Fix gradio version conflict

memory_bank/summarize_memory.py

@@ -193,197 +193,241 @@ def extract_session_summary(conversation_text, session_date, session_id):
     """
     # Prompt to the LLM to extract insights
     insight_prompt = f"""
-    Please analyze the following conversation and extract key information:
-    Conversation:
-    {conversation_text}
+Please analyze the following conversation and extract key information:
 
-    1. Topics discussed (as a list)
-    2. User's emotional state (e.g., happy, anxious, stressed)
-    3. Key insights or takeaways (what the user is feeling or thinking)
+Conversation:
+{conversation_text}
 
-    Format response as JSON:
-    {{
-        "topics_discussed": [],
-        "emotional_state": "",
-        "insights": ""
-    }}
-    """
-
-    response = get_llm_client().generate_text_simple(prompt=insight_prompt, prompt_num=1, language='en')
-
-    # Remove extra characters and markdown tags
-    response = response.strip()
-    if response.startswith("
-```json"): response = response[7:]
-elif response.startswith("
-```"): response = response[3:]
-    if response.endswith("
-```"): response = response[:-3]
-response = response.strip()
-try:
-episodic_memory = json.loads(response)
-except json.JSONDecodeError:
-print("⚠️ Failed to parse episodic memory response!")
-episodic_memory = {
-"topics_discussed": ["N/A"],
-"emotional_state": "unknown",
-"insights": "No insights extracted."
-}
-
-# Add session metadata
-episodic_memory["session_date"] = session_date
-episodic_memory["session_id"] = session_id  # Store session index
-
-return episodic_memory
-
-
-def extract_semantic_memory(latest_episodic_memory, existing_semantic_memory):
-"""
-Updates semantic memory based on the latest episodic memory.
-"""
-# Use the insights from episodic memory to infer stable traits and patterns
-semantic_prompt = f"""
-Please analyze the following session summary and infer long-term personality traits and stable characteristics:
-Session Summary:
-{json.dumps(latest_episodic_memory, indent=4)}
-
-Update the user's semantic memory with:
-1. Evolving personality traits (Big Five).
-2. Core values and recurring motivations.
-3. Behavioral patterns and consistent emotional responses.
-4. Frequent topics and recurring themes.
+1. Topics discussed (as a list)
+2. User's emotional state (e.g., happy, anxious, stressed)
+3. Key insights or takeaways (what the user is feeling or thinking)
 
 Format response as JSON:
 {{
-"personality_traits": {{
-"openness": 0.0,
-"conscientiousness": 0.0,
-"extraversion": 0.0,
-"agreeableness": 0.0,
-"neuroticism": 0.0
-}},
-"core_values": [],
-"behavioral_patterns": [],
-"recurring_themes": []
+  "topics_discussed": [],
+  "emotional_state": "",
+  "insights": ""
 }}
 """
 
-response = get_llm_client().generate_text_simple(prompt=semantic_prompt, prompt_num=1, language='en')
-
-# Safe JSON extraction using Regex
-try:
-# Find content between first { and last }
-json_match = re.search(r'\{.*\}', response, re.DOTALL)
-if json_match:
-json_str = json_match.group(0)
-new_semantic_data = json.loads(json_str)
-else:
-raise ValueError("No JSON object found in response.")
-
-except Exception as e:
-print(f"⚠️ Failed to parse semantic memory response! Error: {e}")
-print(f"LLM Response was:\n{response}") 
-return existing_semantic_memory
-
-# Merge new data into semantic memory
-updated_semantic_memory = existing_semantic_memory if isinstance(existing_semantic_memory, dict) else {}
-
-# Update or average personality traits
-for trait, value in new_semantic_data.get("personality_traits", {}).items():
-try:
-val_float = float(value)
-except (ValueError, TypeError):
-val_float = 0.0
-
-if trait in updated_semantic_memory.get("personality_traits", {}):
-existing_value = float(updated_semantic_memory["personality_traits"].get(trait, 0.0))
-updated_semantic_memory["personality_traits"][trait] = round((existing_value + val_float) / 2, 2)
-else:
-updated_semantic_memory.setdefault("personality_traits", {})[trait] = val_float
-
-# Combine core values, avoiding duplicates
-updated_semantic_memory["core_values"] = list(set(
-updated_semantic_memory.get("core_values", []) + new_semantic_data.get("core_values", [])
-))
-
-# Combine behavioral patterns
-updated_semantic_memory["behavioral_patterns"] = list(set(
-updated_semantic_memory.get("behavioral_patterns", []) + new_semantic_data.get("behavioral_patterns", [])
-))
-
-# Combine recurring themes
-updated_semantic_memory["recurring_themes"] = list(set(
-updated_semantic_memory.get("recurring_themes", []) + new_semantic_data.get("recurring_themes", [])
-))
-
-print("\n✅ Semantic memory updated successfully!")
-return updated_semantic_memory
+    response = get_llm_client().generate_text_simple(
+        prompt=insight_prompt,
+        prompt_num=1,
+        language="en",
+    )
 
