Delete app.py

app.py

@@ -1,617 +0,0 @@
-import os
-import json
-import shutil
-import gradio as gr
-import tempfile
-from datetime import datetime
-from typing import List, Dict, Any, Optional
-from pytube import YouTube
-from pathlib import Path
-import re
-import pandas as pd
-
-# --- Agent Imports ---
-try:
-    from alz_companion.agent import (
-        bootstrap_vectorstore, make_rag_chain, answer_query, synthesize_tts,
-        transcribe_audio, detect_tags_from_query, describe_image, build_or_load_vectorstore,
-        _default_embeddings, route_query_type, call_llm
-    )
-    from alz_companion.prompts import (
-        BEHAVIOUR_TAGS, EMOTION_STYLES, FAITHFULNESS_JUDGE_PROMPT
-    )
-    from langchain.schema import Document
-    from langchain_community.vectorstores import FAISS
-    AGENT_OK = True
-except Exception as e:
-    AGENT_OK = False
-    class Document:
-        def __init__(self, page_content, metadata): self.page_content, self.metadata = page_content, metadata
-    class FAISS:
-        def __init__(self):
-            self.docstore = type('obj', (object,), {'_dict': {}})()
-        def add_documents(self, docs):
-            start_idx = len(self.docstore._dict)
-            for i, d in enumerate(docs, start_idx):
-                self.docstore._dict[i] = d
-        def save_local(self, path): pass
-        @classmethod
-        def from_documents(cls, docs, embeddings=None):
-            inst = cls()
-            inst.add_documents(docs)
-            return inst
-    def build_or_load_vectorstore(docs, index_path, is_personal=False): return FAISS.from_documents(docs or [], embeddings=None)
-    def bootstrap_vectorstore(sample_paths=None, index_path="data/"): return object()
-    def make_rag_chain(vs_general, vs_personal, **kwargs): return lambda q, **k: {"answer": f"(Demo) You asked: {q}", "sources": []}
-    def answer_query(chain, q, **kwargs): return chain(q, **kwargs)
-    def synthesize_tts(text: str, lang: str = "en"): return None
-    def transcribe_audio(filepath: str, lang: str = "en"): return "This is a transcribed message."
-    def detect_tags_from_query(*args, **kwargs): return {"detected_behavior": "None", "detected_emotion": "None"}
-    def describe_image(image_path: str): return "This is a description of an image."
-    def _default_embeddings(): return None
-    def route_query_type(query: str): return "general_conversation"
-    def call_llm(messages, **kwargs): return "Cannot call LLM in fallback mode."
-    BEHAVIOUR_TAGS, EMOTION_STYLES, FAITHFULNESS_JUDGE_PROMPT = {"None": []}, {"None": {}}, ""
-    print(f"WARNING: Could not import from alz_companion ({e}). Running in UI-only demo mode.")
-    
-
-# --- NEW: Import for Evaluation Logic ---
-try:
-    from evaluate import load_test_fixtures, run_comprehensive_evaluation
-except ImportError:
-    # Fallback if evaluate.py is not found
-    def load_test_fixtures(): print("WARNING: evaluate.py not found.")
-    def run_comprehensive_evaluation(*args, **kwargs): return "Evaluation module not found.", []
-
-
-# --- Centralized Configuration ---
-CONFIG = {
-    "themes": ["All", "The Father", "Still Alice", "Away from Her", "Alive Inside", "General Caregiving"],
-    "roles": ["patient", "caregiver"],
-    "disease_stages": ["Default: Mild Stage",  "Moderate Stage", "Advanced Stage"],
-    "behavior_tags": ["None"] + list(BEHAVIOUR_TAGS.keys()),
-    "emotion_tags": ["None"] + list(EMOTION_STYLES.keys()),
-    "topic_tags": ["None", "caregiving_advice", "medical_fact", "personal_story", "research_update", "treatment_option:home_safety", "treatment_option:long_term_care", "treatment_option:music_therapy", "treatment_option:reassurance", "treatment_option:routine_structuring", "treatment_option:validation_therapy"],
-    "context_tags": ["None", "disease_stage_mild", "disease_stage_moderate", "disease_stage_advanced", "disease_stage_unspecified", "interaction_mode_one_to_one", "interaction_mode_small_group", "interaction_mode_group_activity", "relationship_family", "relationship_spouse", "relationship_staff_or_caregiver", "relationship_unspecified", "setting_home_or_community", "setting_care_home", "setting_clinic_or_hospital"],
