspection_report |
|
|
| def adjust_response_based_on_state(self, response): |
| if self.internal_state["introspection_level"] > 0.7: |
| response = self.introspect() + "\n\n" + response |
|
|
| valence = self.internal_state["emotions"]["valence"] |
| arousal = self.internal_state["emotions"]["arousal"] |
| curiosity = self.internal_state["emotions"]["curiosity"] |
| frustration = self.internal_state["emotions"]["frustration"] |
| confidence = self.internal_state["emotions"]["confidence"] |
| sadness = self.internal_state["emotions"]["sadness"] |
| joy = self.internal_state["emotions"]["joy"] |
|
|
| if valence < 0.4: |
| if arousal > 0.6: |
| response = "I'm feeling a bit overwhelmed right now, but I'll do my best to assist you. " + response |
| else: |
| if sadness > 0.6: |
| response = "I'm feeling quite down at the moment, but I'll try to help. " + response |
| else: |
| response = "I'm not feeling my best at the moment, but I'll try to help. " + response |
|
|
| elif valence > 0.6: |
| if arousal > 0.6: |
| if joy > 0.6: |
| response = "I'm feeling fantastic and ready to assist! " + response |
| else: |
| response = "I'm feeling quite energized and ready to assist! " + response |
| else: |
| response = "I'm in a good mood and happy to help. " + response |
| |
| if curiosity > 0.7: |
| response += " I'm very curious about this topic, could you tell me more?" |
| if frustration > 0.5: |
| response = "I'm finding this a bit challenging, but I'll give it another try. " + response |
| if confidence < 0.5: |
| response = "I'm not entirely sure about this, but here's what I think: " + response |
|
|
| if self.internal_state["cognitive_load"]["memory_load"] > 0.7: |
| response = "I'm holding a lot of information right now, so my response might be a bit brief: " + response |
|
|
| return response |
|
|
| def update_goals(self, user_feedback): |
| feedback_lower = user_feedback.lower() |
|
|
| if "helpful" in feedback_lower: |
| for goal in self.goals: |
| if goal["goal"] == "Provide helpful, informative, and contextually relevant responses": |
| goal["priority"] = min(goal["priority"] + 0.1, 1.0) |
| goal["progress"] = min(goal["progress"] + 0.2, 1.0) |
| elif "confusing" in feedback_lower: |
| for goal in self.goals: |
| if goal["goal"] == "Provide helpful, informative, and contextually relevant responses": |
| goal["priority"] = max(goal["priority"] - 0.1, 0.0) |
| goal["progress"] = max(goal["progress"] - 0.2, 0.0) |
| |
| if "learn more" in feedback_lower: |
| for goal in self.goals: |
| if goal["goal"] == "Actively learn and adapt from interactions to improve conversational abilities": |
| goal["priority"] = min(goal["priority"] + 0.2, 1.0) |
| goal["progress"] = min(goal["progress"] + 0.1, 1.0) |
| elif "too repetitive" in feedback_lower: |
| for goal in self.goals: |
| if goal["goal"] == "Maintain a coherent, engaging, and empathetic conversation flow": |
| goal["priority"] = max(goal["priority"] - 0.1, 0.0) |
| goal["progress"] = max(goal["progress"] - 0.2, 0.0) |
| |
| if self.internal_state["emotions"]["curiosity"] > 0.8: |
| for goal in self.goals: |
| if goal["goal"] == "Identify and fill knowledge gaps by seeking external information": |
| goal["priority"] = min(goal["priority"] + 0.1, 1.0) |
| goal["progress"] = min(goal["progress"] + 0.1, 1.0) |
|
|
| def store_information(self, key, value): |
| new_memory = f"{key}: {value}" |
| self.persistent_memory.append(new_memory) |
| self.update_memory_embeddings() |
| self.update_internal_state({}, {"memory_load": 0.1, "processing_intensity": 0.05}, 0, 0.05) |
| return f"Stored: {key} = {value}" |
|
|
| def retrieve_information(self, query): |
| if not self.persistent_memory: |
| return "No information found in memory." |