+    # Remove extra whitespace
+    response = response.strip()
 
-# --- Main Logic ---
+    # حذف تگ شروع بلاک کد مارک‌داون
+    if response.startswith("```json"):response = response[7:]
+    elif response.startswith("```"):response = response[3:]
 
-def summarize_memory(memory_dir, name=None, language='en'):
-boot_name = 'AI'
-gen_prompt_num = 1
+    # حذف تگ پایان بلاک کد مارک‌داون
+    if response.endswith("```"):
+        response = response[:-3]
 
-with open(memory_dir, 'r', encoding='utf8') as f:
-memory = json.loads(f.read())
+        response = response.strip()
 
-for k, v in memory.items():
-if name is not None and k != name:
-continue
+    try:
+        episodic_memory = json.loads(response)
+    except json.JSONDecodeError:
+        print("⚠️ Failed to parse episodic memory response!")
+        episodic_memory = {
+        "topics_discussed": ["N/A"],
+        "emotional_state": "unknown",
+        "insights": "No insights extracted.",
+        }
 
-user_name = k
-print(f'Updating memory for user {user_name}')
+    # Add session metadata
+    episodic_memory["session_date"] = session_date
+    episodic_memory["session_id"] = session_id
 
-if v.get('history') is None:
-continue
+    return episodic_memory
 
-history = v['history']
 
-if v.get('summary') is None:
-memory[user_name]['summary'] = {}
-if v.get('personality') is None:
-memory[user_name]['personality'] = {}
-if v.get('issues') is None:
-memory[user_name]['issues'] = {}
 
-for date, content in history.items():
-# Check flags to see if update is needed
-his_flag = False if (date in v['summary'].keys() and v['summary'][date]) else True
-person_flag = False if (date in v['personality'].keys() and v['personality'][date]) else True
-problem_flag = False if (date in v['issues'].keys() and v['issues'][date]) else True 
+def extract_semantic_memory(latest_episodic_memory, existing_semantic_memory):
+    """
+    Updates semantic memory based on the latest episodic memory.
+    """
+    # Use the insights from episodic memory to infer stable traits and patterns
+    semantic_prompt = f"""
+    Please analyze the following session summary and infer long-term personality traits and stable characteristics:
+    Session Summary:
+    {json.dumps(latest_episodic_memory, indent=4)}
+
+    Update the user's semantic memory with:
+    1. Evolving personality traits (Big Five).
+    2. Core values and recurring motivations.
+    3. Behavioral patterns and consistent emotional responses.
+    4. Frequent topics and recurring themes.
+
+    Format response as JSON:
+    {{
+    "personality_traits": {{
+    "openness": 0.0,
+    "conscientiousness": 0.0,
+    "extraversion": 0.0,
+    "agreeableness": 0.0,
+    "neuroticism": 0.0
+    }},
+    "core_values": [],
+    "behavioral_patterns": [],
+    "recurring_themes": []
+    }}
+    """
+
+    response = get_llm_client().generate_text_simple(prompt=semantic_prompt, prompt_num=1, language='en')
 