-    "languages": {"English": "en", "Chinese": "zh", "Cantonese": "zh-yue", "Korean": "ko", "Japanese": "ja", "Malay": "ms", "French": "fr", "Spanish": "es", "Hindi": "hi", "Arabic": "ar"},
-    "tones": ["warm", "empathetic", "caring", "reassuring", "calm", "optimistic", "motivating", "neutral", "formal", "humorous"]
-}
-
-# --- File Management & Vector Store Logic ---
-def _storage_root() -> Path:
-    for p in [Path(os.getenv("SPACE_STORAGE", "")), Path("/data"), Path.home() / ".cache" / "alz_companion"]:
-        if not p: continue
-        try:
-            p.mkdir(parents=True, exist_ok=True)
-            (p / ".write_test").write_text("ok")
-            (p / ".write_test").unlink(missing_ok=True)
-            return p
-        except Exception: continue
-    tmp = Path(tempfile.gettempdir()) / "alz_companion"
-    tmp.mkdir(parents=True, exist_ok=True)
-    return tmp
-STORAGE_ROOT = _storage_root()
-INDEX_BASE = STORAGE_ROOT / "index"
-PERSONAL_DATA_BASE = STORAGE_ROOT / "personal"
-UPLOADS_BASE = INDEX_BASE / "uploads"
-PERSONAL_INDEX_PATH = str(PERSONAL_DATA_BASE / "personal_faiss_index")
-NLU_EXAMPLES_INDEX_PATH = str(INDEX_BASE / "nlu_examples_faiss_index")
-THEME_PATHS = {t: str(INDEX_BASE / f"faiss_index_{t.replace(' ', '').lower()}") for t in CONFIG["themes"]}
-os.makedirs(UPLOADS_BASE, exist_ok=True)
-os.makedirs(PERSONAL_DATA_BASE, exist_ok=True)
-for p in THEME_PATHS.values(): os.makedirs(p, exist_ok=True)
-vectorstores = {}
-personal_vectorstore = None
-nlu_vectorstore = None
-
-try:
-    personal_vectorstore = build_or_load_vectorstore([], PERSONAL_INDEX_PATH, is_personal=True)
-except Exception:
-    personal_vectorstore = None
-def bootstrap_nlu_vectorstore(example_file: str, index_path: str) -> FAISS:
-    if not os.path.exists(example_file):
-        print(f"WARNING: NLU example file not found at {example_file}. NLU will be less accurate.")
-        return build_or_load_vectorstore([], index_path)
-    docs = []
-    with open(example_file, "r", encoding="utf-8") as f:
-        for line in f:
-            try:
-                data = json.loads(line)
-                doc = Document(page_content=data["query"], metadata=data)
-                docs.append(doc)
-            except (json.JSONDecodeError, KeyError): continue
-    print(f"Found and loaded {len(docs)} NLU training examples.")
-    if os.path.exists(index_path): shutil.rmtree(index_path)
-    return build_or_load_vectorstore(docs, index_path)
-def canonical_theme(tk: str) -> str: return tk if tk in CONFIG["themes"] else "All"
-def theme_upload_dir(theme: str) -> str:
-    p = UPLOADS_BASE / f"theme_{canonical_theme(theme).replace(' ', '').lower()}"
-    p.mkdir(exist_ok=True)
-    return str(p)
-def load_manifest(theme: str) -> Dict[str, Any]:
-    p = os.path.join(theme_upload_dir(theme), "manifest.json")
-    if os.path.exists(p):
-        try:
-            with open(p, "r", encoding="utf-8") as f: return json.load(f)
-        except Exception: pass
-    return {"files": {}}
-def save_manifest(theme: str, man: Dict[str, Any]):
-    with open(os.path.join(theme_upload_dir(theme), "manifest.json"), "w", encoding="utf-8") as f: json.dump(man, f, indent=2)
-def list_theme_files(theme: str) -> List[tuple[str, bool]]:
-    man = load_manifest(theme)
-    base = theme_upload_dir(theme)
-    found = [(n, bool(e)) for n, e in man.get("files", {}).items() if os.path.exists(os.path.join(base, n))]
-    existing = {n for n, e in found}
-    for name in sorted(os.listdir(base)):
-        if name not in existing and os.path.isfile(os.path.join(base, name)): found.append((name, False))
-    man["files"] = dict(found)
-    save_manifest(theme, man)
-    return found
-def copy_into_theme(theme: str, src_path: str) -> str:
-    fname = os.path.basename(src_path)
-    dest = os.path.join(theme_upload_dir(theme), fname)
-    shutil.copy2(src_path, dest)
-    return dest
-def seed_files_into_theme(theme: str):
-    SEED_FILES = [("sample_data/caregiving_tips.txt", True), ("sample_data/the_father_segments_enriched_harmonized_plus.jsonl", True), ("sample_data/still_alice_enriched_harmonized_plus.jsonl", True), ("sample_data/away_from_her_enriched_harmonized_plus.jsonl", True), ("sample_data/alive_inside_enriched_harmonized.jsonl", True)]
-    man, changed = load_manifest(theme), False
-    for path, enable in SEED_FILES:
-        if not os.path.exists(path): continue