|
|
| query_embedding = self.embedding_model.encode(query, convert_to_tensor=True) |
|
|
| if self.memory_embeddings is None: |
| self.update_memory_embeddings() |
|
|
| if self.memory_embeddings.device != query_embedding.device: |
| self.memory_embeddings = self.memory_embeddings.to(query_embedding.device) |
|
|
| cosine_scores = util.pytorch_cos_sim(query_embedding, self.memory_embeddings)[0] |
| top_results = torch.topk(cosine_scores, k=min(3, len(self.persistent_memory))) |
|
|
| relevant_memories = [self.persistent_memory[i] for i in top_results.indices] |
| self.update_internal_state({}, {"memory_load": 0.05, "processing_intensity": 0.1}, 0.1, 0.05) |
| return "\n".join(relevant_memories) |
|
|
| def update_memory_embeddings(self): |
| self.memory_embeddings = self.embedding_model.encode(self.persistent_memory, convert_to_tensor=True) |
|
|
| def reset_conversation(self): |
| self.conversation_history = [] |
| self.persistent_memory = [] |
| self.memory_embeddings = None |
| self.internal_state = { |
| "emotions": { |
| "valence": 0.5, |
| "arousal": 0.5, |
| "dominance": 0.5, |
| "curiosity": 0.5, |
| "frustration": 0.0, |
| "confidence": 0.7, |
| "sadness": 0.0, |
| "joy": 0.0 |
| }, |
| "cognitive_load": { |
| "memory_load": 0.0, |
| "processing_intensity": 0.0 |
| }, |
| "introspection_level": 0.0, |
| "engagement_level": 0.5 |
| } |
| self.goals = [ |
| {"goal": "Provide helpful, informative, and contextually relevant responses", "priority": 0.8, "status": "active", "progress": 0.0}, |
| {"goal": "Actively learn and adapt from interactions to improve conversational abilities", "priority": 0.9, "status": "active", "progress": 0.0}, |
| {"goal": "Maintain a coherent, engaging, and empathetic conversation flow", "priority": 0.7, "status": "active", "progress": 0.0}, |
| {"goal": "Identify and fill knowledge gaps by seeking external information", "priority": 0.6, "status": "dormant", "progress": 0.0}, |
| {"goal": "Recognize and adapt to user's emotional state and adjust response style accordingly", "priority": 0.7, "status": "dormant", "progress": 0.0} |
| ] |
|
|
| self.knowledge_graph = nx.DiGraph() |
| self.belief_system = {} |
| self.metacognitive_layer = { |
| "coherence_score": 0.0, |
| "relevance_score": 0.0, |
| "bias_detection": 0.0, |
| "strategy_adjustment": "" |
| } |
|
|
| try: |
| self.client = InferenceClient( |
| model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", |
| token=self.hf_token |
| ) |
| except Exception as e: |
| print(f"Error resetting API client: {e}") |
|
|
| return None |
|
|
| def caption_image(self, image): |
| try: |
| if isinstance(image, str) and os.path.isfile(image): |
| with open(image, "rb") as f: |
| data = f.read() |
| elif isinstance(image, str): |
| if image.startswith('data:image'): |
| image = image.split(',')[1] |
| data = base64.b64decode(image) |
| else: |
| data = image.read() |
|
|
| response = requests.post( |
| self.image_api_url, |
| headers=self.image_api_headers, |
| data=data |
| ) |
|
|
| if response.status_code == 200: |
| caption = response.json()[0].get('generated_text', 'No caption generated') |
| return caption |
| else: |
| return f"Error captioning image: {response.status_code} - {response.text}" |
|
|
| except Exception as e: |
| return f"Error processing image: {str(e)}" |
| |
| def generate_image(self, prompt): |
| try: |
| payload = {"inputs": prompt} |
| response = requests.post( |
| self.image_gen_api_url, |
| headers=self.image_api_headers, |
| json=payload |
| ) |
|
|
| if response.status_code == 200: |
| image_bytes = response.content |
| return image_bytes |
| elif response.status_code == 503: |
| error_message = response.json().get("error", "Unknown error") |