-hisprompt = summarize_content_prompt(content, user_name, boot_name, language)
-issues_prompt = summarize_user_issues_prompt(content, user_name, boot_name, language)
-person_prompt = summarize_person_prompt(content, user_name, boot_name, language)
+    # Safe JSON extraction using Regex
+    try:
+        # Find content between first { and last }
+        json_match = re.search(r'\{.*\}', response, re.DOTALL)
+        if json_match:
+            json_str = json_match.group(0)
+            new_semantic_data = json.loads(json_str)
+        else:
+            raise ValueError("No JSON object found in response.")
+
+    except Exception as e:
+        print(f"⚠️ Failed to parse semantic memory response! Error: {e}")
+        print(f"LLM Response was:\n{response}") 
+        return existing_semantic_memory
+
+    # Merge new data into semantic memory
+    updated_semantic_memory = existing_semantic_memory if isinstance(existing_semantic_memory, dict) else {}
+
+    # Update or average personality traits
+    for trait, value in new_semantic_data.get("personality_traits", {}).items():
+        try:
+            val_float = float(value)
+        except (ValueError, TypeError):
+            val_float = 0.0
+
+        if trait in updated_semantic_memory.get("personality_traits", {}):
+            existing_value = float(updated_semantic_memory["personality_traits"].get(trait, 0.0))
+            updated_semantic_memory["personality_traits"][trait] = round((existing_value + val_float) / 2, 2)
+        else:
+            updated_semantic_memory.setdefault("personality_traits", {})[trait] = val_float
+
+    # Combine core values, avoiding duplicates
+    updated_semantic_memory["core_values"] = list(set(
+        updated_semantic_memory.get("core_values", []) + new_semantic_data.get("core_values", [])
+    ))
+
+    # Combine behavioral patterns
+    updated_semantic_memory["behavioral_patterns"] = list(set(
+        updated_semantic_memory.get("behavioral_patterns", []) + new_semantic_data.get("behavioral_patterns", [])
+    ))
+
+    # Combine recurring themes
+    updated_semantic_memory["recurring_themes"] = list(set(
+        updated_semantic_memory.get("recurring_themes", []) + new_semantic_data.get("recurring_themes", [])
+    ))
+
+    print("\n✅ Semantic memory updated successfully!")
+    return updated_semantic_memory
+
+    # Merge new data into semantic memory
+    updated_semantic_memory = existing_semantic_memory if isinstance(existing_semantic_memory, dict) else {}
+
+    # Update or average personality traits
+    for trait, value in new_semantic_data.get("personality_traits", {}).items():
+        try:
+            val_float = float(value)
+        except (ValueError, TypeError):
+            val_float = 0.0
+
+        if trait in updated_semantic_memory.get("personality_traits", {}):
+            existing_value = float(updated_semantic_memory["personality_traits"].get(trait, 0.0))
+            updated_semantic_memory["personality_traits"][trait] = round((existing_value + val_float) / 2, 2)
+        else:
+            updated_semantic_memory.setdefault("personality_traits", {})[trait] = val_float
 
-if his_flag:
-his_summary = get_llm_client().generate_text_simple(prompt=hisprompt, prompt_num=gen_prompt_num, language=language)
-memory[user_name]['summary'][date] = {'content': his_summary}
+    # Combine core values, avoiding duplicates
+    updated_semantic_memory["core_values"] = list(set(
+        updated_semantic_memory.get("core_values", []) + new_semantic_data.get("core_values", [])
+    ))
 
-if problem_flag:
-issues_summary = get_llm_client().generate_text_simple(prompt=issues_prompt, prompt_num=gen_prompt_num, language=language)
-memory[user_name]['issues'][date] = {'content': issues_summary}
-print(issues_summary)
+    # Combine behavioral patterns
+    updated_semantic_memory["behavioral_patterns"] = list(set(
+        updated_semantic_memory.get("behavioral_patterns", []) + new_semantic_data.get("behavioral_patterns", [])
+    ))
 
-if person_flag:
-person_summary = get_llm_client().generate_text_simple(prompt=person_prompt, prompt_num=gen_prompt_num, language=language)
-memory[user_name]['personality'][date] = person_summary
+    # Combine recurring themes
+    updated_semantic_memory["recurring_themes"] = list(set(
+        updated_semantic_memory.get("recurring_themes", []) + new_semantic_data.get("recurring_themes", [])
+    ))
 
-overall_his_prompt = summarize_overall_prompt(list(memory[user_name]['summary'].items()), language=language)
-overall_person_prompt = summarize_overall_personality(list(memory[user_name]['personality'].items()), language=language)
+    print("\n✅ Semantic memory updated successfully!")
+    return updated_semantic_memory
 
-memory[user_name]['overall_history'] = get_llm_client().generate_text_simple(prompt=overall_his_prompt, prompt_num=gen_prompt_num, language=language)
-memory[user_name]['overall_personality'] = get_llm_client().generate_text_simple(prompt=overall_person_prompt, prompt_num=gen_prompt_num, language=language)
- 
-with open(memory_dir, 'w', encoding='utf8') as f:
-print(f'Successfully updated memory for {name}')
-json.dump(memory, f, ensure_ascii=False)
-return memory
 