-        fname = os.path.basename(path)
-        if not os.path.exists(os.path.join(theme_upload_dir(theme), fname)):
-            copy_into_theme(theme, path)
-            man["files"][fname] = bool(enable)
-            changed = True
-    if changed: save_manifest(theme, man)
-def ensure_index(theme='All'):
-    theme = canonical_theme(theme)
-    if theme in vectorstores: return vectorstores[theme]
-    upload_dir = theme_upload_dir(theme)
-    enabled_files = [os.path.join(upload_dir, n) for n, enabled in list_theme_files(theme) if enabled]
-    index_path = THEME_PATHS.get(theme)
-    vectorstores[theme] = bootstrap_vectorstore(sample_paths=enabled_files, index_path=index_path)
-    return vectorstores[theme]
-
-# --- Gradio Callbacks ---
-# In app.py, modify the collect_settings function
-
-def collect_settings(*args):
-    keys = ["role", "patient_name", "caregiver_name", "tone", "language", "tts_lang", "temperature", 
-            # --- ADD "disease_stage" to this list ---
-            "disease_stage", 
-            "behaviour_tag", "emotion_tag", "topic_tag", "active_theme", "tts_on", "debug_mode"]
-    return dict(zip(keys, args))
-    
-
-# In app.py, replace the entire parse_and_tag_entries function.
-def parse_and_tag_entries(text_content: str, source: str, settings: dict = None) -> List[Document]:
-    docs_to_add = []
-    # This logic correctly handles both simple text and complex journal entries
-    entries = re.split(r'\n(?:---|--|-|-\*-|-\.-)\n', text_content)
-    if len(entries) == 1 and "title:" not in entries[0].lower() and "content:" not in entries[0].lower():
-        entries = [text_content] # Treat simple text as a single entry
-
-    for entry in entries:
-        if not entry.strip(): continue
-        
-        lines = entry.strip().split('\n')
-        title_line = lines[0].split(':', 1)
-        title = title_line[1].strip() if len(title_line) > 1 and "title:" in lines[0].lower() else "Untitled Text Entry"
-        content_part = "\n".join(lines[1:])
-        content = content_part.split(':', 1)[1].strip() if "content:" in content_part.lower() else content_part.strip() or entry.strip()
-
-        if not content: continue
-        
-        full_content = f"Title: {title}\n\nContent: {content}"
-        
-        detected_tags = detect_tags_from_query(
-            content, nlu_vectorstore=nlu_vectorstore, 
-            behavior_options=CONFIG["behavior_tags"], emotion_options=CONFIG["emotion_tags"],
-            topic_options=CONFIG["topic_tags"], context_options=CONFIG["context_tags"],
-            settings=settings
-        )
-        
-        metadata = {"source": source, "title": title}
-        
-        # --- START: CORRECTED METADATA ASSIGNMENT ---
-        if detected_tags.get("detected_behaviors"):
-            metadata["behaviors"] = [b.lower() for b in detected_tags["detected_behaviors"]]
-        detected_emotion = detected_tags.get("detected_emotion")
-        if detected_emotion and detected_emotion != "None":
-            metadata["emotion"] = detected_emotion.lower()
-        
-        # Correctly handle the plural "detected_topics" key and list value
-        detected_topics = detected_tags.get("detected_topics")
-        if detected_topics:
-            metadata["topic_tags"] = [t.lower() for t in detected_topics]
-            
-        if detected_tags.get("detected_contexts"):
-            metadata["context_tags"] = [c.lower() for c in detected_tags["detected_contexts"]]
-        # --- END: CORRECTED METADATA ASSIGNMENT ---
-        
-        docs_to_add.append(Document(page_content=full_content, metadata=metadata))
-
-    return docs_to_add
-
-
-def handle_add_knowledge(title, text_input, file_input, image_input, yt_url, settings):
-    global personal_vectorstore
-    docs_to_add = []
-    source, content = "Unknown", ""
-    if text_input and text_input.strip():
-        source, content = "Text Input", f"Title: {title or 'Untitled'}\n\nContent: {text_input}"
-    elif file_input:
-        source = os.path.basename(file_input.name)
-        if file_input.name.lower().endswith('.txt'):
-            with open(file_input.name, 'r', encoding='utf-8') as f: content = f.read()
-        else:
-            transcribed = transcribe_audio(file_input.name)