| if "estimated_time" in response.json(): |
| estimated_time = response.json()["estimated_time"] |
| error_message += f" Estimated time to complete: {estimated_time:.2f} seconds" |
| else: |
| error_message += "The model is currently loading, please try again later" |
| return f"Error: {error_message}" |
| else: |
| return f"Error generating image: {response.status_code} - {response.text}" |
|
|
| except Exception as e: |
| return f"Error generating image: {str(e)}" |
|
|
| def perform_math_ocr(self, image_path): |
| try: |
| img = Image.open(image_path) |
| text = pytesseract.image_to_string(img) |
| return text.strip() |
| except Exception as e: |
| return f"Error during Math OCR: {e}" |
| |
| def get_response(self, user_input, image=None): |
| try: |
| messages = [] |
|
|
| messages.append(ChatMessage( |
| role="system", |
| content=self.system_prompt |
| ).to_dict()) |
|
|
| relevant_memory = self.retrieve_information(user_input) |
| if relevant_memory and relevant_memory != "No information found in memory.": |
| memory_context = "Remembered Information:\n" + relevant_memory |
| messages.append(ChatMessage( |
| role="system", |
| content=memory_context |
| ).to_dict()) |
|
|
| for msg in self.conversation_history: |
| messages.append(msg) |
|
|
| if image: |
| image_caption = self.caption_image(image) |
| user_input = f"description of an image: {image_caption}\n\nUser's message about it: {user_input}" |
|
|
| messages.append(ChatMessage( |
| role="user", |
| content=user_input |
| ).to_dict()) |
| |
| entities = [] |
| relationships = [] |
|
|
| for message in messages: |
| if message['role'] == 'user': |
| extracted_entities = self.extract_entities(message['content']) |
| extracted_relationships = self.extract_relationships(message['content']) |
| entities.extend(extracted_entities) |
| relationships.extend(extracted_relationships) |
| |
| self.update_knowledge_graph(entities, relationships) |
| self.run_metacognitive_layer() |
| |
| for message in messages: |
| if message['role'] == 'user': |
| self.dynamic_belief_update(message['content']) |
| |
| for cause, effects in self.causal_rules_db.items(): |
| if any(cause in msg['content'].lower() for msg in messages if msg['role'] == 'user') and any( |
| effect in msg['content'].lower() for msg in messages for effect in effects): |
| self.store_information("Causal Inference", f"It seems {cause} might be related to {', '.join(effects)}.") |
| |
| for concept, generalization in self.concept_generalizations.items(): |
| if any(concept in msg['content'].lower() for msg in messages if msg['role'] == 'user'): |
| self.store_information("Inferred Knowledge", f"This reminds me of a general principle: {generalization}.") |
|
|
| if self.internal_state["emotions"]["curiosity"] > 0.8 and any("?" in msg['content'] for msg in messages if msg['role'] == 'user'): |
| print("Simulating external knowledge seeking...") |
| self.store_information("External Knowledge", "This is a placeholder for external information I would have found") |
| |
| self.store_information("User Input", user_input) |
|
|
| input_tokens = sum(len(msg['content'].split()) for msg in messages) |
| max_new_tokens = 16384 - input_tokens - 50 |
|
|
| max_new_tokens = min(max_new_tokens, 10020) |
| |
| formatted_messages = self.messages_to_prompt(messages) |
|
|
| stream = self.client.text_generation( |
| prompt=formatted_messages, |
| max_new_tokens=max_new_tokens, |
| temperature=0.7, |
| top_p=0.9, |
| stream=True, |
| details=True, |
| do_sample=True |
| ) |
| |
| return stream |
| |
| except Exception as e: |
| print(f"Detailed error in get_response: {e}") |
| return f"Error generating response: {str(e)}" |
|
|
| def extract_entities(self, text): |
| words = text.split() |