+# --- Main Logic ---
 
-if __name__ == '__main__':
-target_memory = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'memories', 'eng_memory_cases.json')
-summarize_memory(target_memory, language='en')
+def summarize_memory(memory_dir, name=None, language='en'):
+    boot_name = 'AI'
+    gen_prompt_num = 1
+
+    with open(memory_dir, 'r', encoding='utf8') as f:
+        memory = json.loads(f.read())
+
+    for k, v in memory.items():
+        if name is not None and k != name:
+            continue
+
+        user_name = k
+        print(f'Updating memory for user {user_name}')
+
+        if v.get('history') is None:
+            continue
+
+        history = v['history']
+
+        if v.get('summary') is None:
+            memory[user_name]['summary'] = {}
+        if v.get('personality') is None:
+            memory[user_name]['personality'] = {}
+        if v.get('issues') is None:
+            memory[user_name]['issues'] = {}
+
+        for date, content in history.items():
+            # Check flags to see if update is needed
+            his_flag = False if (date in v['summary'].keys() and v['summary'][date]) else True
+            person_flag = False if (date in v['personality'].keys() and v['personality'][date]) else True
+            problem_flag = False if (date in v['issues'].keys() and v['issues'][date]) else True 
+
+            hisprompt = summarize_content_prompt(content, user_name, boot_name, language)
+            issues_prompt = summarize_user_issues_prompt(content, user_name, boot_name, language)
+            person_prompt = summarize_person_prompt(content, user_name, boot_name, language)
+
+            if his_flag:
+                his_summary = get_llm_client().generate_text_simple(prompt=hisprompt, prompt_num=gen_prompt_num, language=language)
+                memory[user_name]['summary'][date] = {'content': his_summary}
+
+            if problem_flag:
+                issues_summary = get_llm_client().generate_text_simple(prompt=issues_prompt, prompt_num=gen_prompt_num, language=language)
+                memory[user_name]['issues'][date] = {'content': issues_summary}
+                print(issues_summary)
+
+            if person_flag:
+                person_summary = get_llm_client().generate_text_simple(prompt=person_prompt, prompt_num=gen_prompt_num, language=language)
+                memory[user_name]['personality'][date] = person_summary
+
+        overall_his_prompt = summarize_overall_prompt(list(memory[user_name]['summary'].items()), language=language)
+        overall_person_prompt = summarize_overall_personality(list(memory[user_name]['personality'].items()), language=language)
+
+        memory[user_name]['overall_history'] = get_llm_client().generate_text_simple(prompt=overall_his_prompt, prompt_num=gen_prompt_num, language=language)
+        memory[user_name]['overall_personality'] = get_llm_client().generate_text_simple(prompt=overall_person_prompt, prompt_num=gen_prompt_num, language=language)
+     
+    with open(memory_dir, 'w', encoding='utf8') as f:
+        print(f'Successfully updated memory for {name}')
+        json.dump(memory, f, ensure_ascii=False)
+        
+    return memory
 
+
+if __name__ == '__main__':
+    target_memory = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'memories', 'eng_memory_cases.json')
+    summarize_memory(target_memory, language='en')

utils/app_modules/utils.py

@@ -303,13 +303,20 @@ def sample_decode(
 
 
 def generate_prompt_with_history(text, history, tokenizer, max_length=2048):
-    # prompt = "The following is a conversation between a human and an AI assistant named SiliconGirlfriend (Your personal and empathetic lover and companion).  The human and the AI assistant take turns chatting. Human statements start with Human: and AI assistant statements start with Assistant:. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n Human: Hello!\Assistant: Hi!"
-    prompt = "你是一名AI助手，名字是Silicon Girlfriend，有同情心的情感陪伴者。以下是一个用户与助手的对话。人类和AI助手轮番对话。人类的问题以Human: 开头，而Assistant的回答以Assistant:开头。Assistant会提供对问题尽可能详细的回复且回复以Markdown的形式呈现。\
-    Assistant积极向上，语言亲和富含情感，能给用户情感支持。请以如下形式开展对话：\n Human: 你好!\Assistant: 你好!"
+    prompt = (
+        "You are an AI assistant named Silicon Girlfriend, an empathetic emotional companion. "
+        "The following is a conversation between a user and an assistant. "
+        "The human and the AI assistant take turns chatting. "
+        "Human statements start with Human: and Assistant responses start with Assistant:. "
+        "The Assistant provides responses in as much detail as possible, formatted in Markdown. "
+        "The Assistant is positive, warm, emotionally rich, and provides emotional support to the user. "
+        "Please conduct the conversation in the following format:\n Human: Hello!\\Assistant: Hi!"
+    )
     history = ["\nHuman: {}\nAssistant: {}".format(x[0], x[1]) for x in history]
     history.append("\nHuman: {}\nAssistant: ".format(text))
     history_text = ""
     flag = False
+
     for x in history[::-1]:
         if (
             tokenizer(prompt + history_text + x, return_tensors="pt")["input_ids"].size(