-            content = f"Title: {title or 'Audio/Video Note'}\n\nContent: {transcribed}"
-    elif image_input:
-        source, description = "Image Input", describe_image(image_input)
-        content = f"Title: {title or 'Image Note'}\n\nContent: {description}"
-    elif yt_url and ("youtube.com" in yt_url or "youtu.be" in yt_url):
-        try:
-            yt = YouTube(yt_url)
-            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_audio_file:
-                yt.streams.get_audio_only().download(filename=temp_audio_file.name)
-                transcribed = transcribe_audio(temp_audio_file.name)
-                os.remove(temp_audio_file.name)
-            source, content = f"YouTube: {yt.title}", f"Title: {title or yt.title}\n\nContent: {transcribed}"
-        except Exception as e:
-            return f"Error processing YouTube link: {e}"
-    else:
-        return "Please provide content to add."
-    if content:
-        docs_to_add = parse_and_tag_entries(content, source, settings=settings)
-    if not docs_to_add: return "No processable content found to add."
-    if personal_vectorstore is None:
-        personal_vectorstore = build_or_load_vectorstore(docs_to_add, PERSONAL_INDEX_PATH, is_personal=True)
-    else:
-        personal_vectorstore.add_documents(docs_to_add)
-    personal_vectorstore.save_local(PERSONAL_INDEX_PATH)
-    return f"Successfully added {len(docs_to_add)} new memory/memories."
-
-    
-# REPLACE your entire old chat_fn function with this new one
-
-def chat_fn(user_text, audio_file, settings, chat_history):
-    global personal_vectorstore
-    question = (user_text or "").strip()
-    if audio_file and not question:
-        try:
-            question = transcribe_audio(audio_file, lang=CONFIG["languages"].get(settings.get("tts_lang", "English"), "en"))
-        except Exception as e:
-            err_msg = f"Audio Error: {e}" if settings.get("debug_mode") else "Sorry, I couldn't understand the audio."
-            chat_history.append({"role": "assistant", "content": err_msg})
-            return "", None, chat_history
-            
-    if not question:
-        return "", None, chat_history
-
-    # --- START FIX 1: Correctly process the incoming chat_history (list of dicts) ---
-    # The incoming chat_history is already in the desired format for the API,
-    # we just need to filter out our special system messages (like sources).
-    api_chat_history = [
-        msg for msg in chat_history
-        if msg.get("content") and not msg["content"].strip().startswith("*(")
-    ]
-    
-    # Append the new user question to the history that will be displayed in the UI
-    chat_history.append({"role": "user", "content": question})
-    # --- END FIX 1 ---
-
-    query_type = route_query_type(question)
-    final_tags = { "scenario_tag": None, "emotion_tag": None, "topic_tag": None, "context_tags": [] }
-    manual_behavior = settings.get("behaviour_tag", "None")
-    manual_emotion = settings.get("emotion_tag", "None")
-    manual_topic = settings.get("topic_tag", "None")
-
-    auto_detected_context = ""
-    if not all(m == "None" for m in [manual_behavior, manual_emotion, manual_topic]):
-        final_tags["scenario_tag"] = manual_behavior if manual_behavior != "None" else None
-        final_tags["emotion_tag"] = manual_emotion if manual_emotion != "None" else None
-        final_tags["topic_tag"] = manual_topic if manual_topic != "None" else None
-    elif "caregiving_scenario" in query_type:
-        detected_tags = detect_tags_from_query(
-            question, nlu_vectorstore=nlu_vectorstore, behavior_options=CONFIG["behavior_tags"],
-            emotion_options=CONFIG["emotion_tags"], topic_options=CONFIG["topic_tags"],
-            context_options=CONFIG["context_tags"], settings=settings)
-        behaviors = detected_tags.get("detected_behaviors")
-        final_tags["scenario_tag"] = behaviors[0] if behaviors else None
-        final_tags["emotion_tag"] = detected_tags.get("detected_emotion")
-        final_tags["topic_tag"] = detected_tags.get("detected_topic")
-        final_tags["context_tags"] = detected_tags.get("detected_contexts", [])
-        detected_parts = [f"{k.split('_')[1]}=`{v}`" for k, v in final_tags.items() if v and v != "None" and v != []]
