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()