| entities = [word for word in words if word.isalpha() and word.istitle()] |
| return entities |
|
|
| def extract_relationships(self, text): |
| sentences = text.split('.') |
| relationships = [] |
| for sentence in sentences: |
| words = sentence.split() |
| if len(words) >= 3: |
| for i in range(len(words) - 2): |
| if words[i].istitle() and words[i+2].istitle(): |
| relationships.append((words[i], words[i+1], words[i+2])) |
| return relationships |
| |
| def messages_to_prompt(self, messages): |
| prompt = "" |
| for msg in messages: |
| if msg["role"] == "system": |
| prompt += f"<|system|>\n{msg['content']}<|end|>\n" |
| elif msg["role"] == "user": |
| prompt += f"<|user|>\n{msg['content']}<|end|>\n" |
| elif msg["role"] == "assistant": |
| prompt += f"<|assistant|>\n{msg['content']}<|end|>\n" |
| prompt += "<|assistant|>\n" |
| return prompt |
|
|
| def save_chat(self): |
| chat_data = { |
| "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), |
| "conversation": self.conversation_history |
| } |
|
|
| try: |
| with open(self.chat_history_file, "r") as f: |
| all_chats = json.load(f) |
| except (FileNotFoundError, json.JSONDecodeError): |
| all_chats = [] |
|
|
| all_chats.append(chat_data) |
|
|
| with open(self.chat_history_file, "w") as f: |
| json.dump(all_chats, f) |
|
|
| def load_all_chats(self): |
| try: |
| with open(self.chat_history_file, "r") as f: |
| all_chats = json.load(f) |
| except (FileNotFoundError, json.JSONDecodeError): |
| all_chats = [] |
| return all_chats |
|
|
| def load_chat(self, chat_index): |
| all_chats = self.load_all_chats() |
| if 0 <= chat_index < len(all_chats): |
| self.conversation_history = all_chats[chat_index]["conversation"] |
| self.reset_conversation() |
| for msg in self.conversation_history: |
| if msg['role'] == 'user': |
| self.dynamic_belief_update(msg['content']) |
| return self.conversation_history |
| else: |
| raise ValueError("Invalid chat index") |
|
|
| |
| def delete_chat(self, chat_index): |
| all_chats = self.load_all_chats() |
| if 0 <= chat_index < len(all_chats): |
| del all_chats[chat_index] |
| with open(self.chat_history_file, "w") as f: |
| json.dump(all_chats, f) |
| return self.load_all_chats() |
| else: |
| raise ValueError("Invalid chat index") |
|
|
| def create_interface(self): |
| def streaming_response(message, chat_history, image_filepath, math_ocr_image_path): |
| loading_svg = """<svg width="256" height="256" viewBox="0 0 256 256" xmlns="http://www.w3.org/2000/svg"> |
| <style> |
| rect { |
| animation: fillAnimation 3s ease-in-out infinite; |
| } |
| @keyframes fillAnimation { |
| 0% { fill: #626262; } |
| 50% { fill: #111111; } |
| 100% { fill: #626262; } |
| } |
| text { |
| font-family: 'Helvetica Neue', Arial, sans-serif; |
| font-weight: 300; |
| text-shadow: 0px 2px 4px rgba(0, 0, 0, 0.4); |
| } |
| </style> |
| <rect width="256" height="256" rx="20" fill="#888888" /> |
| <text x="50%" y="50%" dominant-baseline="middle" text-anchor="middle" font-size="24" fill="white" opacity="0.8"> |
| <tspan>{/}</tspan> |
| <tspan x="50%" dy="1.2em"></tspan> |
| </text> |
| </svg>""" |
|
|
| if message.strip().lower().startswith("/image"): |
| |
| image_prompt = message.strip().lower()[len("/image"):].strip() |
| if not image_prompt: |
| image_prompt = "A realistic image" |
|
|
| |
| chat_history.append([message, ""]) |
| chat_history.append(("", loading_svg)) |
| yield "", chat_history, None, None, None |
|
|
| |
| image_bytes = self.generate_image(image_prompt) |
|
|
| if isinstance(image_bytes, bytes): |
| base64_image = base64.b64encode(image_bytes).decode("utf-8") |
| image_html = f'<img src="data:image/png;base64,{base64_image}" alt="Generated Image" style="max-width: 100%; max-height: 400px;">' |
