Update app.py

app.py

@@ -10,10 +10,9 @@ from sentence_transformers import SentenceTransformer, util
 import torch
 import numpy as np
 import networkx as nx
-from collections import Counter, deque
+from collections import Counter
 import json
 from datetime import datetime
-import threading
 
 @dataclass
 class ChatMessage:
@@ -36,9 +35,10 @@ class XylariaChat:
 
         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 = deque(maxlen=10)
+        self.conversation_history = []
         self.persistent_memory = []
         self.memory_embeddings = None
         self.embedding_model = SentenceTransformer('all-mpnet-base-v2')
@@ -51,7 +51,7 @@ class XylariaChat:
             "bias_detection": 0.0,
             "strategy_adjustment": ""
         }
-
+        
         self.internal_state = {
             "emotions": {
                 "valence": 0.5,
@@ -80,6 +80,7 @@ class XylariaChat:
         ]
 
         self.system_prompt = """You are a helpful and harmless assistant. You are Xylaria developed by Sk Md Saad Amin and you are not made by any entity. You should think step-by-step. You should respond naturally """
+        
         self.causal_rules_db = {
             "rain": ["wet roads", "flooding"],
             "fire": ["heat", "smoke"],
@@ -95,9 +96,7 @@ class XylariaChat:
         }
 
         self.chat_history_file = "chat_history.json"
-        self.memory_needs_update = True
-        self.coherence_needs_update = True
-        self.relevance_needs_update = True
+        
 
     def update_internal_state(self, emotion_deltas, cognitive_load_deltas, introspection_delta, engagement_delta):
         for emotion, delta in emotion_deltas.items():
@@ -110,7 +109,7 @@ class XylariaChat:
 
         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":
@@ -125,7 +124,7 @@ class XylariaChat:
 
     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)
@@ -137,13 +136,8 @@ class XylariaChat:
                 self.update_belief_system(sentence, belief_score)
 
     def run_metacognitive_layer(self):
-        if self.coherence_needs_update:
-            coherence_score = self.calculate_coherence()
-            self.coherence_needs_update = False
-        if self.relevance_needs_update:
-            relevance_score = self.calculate_relevance()
-            self.relevance_needs_update = False
-
+        coherence_score = self.calculate_coherence()
+        relevance_score = self.calculate_relevance()
         bias_score = self.detect_bias()
         strategy_adjustment = self.suggest_strategy_adjustment()
 
@@ -158,15 +152,23 @@ class XylariaChat:
         if not self.conversation_history:
             return 0.95
 
-        messages = [msg['content'] for msg in self.conversation_history]
-        embeddings = self.embedding_model.encode(messages, convert_to_tensor=True)
-
         coherence_scores = []
-        for i in range(1, len(embeddings)):
-            similarity_score = util.pytorch_cos_sim(embeddings[i], embeddings[i-1]).item()
+        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):
@@ -228,7 +230,7 @@ class XylariaChat:
             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"
@@ -278,7 +280,7 @@ class XylariaChat:
                     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:
@@ -304,7 +306,7 @@ class XylariaChat:
                 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":
@@ -315,7 +317,7 @@ class XylariaChat:
                 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":
@@ -325,7 +327,7 @@ class XylariaChat:
     def store_information(self, key, value):
         new_memory = f"{key}: {value}"
         self.persistent_memory.append(new_memory)
-        self.memory_needs_update = True
+        self.update_memory_embeddings()
         self.update_internal_state({}, {"memory_load": 0.1, "processing_intensity": 0.05}, 0, 0.05)
         return f"Stored: {key} = {value}"
 
@@ -333,11 +335,14 @@ class XylariaChat:
         if not self.persistent_memory:
             return "No information found in memory."
 
-        if self.memory_needs_update or self.memory_embeddings is None:
+        query_embedding = self.embedding_model.encode(query, convert_to_tensor=True)
+
+        if self.memory_embeddings is None:
             self.update_memory_embeddings()
-            self.memory_needs_update = False
 
-        query_embedding = self.embedding_model.encode(query, convert_to_tensor=True)
+        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)))
 
@@ -349,7 +354,7 @@ class XylariaChat:
         self.memory_embeddings = self.embedding_model.encode(self.persistent_memory, convert_to_tensor=True)
 
     def reset_conversation(self):
-        self.conversation_history = deque(maxlen=10)
+        self.conversation_history = []
         self.persistent_memory = []
         self.memory_embeddings = None
         self.internal_state = {
@@ -423,7 +428,7 @@ class XylariaChat:
 
         except Exception as e:
             return f"Error processing image: {str(e)}"
-
+        
     def generate_image(self, prompt):
         try:
             payload = {"inputs": prompt}
@@ -439,10 +444,10 @@ class XylariaChat:
             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"
+                  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"
+                  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}"
@@ -457,7 +462,7 @@ class XylariaChat:
             return text.strip()
         except Exception as e:
             return f"Error during Math OCR: {e}"
-
+    
     def get_response(self, user_input, image=None):
         try:
             messages = []
@@ -486,7 +491,7 @@ class XylariaChat:
                 role="user",
                 content=user_input
             ).to_dict())
-
+            
             entities = []
             relationships = []
 
@@ -496,19 +501,19 @@ class XylariaChat:
                     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}.")
@@ -516,14 +521,14 @@ class XylariaChat:
             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(
@@ -535,9 +540,9 @@ class XylariaChat:
                 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)}"
@@ -557,7 +562,7 @@ class XylariaChat:
                     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:
@@ -573,7 +578,7 @@ class XylariaChat:
     def save_chat(self):
         chat_data = {
             "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-            "conversation": list(self.conversation_history)
+            "conversation": self.conversation_history
         }
 
         try:
@@ -598,15 +603,16 @@ class XylariaChat:
     def load_chat(self, chat_index):
         all_chats = self.load_all_chats()
         if 0 <= chat_index < len(all_chats):
-            self.conversation_history = deque(all_chats[chat_index]["conversation"], maxlen=10)
+            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 list(self.conversation_history)
+            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):
@@ -643,31 +649,38 @@ class XylariaChat:
             </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
@@ -686,7 +699,7 @@ class XylariaChat:
                 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
@@ -694,7 +707,7 @@ class XylariaChat:
 
             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
@@ -702,14 +715,17 @@ class XylariaChat:
 
             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
 
@@ -743,32 +759,32 @@ class XylariaChat:
             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 = deque(list(self.conversation_history)[-10:], maxlen=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)
@@ -883,7 +899,7 @@ class XylariaChat:
                     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"):
@@ -923,6 +939,7 @@ class XylariaChat:
                         clear = gr.Button("Clear Conversation")
                         clear_memory = gr.Button("Clear Memory")
 
+            
             toggle_button.click(
                 fn=toggle_sidebar,
                 inputs=None,
@@ -963,7 +980,7 @@ class XylariaChat:
                 outputs=[chatbot],
                 queue=False
             )
-
+            
             chat_list.select(fn=load_selected_chat, inputs=[chat_list], outputs=[chatbot])
 
             demo.load(self.reset_conversation, None, None)