-        if detected_parts:
-            auto_detected_context = f"*(Auto-detected context: {', '.join(detected_parts)})*"
-            
-    vs_general = ensure_index(settings.get("active_theme", "All"))
-    if personal_vectorstore is None:
-        personal_vectorstore = build_or_load_vectorstore([], PERSONAL_INDEX_PATH, is_personal=True)
-        
-    # OLD rag_settings = {k: settings.get(k) for k in ["role", "temperature", "language", "patient_name", "caregiver_name", "tone"]}
-    # NEW add "disease_stage"
-    rag_settings = {k: settings.get(k) for k in ["role", "temperature", "language", "patient_name", "caregiver_name", "tone", "disease_stage"]}
-    chain = make_rag_chain(vs_general, personal_vectorstore, **rag_settings)
-    
-    response = answer_query(chain, question, query_type=query_type, chat_history=api_chat_history, **final_tags)
-    answer = response.get("answer", "[No answer found]")
-
-    # --- START FIX 2: Append new messages directly in the correct dictionary format ---
-    # 1. The main answer is appended first to appear at the top of the new messages.
-    chat_history.append({"role": "assistant", "content": answer})
-
-    # 2. Then, append the supplementary context and source information below the answer.
-    if auto_detected_context:
-        chat_history.append({"role": "assistant", "content": auto_detected_context})
-    if response.get("sources"):
-        chat_history.append({"role": "assistant", "content": f"*(Sources used: {', '.join(response['sources'])})*"})
-    
-    # chat_history.append({"role": "assistant", "content": answer})
-    # --- END FIX 2 ---
-
-    audio_out = None
-    if settings.get("tts_on") and answer:
-        audio_out = synthesize_tts(answer, lang=CONFIG["languages"].get(settings.get("tts_lang"), "en"))
-
-    # --- START FIX 3: The chat_history is now already in the correct format to be returned ---
-    return "", gr.update(value=audio_out, visible=bool(audio_out)), chat_history
-    # --- END FIX 3 ---
-    
-# The save_chat_to_memory function incorrectly assumes the history is 
-# a list of tuples, like [(True, "..."), (False, "...")]
-# However, The chat_fn function correctly builds the chat_history as 
-# a list of dictionaries, like this:
-# [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
-# To correctly parse the list of dictionaries.
-def save_chat_to_memory(chat_history):
-    if not chat_history:
-        return "Nothing to save."
-
-    # --- START: MODIFIED LOGIC ---
-    # Correctly processes the list of dictionaries from the chatbot
-    formatted_chat = [
-        f"{msg.get('role', 'assistant').capitalize()}: {msg.get('content', '').strip()}"
-        for msg in chat_history
-        if isinstance(msg, dict) and msg.get('content') and not msg.get('content', '').strip().startswith("*(")
-    ]
-    # --- END: MODIFIED LOGIC ---
-
-    if not formatted_chat:
-        return "No conversation to save."
-
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    title = f"Conversation from {timestamp}"
-    full_content = f"Title: {title}\n\nContent:\n" + "\n".join(formatted_chat)
-    doc = Document(page_content=full_content, metadata={"source": "Saved Chat", "title": title})
-
-    global personal_vectorstore
-    if personal_vectorstore is None:
-        personal_vectorstore = build_or_load_vectorstore([doc], PERSONAL_INDEX_PATH, is_personal=True)
-    else:
-        personal_vectorstore.add_documents([doc])
-    
-    personal_vectorstore.save_local(PERSONAL_INDEX_PATH)
-    return f"Conversation from {timestamp} saved."
-
-
-def list_personal_memories():
-    global personal_vectorstore
-    if personal_vectorstore is None or not hasattr(personal_vectorstore.docstore, '_dict') or not personal_vectorstore.docstore._dict:
-        return gr.update(value=[["No memories", "", ""]]), gr.update(choices=[], value=None)
-    docs = list(personal_vectorstore.docstore._dict.values())
-    return gr.update(value=[[d.metadata.get('title', '...'), d.metadata.get('source', '...'), d.page_content] for d in docs]), gr.update(choices=[d.page_content for d in docs])
-def delete_personal_memory(memory_to_delete):
-    global personal_vectorstore
-    if personal_vectorstore is None or not memory_to_delete: return "No memory selected."
