app.py

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 nltk

# Ensure NLTK resources are available
try:
    nltk.data.find('tokenizers/punkt')
    nltk.data.find('averaged_perceptron_tagger')
except LookupError:
    nltk.download('punkt')
    nltk.download('averaged_perceptron_tagger')

@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="Qwen/QwQ-32B-Preview",
            api_key=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.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": ""
        }
        
        # Enhanced Internal State with more nuanced emotional and cognitive parameters
        self.internal_state = {
            "emotions": {
                "valence": 0.5,  # Overall positivity or negativity
                "arousal": 0.5,  # Level of excitement or calmness
                "dominance": 0.5, # Feeling of control in the interaction
                "curiosity": 0.5, # Drive to learn and explore new information
                "frustration": 0.0, # Level of frustration or impatience
                "confidence": 0.7 # Confidence in providing accurate and relevant responses
            },
            "cognitive_load": {
                "memory_load": 0.0, # How much of the current memory capacity is being used
                "processing_intensity": 0.0 # How hard the model is working to process information
            },
            "introspection_level": 0.0,
            "engagement_level": 0.5 # How engaged the model is with the current conversation
        }

        # More dynamic and adaptive goals
        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}, # New goal for proactive learning
            {"goal": "Recognize and adapt to user's emotional state and adjust response style accordingly", "priority": 0.7, "status": "dormant", "progress": 0.0} # New goal for emotional intelligence
        ]

        self.system_prompt = """You are a helpful and harmless assistant. You are Xylaria developed by Sk Md Saad Amin. You should think step-by-step """
        
        # --- SCALED-DOWN AGI FEATURES ---

        # 1. Advanced Knowledge Representation & Reasoning (Simplified)
        self.causal_rules_db = {  # Simple rule-based causal relationships
            "rain": ["wet roads", "flooding"],
            "study": ["good grades"],
            "exercise": ["better health"]
        }
        self.concept_generalizations = {  # Basic concept generalizations
            "planet": "system with orbiting bodies",
            "electron": "system with orbiting bodies",
            "atom": "system with orbiting bodies"
        }

    def update_internal_state(self, emotion_deltas, cognitive_load_deltas, introspection_delta, engagement_delta):
        # Update emotions with more nuanced changes
        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)

        # Update cognitive load
        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)

        # Update introspection and engagement levels
        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)
        
        # Activate dormant goals based on internal state
        if self.internal_state["emotions"]["curiosity"] > 0.7 and self.goals[3]["status"] == "dormant":
            self.goals[3]["status"] = "active" # Activate knowledge gap filling
        if self.internal_state["engagement_level"] > 0.8 and self.goals[4]["status"] == "dormant":
            self.goals[4]["status"] = "active" # Activate emotional adaptation

    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 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):
        # Improved coherence calculation considering conversation history and internal state
        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)

        # Adjust coherence based on internal state
        if self.internal_state["cognitive_load"]["processing_intensity"] > 0.8:
            average_coherence -= 0.1  # Reduce coherence if under heavy processing load
        if self.internal_state["emotions"]["frustration"] > 0.5:
            average_coherence -= 0.15 # Reduce coherence if frustrated

        return np.clip(average_coherence, 0.0, 1.0)

    def calculate_relevance(self):
        # More sophisticated relevance calculation using knowledge graph and goal priorities
        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

        # Check if entities are present in the knowledge graph
        for entity in relevant_entities:
            if entity in self.knowledge_graph:
                relevance_score += 0.2

        # Consider current goals and their priorities
        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 # Boost relevance if aligned with primary goal
                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 # Boost relevance if triggering knowledge gap filling

        return np.clip(relevance_score, 0.0, 1.0)

    def detect_bias(self):
        # Enhanced bias detection using sentiment analysis and internal state monitoring
        bias_score = 0.0

        # Analyze sentiment of recent conversation history
        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 # Potential bias if sentiment is strongly positive or negative

        # Check for emotional extremes in internal state
        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):
        # More nuanced strategy adjustments based on metacognitive analysis and internal state
        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.")

        # Internal state-driven adjustments
        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):
        # More sophisticated response adjustment based on internal state
        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"]

        # Adjust tone based on valence and arousal
        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:
                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:
                response = "I'm feeling quite energized and ready to assist! " + response
            else:
                response = "I'm in a good mood and happy to help. " + response

        # Adjust response based on other emotional states
        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

        # Adjust based on cognitive load
        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):
        # More dynamic goal updates based on feedback and internal state
        feedback_lower = user_feedback.lower()

        # General feedback
        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)
        
        # Goal-specific feedback
        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)
        
        # Internal state influence on goal updates
        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
            },
            "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="Qwen/QwQ-32B-Preview",
                api_key=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 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()