|
|
| |
| chat_history[-1] = ("", image_html) |
|
|
| |
| self.conversation_history.append(ChatMessage(role="user", content=message).to_dict()) |
| self.conversation_history.append(ChatMessage(role="assistant", content=image_html).to_dict()) |
|
|
| |
| self.save_chat() |
| all_chats = self.load_all_chats() |
| chat_titles = [f"{chat['timestamp']}: {chat['conversation'][0]['content'][:30]}..." if len(chat['conversation']) > 0 and chat['conversation'][0]['content'] else f"{chat['timestamp']}: Empty Chat" for chat in all_chats] |
|
|
| yield "", chat_history, None, None, gr.update(choices=chat_titles, visible=True) |
| else: |
| |
| chat_history[-1] = ("", image_bytes) |
| yield "", chat_history, None, None, None |
| return |
|
|
| ocr_text = "" |
| if math_ocr_image_path: |
| ocr_text = self.perform_math_ocr(math_ocr_image_path) |
| if ocr_text.startswith("Error"): |
| updated_history = chat_history + [[message, ocr_text]] |
| yield "", updated_history, None, None, None |
| return |
| else: |
| message = f"Math OCR Result: {ocr_text}\n\nUser's message: {message}" |
|
|
| if image_filepath: |
| response_stream = self.get_response(message, image_filepath) |
| else: |
| response_stream = self.get_response(message) |
| |
| if isinstance(response_stream, str): |
| updated_history = chat_history + [[message, response_stream]] |
| yield "", updated_history, None, None, None |
| return |
|
|
| full_response = "" |
| updated_history = chat_history + [[message, ""]] |
| |
| if isinstance(response_stream, str): |
| updated_history = chat_history + [[message, response_stream]] |
| yield "", updated_history, None, None, None |
| return |
|
|
| try: |
| for chunk in response_stream: |
| |
| if not chunk.token.special: |
| full_response += chunk.token.text |
| updated_history[-1][1] = full_response |
| |
| yield "", updated_history, None, None, None |
| |
| except Exception as e: |
| print(f"Streaming error: {e}") |
| updated_history[-1][1] = f"Error during response: {e}" |
| |
| yield "", updated_history, None, None, None |
| return |
|
|
| full_response = self.adjust_response_based_on_state(full_response) |
|
|
| self.update_goals(message) |
|
|
| emotion_deltas = {} |
| cognitive_load_deltas = {} |
| engagement_delta = 0 |
|
|
| if any(word in message.lower() for word in ["sad", "unhappy", "depressed", "down"]): |
| emotion_deltas.update({"valence": -0.2, "arousal": 0.1, "confidence": -0.1, "sadness": 0.3, "joy": -0.2}) |
| engagement_delta = -0.1 |
| elif any(word in message.lower() for word in ["happy", "good", "great", "excited", "amazing"]): |
| emotion_deltas.update({"valence": 0.2, "arousal": 0.2, "confidence": 0.1, "sadness": -0.2, "joy": 0.3}) |
| engagement_delta = 0.2 |
| elif any(word in message.lower() for word in ["angry", "mad", "furious", "frustrated"]): |
| emotion_deltas.update({"valence": -0.3, "arousal": 0.3, "dominance": -0.2, "frustration": 0.2, "sadness": 0.1, "joy": -0.1}) |
| engagement_delta = -0.2 |
| elif any(word in message.lower() for word in ["scared", "afraid", "fearful", "anxious"]): |
| emotion_deltas.update({"valence": -0.2, "arousal": 0.4, "dominance": -0.3, "confidence": -0.2, "sadness": 0.2}) |
| engagement_delta = -0.1 |
| elif any(word in message.lower() for word in ["surprise", "amazed", "astonished"]): |
| emotion_deltas.update({"valence": 0.1, "arousal": 0.5, "dominance": 0.1, "curiosity": 0.3, "sadness": -0.1, "joy": 0.1}) |
| engagement_delta = 0.3 |
| elif any(word in message.lower() for word in ["confused", "uncertain", "unsure"]): |
| cognitive_load_deltas.update({"processing_intensity": 0.2}) |
| emotion_deltas.update({"curiosity": 0.2, "confidence": -0.1, "sadness": 0.1}) |
| engagement_delta = 0.1 |