-    all_docs = list(personal_vectorstore.docstore._dict.values())
-    docs_to_keep = [d for d in all_docs if d.page_content != memory_to_delete]
-    if len(all_docs) == len(docs_to_keep): return "Error: Could not find memory."
-    if not docs_to_keep:
-        if os.path.isdir(PERSONAL_INDEX_PATH): shutil.rmtree(PERSONAL_INDEX_PATH)
-        personal_vectorstore = build_or_load_vectorstore([], PERSONAL_INDEX_PATH, is_personal=True)
-    else:
-        new_vs = FAISS.from_documents(docs_to_keep, _default_embeddings())
-        new_vs.save_local(PERSONAL_INDEX_PATH)
-        personal_vectorstore = new_vs
-    return "Successfully deleted memory."
-
-# --- EVALUATION FUNCTIONS: move them into evaluate.py
-# def evaluate_nlu_tags(expected: Dict[str, Any], actual: Dict[str, Any], tag_key: str, expected_key_override: str = None) -> Dict[str, float]:
-# def _parse_judge_json(raw_str: str) -> dict | None:
-# def run_comprehensive_evaluation():
-
-def upload_knowledge(files, theme):
-    for f in files: copy_into_theme(theme, f.name)
-    if theme in vectorstores: del vectorstores[theme]
-    return f"Uploaded {len(files)} file(s)."
-def save_file_selection(theme, enabled):
-    man = load_manifest(theme)
-    for fname in man['files']: man['files'][fname] = fname in enabled
-    save_manifest(theme, man)
-    if theme in vectorstores: del vectorstores[theme]
-    return f"Settings saved for theme '{theme}'."
-def refresh_file_list_ui(theme):
-    files = list_theme_files(theme)
-    return gr.update(choices=[f for f, _ in files], value=[f for f, en in files if en]), f"Found {len(files)} file(s)."
-def auto_setup_on_load(theme):
-    if not os.listdir(theme_upload_dir(theme)): seed_files_into_theme(theme)
-    settings = collect_settings("patient", "", "", "warm", "English", "English", 0.7, "None", "None", "None", "All", True, False)
-    files_ui, status = refresh_file_list_ui(theme)
-    return settings, files_ui, status
-def test_save_file():
-    try:
-        path = PERSONAL_DATA_BASE / "persistence_test.txt"
-        path.write_text(f"File saved at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-        return f"✅ Success! Wrote test file to: {path}"
-    except Exception as e: return f"❌ Error! Failed to write file: {e}"
-def check_test_file():
-    path = PERSONAL_DATA_BASE / "persistence_test.txt"
-    if path.exists(): return f"✅ Success! Found test file. Contents: '{path.read_text()}'"
-    return f"❌ Failure. Test file not found at: {path}"
-
-# --- UI Definition ---
-CSS = """
-.gradio-container { font-size: 14px; } 
-#chatbot { min-height: 400px; } 
-#audio_in audio, #audio_out audio { max-height: 40px; } 
-#audio_in .waveform, #audio_out .waveform { display: none !important; }
-#audio_in, #audio_out { min-height: 0px !important; }
-"""
-with gr.Blocks(theme=gr.themes.Soft(), css=CSS) as demo:
-    settings_state = gr.State({})
-    with gr.Tab("Chat"):
-        with gr.Row():
-            user_text = gr.Textbox(show_label=False, placeholder="Type your message here...", scale=7)
-            submit_btn = gr.Button("Send", variant="primary", scale=1)
-        with gr.Row():
-            audio_in = gr.Audio(sources=["microphone"], type="filepath", label="Voice Input", elem_id="audio_in")
-            audio_out = gr.Audio(label="Response Audio", autoplay=True, visible=True, elem_id="audio_out")
-        
-        chatbot = gr.Chatbot(elem_id="chatbot", label="Conversation", type="messages")
-        chat_status = gr.Markdown()
-        with gr.Row():
-            clear_btn = gr.Button("Clear")
-            save_btn = gr.Button("Save to Memory")
-
-    with gr.Tab("Personalize"):
-        with gr.Accordion("Add to Personal Knowledge Base", open=True):
-            personal_title = gr.Textbox(label="Title")
-            personal_text = gr.Textbox(lines=5, label="Text Content")
-            with gr.Row():
-                personal_file = gr.File(label="Upload Audio/Video/Text File")
-                personal_image = gr.Image(type="filepath", label="Upload Image")