            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)

            stream = self.client.chat_completion(
                messages=messages,
                model="Qwen/QwQ-32B-Preview",
                temperature=0.7,
                max_tokens=max_new_tokens,
                top_p=0.9,
                stream=True
            )
            
             # --- SCALED-DOWN AGI FEATURE INTEGRATION INTO RESPONSE GENERATION ---

            # 1.b. Abstract Reasoning (Simplified):
            # Check if the current message involves a concept with a known generalization.
            for concept, generalization in self.concept_generalizations.items():
                if concept in user_input.lower():
                    inferred_knowledge = f"This reminds me of a general principle: {generalization}."
                    self.store_information("Inferred Knowledge", inferred_knowledge)  # Store for later retrieval, if needed
            
             # 1.c. Dynamic Updating of Beliefs (Simplified):
            # Very basic example: increase belief if something is stated repeatedly.
            belief_updates = Counter()
            for msg in self.conversation_history:
                if msg['role'] == 'user':
                    sentences = nltk.sent_tokenize(msg['content']) # Using nltk for sentence tokenization
                    for sentence in sentences:
                        belief_updates[sentence] += 1

            for statement, count in belief_updates.items():
                if count >= 2:  # If a statement is repeated 2 or more times
                    current_belief_score = self.belief_system.get(statement, 0.5) # Default belief 0.5
                    updated_belief_score = min(current_belief_score + 0.2, 1.0) # Increase belief, max 1.0
                    self.update_belief_system(statement, updated_belief_score)
            
             # 2.a. Lifelong Learning (Simplified):
            # Store key information from user input in persistent memory.
            if user_input:
                self.store_information("User Input", user_input)

            # 2.b. Autonomous Knowledge Discovery (Very Simplified):
            # Simulate seeking information if curiosity is high and the user asks a question.
            if self.internal_state["emotions"]["curiosity"] > 0.8 and "?" in user_input:
                print("Simulating external knowledge seeking...")
                # In a real implementation, you might query an API or database here.
                simulated_external_info = "This is a placeholder for external information I would have found."
                self.store_information("External Knowledge", simulated_external_info)
                # The chatbot can then use this "External Knowledge" in its response.
            
            # 1.a. Causal Reasoning (Simplified):
            # Check for potential causal relationships in the user input and conversation history.
            for cause, effects in self.causal_rules_db.items():
                if cause in user_input.lower():
                    for effect in effects:
                        if effect in " ".join([msg['content'].lower() for msg in self.conversation_history]).lower():
                            causal_inference = f"It seems {cause} might be related to {effect}."
                            self.store_information("Causal Inference", causal_inference)
            
            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):
        # Placeholder for a more advanced entity extraction using NLP techniques
        # This is a very basic example and should be replaced with a proper NER model
        words = text.split()
        entities = [word for word in words if word.isalpha() and word.istitle()]
        return entities

    def extract_relationships(self, text):
        # Placeholder for relationship extraction - this is a very basic example
        # Consider using dependency parsing or other NLP techniques for better results
        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 create_interface(self):
        def streaming_response(message, chat_history, image_filepath, math_ocr_image_path):
            
            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
                    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
                return

            full_response = ""
            updated_history = chat_history + [[message, ""]]

            try:
                for chunk in response_stream:
                    if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
                        chunk_content = chunk.choices[0].delta.content
                        full_response += chunk_content
                        
                        updated_history[-1][1] = full_response
                        yield "", updated_history, 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
                return

            full_response = self.adjust_response_based_on_state(full_response)

            self.update_goals(message)

            # Update internal state based on user input (more nuanced)
            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})
                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})
                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})
                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})
                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})
                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})
                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:]

        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 Upload Styling */
        .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;
        }
        /* Remove clear image buttons */
        .clear-button {
            display: none;
        }
        /* Animate chatbot messages */
        .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);
            }
        }
        /* Accordion Styling and Animation */
        .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; /* Adjust as needed */
        }
        /* Accordion Animation - Upwards */
        .gr-accordion {
            display: flex;
            flex-direction: column-reverse;
        }
        """

        with gr.Blocks(theme='soft', css=custom_css) as demo:
            with gr.Column():
                chatbot = gr.Chatbot(
                    label="Xylaria 1.5 Senoa",
                    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")

                btn.click(
                    fn=streaming_response,
                    inputs=[txt, chatbot, img, math_ocr_img],
                    outputs=[txt, chatbot, img, math_ocr_img]
                )
                txt.submit(
                    fn=streaming_response,
                    inputs=[txt, chatbot, img, math_ocr_img],
                    outputs=[txt, chatbot, img, math_ocr_img]
                )

                clear.click(
                    fn=lambda: None,
                    inputs=None,
                    outputs=[chatbot],
                    queue=False
                )

                clear_memory.click(
                    fn=self.reset_conversation,
                    inputs=None,
                    outputs=[chatbot],
                    queue=False
                )

                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()