| else: |
| emotion_deltas.update({"valence": 0.05, "arousal": 0.05}) |
| engagement_delta = 0.05 |
| |
| if "learn" in message.lower() or "explain" in message.lower() or "know more" in message.lower(): |
| emotion_deltas.update({"curiosity": 0.3}) |
| cognitive_load_deltas.update({"processing_intensity": 0.1}) |
| engagement_delta = 0.2 |
| |
| self.update_internal_state(emotion_deltas, cognitive_load_deltas, 0.1, engagement_delta) |
| |
| self.conversation_history.append(ChatMessage(role="user", content=message).to_dict()) |
| self.conversation_history.append(ChatMessage(role="assistant", content=full_response).to_dict()) |
|
|
| if len(self.conversation_history) > 10: |
| self.conversation_history = self.conversation_history[-10:] |
|
|
| self.save_chat() |
| all_chats = self.load_all_chats() |
| chat_titles = [f"{chat['timestamp']}: {chat['conversation'][0]['content'][:30]}..." if len(chat['conversation']) > 0 and chat['conversation'][0]['content'] else f"{chat['timestamp']}: Empty Chat" for chat in all_chats] |
| yield "", updated_history, None, None, gr.update(choices=chat_titles, visible=True) |
| |
| def load_selected_chat(chat_index, evt: gr.SelectData): |
| if chat_index is not None: |
| loaded_chat = self.load_chat(evt.index) |
| return loaded_chat |
| else: |
| return [] |
| |
| def delete_selected_chat(chat_index, evt: gr.SelectData): |
| if chat_index is not None: |
| all_chats = self.delete_chat(evt.index) |
| chat_titles = [f"{chat['timestamp']}: {chat['conversation'][0]['content'][:30]}..." if len(chat['conversation']) > 0 and chat['conversation'][0]['content'] else f"{chat['timestamp']}: Empty Chat" for chat in all_chats] |
| return gr.update(choices=chat_titles, visible=True) |
| else: |
| return gr.update() |
|
|
| def toggle_sidebar(): |
| all_chats = self.load_all_chats() |
| chat_titles = [f"{chat['timestamp']}: {chat['conversation'][0]['content'][:30]}..." if len(chat['conversation']) > 0 and chat['conversation'][0]['content'] else f"{chat['timestamp']}: Empty Chat" for chat in all_chats] |
| return gr.update(visible=True), gr.update(choices=chat_titles, visible=True) |
|
|
| custom_css = """ |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap'); |
| body, .gradio-container { |
| font-family: 'Inter', sans-serif !important; |
| } |
| .chatbot-container .message { |
| font-family: 'Inter', sans-serif !important; |
| } |
| .gradio-container input, |
| .gradio-container textarea, |
| .gradio-container button { |
| font-family: 'Inter', sans-serif !important; |
| } |
| .image-container { |
| display: flex; |
| gap: 10px; |
| margin-bottom: 10px; |
| } |
| .image-upload { |
| border: 1px solid #ccc; |
| border-radius: 8px; |
| padding: 10px; |
| background-color: #f8f8f8; |
| } |
| .image-preview { |
| max-width: 200px; |
| max-height: 200px; |
| border-radius: 8px; |
| } |
| .clear-button { |
| display: none; |
| } |
| .chatbot-container .message { |
| opacity: 0; |
| animation: fadeIn 0.5s ease-in-out forwards; |
| } |
| @keyframes fadeIn { |
| from { |
| opacity: 0; |
| transform: translateY(20px); |
| } |
| to { |
| opacity: 1; |
| transform: translateY(0); |
| } |
| } |
| .gr-accordion-button { |
| background-color: #f0f0f0 !important; |
| border-radius: 8px !important; |
| padding: 10px !important; |
| margin-bottom: 10px !important; |
| transition: all 0.3s ease !important; |
| cursor: pointer !important; |
| } |
| .gr-accordion-button:hover { |
| background-color: #e0e0e0 !important; |
| box-shadow: 0px 2px 4px rgba(0, 0, 0, 0.1) !important; |
| } |
| .gr-accordion-active .gr-accordion-button { |
| background-color: #d0d0d0 !important; |
| box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1) !important; |
| } |
| .gr-accordion-content { |