-            personal_yt_url = gr.Textbox(label="Or, provide a YouTube URL")
-            personal_add_btn = gr.Button("Add Knowledge", variant="primary")
-            personal_status = gr.Markdown()
-        gr.Markdown("### **Manage Personal Knowledge**")
-        with gr.Accordion("View/Hide Details", open=False):
-            personal_memory_display = gr.DataFrame(headers=["Title", "Source", "Content"], label="Saved Memories", row_count=(5, "dynamic"))
-            personal_refresh_btn = gr.Button("Refresh Memories")
-            personal_delete_selector = gr.Dropdown(label="Select memory to delete", scale=3, interactive=True)
-            personal_delete_btn = gr.Button("Delete Selected", variant="stop", scale=1)
-            personal_delete_status = gr.Markdown()
-
-    with gr.Tab("Settings"):
-        with gr.Group():
-            gr.Markdown("## Conversation & Persona Settings")
-            with gr.Row():
-                role = gr.Radio(CONFIG["roles"], value="patient", label="Your Role")
-                patient_name = gr.Textbox(label="Patient's Name")
-                caregiver_name = gr.Textbox(label="Caregiver's Name")
-            with gr.Row():
-                temperature = gr.Slider(0.0, 1.2, value=0.7, step=0.1, label="Creativity")
-                tone = gr.Dropdown(CONFIG["tones"], value="warm", label="Response Tone")
-            with gr.Row():
-                # --- ADD THIS NEW DROPDOWN ---
-                # disease_stage = gr.Dropdown(CONFIG["disease_stages"], value="Normal / Unspecified", label="Assumed Disease Stage")
-                disease_stage = gr.Dropdown(CONFIG["disease_stages"], value="Default: Mild Stage", label="Assumed Disease Stage")
-                # --- END OF ADDITION ---
-                behaviour_tag = gr.Dropdown(CONFIG["behavior_tags"], value="None", label="Behaviour Filter (Manual)")
-                emotion_tag = gr.Dropdown(CONFIG["emotion_tags"], value="None", label="Emotion Filter (Manual)")
-                topic_tag = gr.Dropdown(CONFIG["topic_tags"], value="None", label="Topic Tag Filter (Manual)")
-        with gr.Accordion("Language, Voice & Debugging", open=False):
-            language = gr.Dropdown(list(CONFIG["languages"].keys()), value="English", label="Response Language")
-            tts_lang = gr.Dropdown(list(CONFIG["languages"].keys()), value="English", label="Voice Language")
-            tts_on = gr.Checkbox(True, label="Enable Voice Response")
-            debug_mode = gr.Checkbox(False, label="Show Debug Info")
-        gr.Markdown("--- \n ## General Knowledge Base Management")
-        with gr.Row():
-            with gr.Column(scale=1):
-                files_in = gr.File(file_count="multiple", file_types=[".jsonl", ".txt"], label="Upload Knowledge Files")
-                upload_btn = gr.Button("Upload to Theme")
-                seed_btn = gr.Button("Import Sample Data")
-                mgmt_status = gr.Markdown()
-            with gr.Column(scale=2):
-                active_theme = gr.Radio(CONFIG["themes"], value="All", label="Active Knowledge Theme")
-                files_box = gr.CheckboxGroup(choices=[], label="Enable Files for Selected Theme")
-                with gr.Row():
-                    save_files_btn = gr.Button("Save Selection", variant="primary")
-                    refresh_btn = gr.Button("Refresh List")
-        with gr.Accordion("Persistence Test", open=False):
-            test_save_btn = gr.Button("1. Run Persistence Test (Save File)")
-            check_save_btn = gr.Button("3. Check for Test File")
-            test_status = gr.Markdown()
-  
-    # --- UPDATED TESTING TAB ---
-    with gr.Tab("Testing"):
-        gr.Markdown("## Comprehensive Performance Evaluation")
-        gr.Markdown("Click the button below to run a full evaluation on all test fixtures. This will test NLU (Routing & Tagging) and generate RAG responses for manual review.")
-        
-        run_comprehensive_btn = gr.Button("Run Comprehensive Evaluation", variant="primary")
-        
-        batch_summary_md = gr.Markdown("### Evaluation Summary: Not yet run.")
