Delete alz_companion/agent.py

alz_companion/agent.py

@@ -1,342 +0,0 @@
-from __future__ import annotations
-import os
-import json
-import base64
-import time
-import tempfile
-from typing import List, Dict, Any, Optional
-import re
-
-# OpenAI for LLM (optional)
-try:
-    from openai import OpenAI
-except Exception:
-    OpenAI = None
-
-# LangChain & RAG
-from langchain.schema import Document
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-
-# TTS
-try:
-    from gtts import gTTS
-except Exception:
-    gTTS = None
-
-# Import all necessary prompts from the final prompts.py
-from .prompts import (
-    SYSTEM_TEMPLATE, ANSWER_TEMPLATE_CALM, ANSWER_TEMPLATE_ADQ,
-    SAFETY_GUARDRAILS, RISK_FOOTER, render_emotion_guidelines,
-    GOAL_ROUTER_PROMPT, 
-    NLU_EXAMPLES, 
-    SPECIALIST_CLASSIFICATION_PROMPT,
-    ANSWER_TEMPLATE_FACTUAL, 
-    ANSWER_TEMPLATE_GENERAL_KNOWLEDGE, 
-    ANSWER_TEMPLATE_GENERAL,
-    ROUTER_PROMPT as RAG_ROUTER_PROMPT # Alias the RAG router to avoid name conflicts
-)
-
-
-# -----------------------------
-# Multimodal Processing Functions
-# -----------------------------
-
-def _openai_client() -> Optional[OpenAI]:
-    api_key = os.getenv("OPENAI_API_KEY", "").strip()
-    return OpenAI(api_key=api_key) if api_key and OpenAI else None
-
-def describe_image(image_path: str) -> str:
-    """Uses a vision model to describe an image for context."""
-    client = _openai_client()
-    if not client:
-        return "(Image description failed: OpenAI API key not configured.)"
-    
-    try:
-        extension = os.path.splitext(image_path)[1].lower()
-        mime_type = "image/jpeg"
-        if extension == ".png": mime_type = "image/png"
-        elif extension in [".jpg", ".jpeg"]: mime_type = "image/jpeg"
-
-        with open(image_path, "rb") as image_file:
-            base64_image = base64.b64encode(image_file.read()).decode('utf-8')
-
-        response = client.chat.completions.create(
-            model="gpt-4o",
-            messages=[{"role": "user", "content": [
-                {"type": "text", "text": "Describe this image concisely for a memory journal. Focus on people, places, and key objects."},
-                {"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{base64_image}"}}
-            ]}],
-            max_tokens=100,
-        )
-        return response.choices[0].message.content or "No description available."
-    except Exception as e:
-        return f"[Image description error: {e}]"
-
-# -----------------------------
-# NLU Classification Function (Router/Specialist Model)
-# -----------------------------
-def detect_tags_from_query(
-    query: str,
-    behavior_options: list, emotion_options: list, topic_options: list, context_options: list,
-    example_retriever: Optional[FAISS] = None, **kwargs
-) -> Dict[str, Any]:
-    """Uses a two-step Router/Specialist model to classify the user's query."""
-    
-    print("\n--- RUNNING NLU V3 (Router/Specialist Model) ---")
-    
-    router_prompt = GOAL_ROUTER_PROMPT.format(query=query)
-    primary_goal = call_llm([{"role": "user", "content": router_prompt}], temperature=0).strip()
-    print(f"Detected Primary Goal: {primary_goal}")
-
-    result_dict = {"detected_behaviors": [], "detected_emotion": "None", "detected_topic": "None", "detected_contexts": []}
-
-    if primary_goal not in NLU_EXAMPLES:
-        print("Goal is not Practical Planning or Emotional Support. Skipping specialist.")