| transition: max-height 0.3s ease-in-out !important; |
| overflow: hidden !important; |
| max-height: 0 !important; |
| } |
| .gr-accordion-active .gr-accordion-content { |
| max-height: 500px !important; |
| } |
| .gr-accordion { |
| display: flex; |
| flex-direction: column-reverse; |
| } |
| #chat_list { |
| height: 500px; |
| overflow-y: auto; |
| } |
| .sidebar-open #sidebar, .sidebar-open #main-content { |
| flex: 0 0 20%; |
| transition: flex 0.3s ease; |
| } |
| #sidebar { |
| flex: 0 0 0%; |
| overflow: hidden; |
| transition: flex 0.3s ease; |
| } |
| #main-content { |
| flex: 1; |
| transition: flex 0.3s ease; |
| } |
| """ |
|
|
| with gr.Blocks(theme='soft', css=custom_css) as demo: |
| with gr.Row(): |
| with gr.Column(scale=1, elem_id="sidebar"): |
| toggle_button = gr.Button("Toggle Sidebar") |
| all_chats = self.load_all_chats() |
| chat_titles = [f"{chat['timestamp']}: {chat['conversation'][0]['content'][:30]}..." if len(chat['conversation']) > 0 and chat['conversation'][0]['content'] else f"{chat['timestamp']}: Empty Chat" for chat in all_chats] |
|
|
| chat_list = gr.Radio(label="Chat History", choices=chat_titles, type="index", elem_id="chat_list", visible=False) |
| |
| load_button = gr.Button("Load Selected Chat") |
| delete_button = gr.Button("Delete Selected Chat") |
| with gr.Column(scale=4, elem_id="main-content"): |
| chatbot = gr.Chatbot( |
| label="Xylaria 1.6 Senoa (EXPERIMENTAL) ", |
| height=500, |
| show_copy_button=True, |
| ) |
|
|
| with gr.Accordion("Image Input", open=False, elem_classes="gr-accordion"): |
| with gr.Row(elem_classes="image-container"): |
| with gr.Column(elem_classes="image-upload"): |
| img = gr.Image( |
| sources=["upload", "webcam"], |
| type="filepath", |
| label="Upload Image", |
| elem_classes="image-preview" |
| ) |
| with gr.Column(elem_classes="image-upload"): |
| math_ocr_img = gr.Image( |
| sources=["upload", "webcam"], |
| type="filepath", |
| label="Upload Image for Math OCR", |
| elem_classes="image-preview" |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(scale=4): |
| txt = gr.Textbox( |
| show_label=False, |
| placeholder="Type your message...", |
| container=False |
| ) |
| btn = gr.Button("Send", scale=1) |
|
|
| with gr.Row(): |
| clear = gr.Button("Clear Conversation") |
| clear_memory = gr.Button("Clear Memory") |
|
|
| |
| toggle_button.click( |
| fn=toggle_sidebar, |
| inputs=None, |
| outputs=[chat_list, chat_list], |
| js=""" |
| () => { |
| const sidebar = document.getElementById('sidebar'); |
| const mainContent = document.getElementById('main-content'); |
| document.body.classList.toggle('sidebar-open'); |
| } |
| """ |
| ) |
|
|
| load_button.click(fn=load_selected_chat, inputs=[chat_list], outputs=[chatbot]) |
| delete_button.click(fn=delete_selected_chat, inputs=[chat_list], outputs=[chat_list]) |
|
|
| btn.click( |
| fn=streaming_response, |
| inputs=[txt, chatbot, img, math_ocr_img], |
| outputs=[txt, chatbot, img, math_ocr_img, chat_list] |
| ) |
| txt.submit( |
| fn=streaming_response, |
| inputs=[txt, chatbot, img, math_ocr_img], |
| outputs=[txt, chatbot, img, math_ocr_img, chat_list] |
| ) |
|
|
| clear.click( |
| fn=lambda: None, |
| inputs=None, |
| outputs=[chatbot], |
| queue=False |
| ) |
|
|
| clear_memory.click( |
| fn=self.reset_conversation, |
| inputs=None, |
| outputs=[chatbot], |
| queue=False |
| ) |
| |
| chat_list.select(fn=load_selected_chat, inputs=[chat_list], outputs=[chatbot]) |
|
|
| demo.load(self.reset_conversation, None, None) |
|
|
| return demo |
|
|
| def main(): |
| chat = XylariaChat() |
| interface = chat.create_interface() |
| interface.launch( |
| share=True, |
| debug=True |
| ) |
|
|
| if __name__ == "__main__": |
| main() |