-        
-        comprehensive_results_df = gr.DataFrame(
-            label="Detailed Evaluation Results", 
-            elem_id="comprehensive_results_df",
-            headers=[
-                "Test ID","Title","Route Correct?","Expected Route","Actual Route",
-                "Behavior F1","Emotion F1","Topic F1","Context F1",
-                "Generated Answer","Sources","Source Count","Latency (ms)", "Faithfulness"
-            ],
-            interactive=False
-        )
-        
-
-    # --- Event Wiring ---
-    all_settings = [role, patient_name, caregiver_name, tone, language, tts_lang, temperature, 
-                    disease_stage, behaviour_tag, emotion_tag, topic_tag, active_theme, tts_on, debug_mode]
-    
-    for c in all_settings: c.change(fn=collect_settings, inputs=all_settings, outputs=settings_state)
-    submit_btn.click(fn=chat_fn, inputs=[user_text, audio_in, settings_state, chatbot], outputs=[user_text, audio_out, chatbot])
-    save_btn.click(fn=save_chat_to_memory, inputs=[chatbot], outputs=[chat_status])
-    clear_btn.click(lambda: (None, None, [], None, "", ""), outputs=[user_text, audio_out, chatbot, audio_in, user_text, chat_status])
-    personal_add_btn.click(fn=handle_add_knowledge, inputs=[personal_title, personal_text, personal_file, personal_image, personal_yt_url, settings_state], outputs=[personal_status]).then(lambda: (None, None, None, None, None), outputs=[personal_title, personal_text, personal_file, personal_image, personal_yt_url])
-    personal_refresh_btn.click(fn=list_personal_memories, inputs=None, outputs=[personal_memory_display, personal_delete_selector])
-    personal_delete_btn.click(fn=delete_personal_memory, inputs=[personal_delete_selector], outputs=[personal_delete_status]).then(fn=list_personal_memories, inputs=None, outputs=[personal_memory_display, personal_delete_selector])
-    upload_btn.click(upload_knowledge, inputs=[files_in, active_theme], outputs=[mgmt_status]).then(refresh_file_list_ui, inputs=[active_theme], outputs=[files_box, mgmt_status])
-    save_files_btn.click(save_file_selection, inputs=[active_theme, files_box], outputs=[mgmt_status])
-    seed_btn.click(seed_files_into_theme, inputs=[active_theme]).then(refresh_file_list_ui, inputs=[active_theme], outputs=[files_box, mgmt_status])
-    refresh_btn.click(refresh_file_list_ui, inputs=[active_theme], outputs=[files_box, mgmt_status])
-    active_theme.change(refresh_file_list_ui, inputs=[active_theme], outputs=[files_box, mgmt_status])
-
-    # Then update the .click() event handler
-    run_comprehensive_btn.click(
-        fn=lambda: run_comprehensive_evaluation(
-            vs_general=ensure_index("All"),
-            vs_personal=personal_vectorstore,
-            nlu_vectorstore=nlu_vectorstore,
-            config=CONFIG
-        ), 
-        # The output list now has three components
-        outputs=[batch_summary_md, comprehensive_results_df, comprehensive_results_df]
-    )
-    
-    demo.load(auto_setup_on_load, inputs=[active_theme], outputs=[settings_state, files_box, mgmt_status])
-    demo.load(load_test_fixtures)
-    test_save_btn.click(fn=test_save_file, inputs=None, outputs=[test_status])
-    check_save_btn.click(fn=check_test_file, inputs=None, outputs=[test_status])
-
-# --- Startup Logic ---
-def pre_load_indexes():
-    global personal_vectorstore, nlu_vectorstore
-    print("Pre-loading all indexes at startup...")
-    print("  - Loading NLU examples index...")
-    nlu_vectorstore = bootstrap_nlu_vectorstore("nlu_training_examples.jsonl", NLU_EXAMPLES_INDEX_PATH)
-    print(f"    ...NLU index loaded.")
-    for theme in CONFIG["themes"]:
-        print(f"  - Loading general index for theme: '{theme}'")
-        try:
-            ensure_index(theme)
-            print(f"    ...'{theme}' theme loaded.")
-        except Exception as e:
-            print(f"    ...Error loading theme '{theme}': {e}")
-    print("  - Loading personal knowledge index...")
-    try:
-        personal_vectorstore = build_or_load_vectorstore([], PERSONAL_INDEX_PATH, is_personal=True)
-        print("    ...Personal knowledge loaded.")
-    except Exception as e:
-        print(f"    ...Error loading personal knowledge: {e}")
-    print("All indexes loaded. Application is ready.")
-
-if __name__ == "__main__":
-    seed_files_into_theme('All')
-    pre_load_indexes()
-    demo.queue().launch(debug=True)