-        return result_dict
-
-    relevant_examples = NLU_EXAMPLES[primary_goal]
-    examples_str = "\n\n".join([
-        f"User Query: \"{ex['query']}\"\nJSON Response:\n{json.dumps(ex['json'], indent=4)}"
-        for ex in relevant_examples
-    ])
-    print(f"Selected {len(relevant_examples)} examples for goal: '{primary_goal}'")
-
-    specialist_prompt = SPECIALIST_CLASSIFICATION_PROMPT.format(examples=examples_str, query=query)
-    
-    messages = [{"role": "user", "content": specialist_prompt}]
-    response_str = call_llm(messages, temperature=0.1)
-    
-    print(f"\n--- NLU Full Response ---\n{response_str}\n-----------------------\n")
-    
-    try:
-        start_brace = response_str.find('{')
-        end_brace = response_str.rfind('}')
-        if start_brace != -1 and end_brace != -1 and end_brace > start_brace:
-            json_str = response_str[start_brace : end_brace + 1]
-            result = json.loads(json_str)
-            
-            result_dict["detected_behaviors"] = [b for b in result.get("detected_behaviors", []) if b in behavior_options]
-            result_dict["detected_emotion"] = result.get("detected_emotion") if result.get("detected_emotion") in emotion_options else "None"
-            result_dict["detected_topic"] = result.get("detected_topic") if result.get("detected_topic") in topic_options else "None"
-            result_dict["detected_contexts"] = [c for c in result.get("detected_contexts", []) if c in context_options]
-        
-        return result_dict
-    except (json.JSONDecodeError, AttributeError) as e:
-        print(f"ERROR parsing Specialist JSON: {e}")
-        return result_dict
-
-# -----------------------------
-# Embeddings & VectorStore
-# -----------------------------
-
-def _default_embeddings():
-    model_name = os.getenv("EMBEDDINGS_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
-    return HuggingFaceEmbeddings(model_name=model_name)
-
-def build_or_load_vectorstore(docs: List[Document], index_path: str, is_personal: bool = False) -> FAISS:
-    os.makedirs(os.path.dirname(index_path), exist_ok=True)
-    if os.path.isdir(index_path) and os.path.exists(os.path.join(index_path, "index.faiss")):
-        try:
-            return FAISS.load_local(index_path, _default_embeddings(), allow_dangerous_deserialization=True)
-        except Exception as e:
-            print(f"Could not load existing vector store at {index_path}: {e}")
-    
-    if is_personal and not docs:
-        docs = [Document(page_content="(This is the start of the personal memory journal.)", metadata={"source": "placeholder"})]
-        
-    vs = FAISS.from_documents(docs, _default_embeddings())
-    vs.save_local(index_path)
-    return vs
-
-def texts_from_jsonl(path: str) -> List[Document]:
-    out: List[Document] = []
-    try:
-        with open(path, "r", encoding="utf-8") as f:
-            for i, line in enumerate(f):
-                line = line.strip()
-                if not line: continue
-                obj = json.loads(line)
-                txt = obj.get("text") or ""
-                if not isinstance(txt, str) or not txt.strip(): continue
-                md = {"source": os.path.basename(path), "chunk": i}
-                for k in ("behaviors", "emotion", "topic_tags", "context_tags"):
-                    if k in obj: md[k] = obj[k]
-                out.append(Document(page_content=txt, metadata=md))
-    except Exception as e:
-        print(f"Error reading from JSONL file {path}: {e}")
-        return []
-    return out
-
-def bootstrap_vectorstore(sample_paths: List[str] | None = None, index_path: str = "data/faiss_index") -> FAISS:
-    docs: List[Document] = []
-    for p in (sample_paths or []):
-        try:
-            if p.lower().endswith(".jsonl"):
-                docs.extend(texts_from_jsonl(p))
-            else:
-                with open(p, "r", encoding="utf-8", errors="ignore") as fh:
-                    docs.append(Document(page_content=fh.read(), metadata={"source": os.path.basename(p)}))
-        except Exception:
-            continue
-    if not docs:
-        docs = [Document(page_content="(empty index)", metadata={"source": "placeholder"})]
-    return build_or_load_vectorstore(docs, index_path=index_path)
-
-# -----------------------------
-# LLM Call
-# -----------------------------
-def call_llm(messages: List[Dict[str, str]], temperature: float = 0.6, stop: Optional[List[str]] = None) -> str:
-    """Call OpenAI Chat Completions if available; else return a fallback."""
-    client = _openai_client()
-    model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
-    if not client:
-        return "(Offline Mode: OpenAI API key not configured.)"
-    try:
-        api_args = {
-            "model": model, "messages": messages,
-            "temperature": float(temperature if temperature is not None else 0.6)
-        }
-        if stop: api_args["stop"] = stop
-        resp = client.chat.completions.create(**api_args)
-        return (resp.choices[0].message.content or "").strip()
-    except Exception as e:
-        return f"[LLM API Error: {e}]"
-
-# -----------------------------
-# Prompting & RAG Chain
-# -----------------------------
-
-def _format_sources(docs: List[Document]) -> List[str]:
-    return list(set(d.metadata.get("source", "unknown") for d in docs))
-
-def make_rag_chain(
-    vs_general: FAISS,
-    vs_personal: FAISS,
-    *,
-    role: str = "patient",
-    temperature: float = 0.6,
-    language: str = "English",
-    patient_name: str = "the patient",
-    caregiver_name: str = "the caregiver",
-    tone: str = "warm",
-    # Custom prompt arguments
-    system_template: str,
-    factual_template: str,
-    general_knowledge_template: str,
-    general_conversation_template: str,
-    **kwargs
-):
-    """Returns a callable that performs the complete, RAG process."""
-    
-    def _format_docs(docs: List[Document], default_msg: str) -> str:
-        if not docs: return default_msg
-        unique_docs = {doc.page_content: doc for doc in docs}.values()
-        return "\n".join([f"- {d.page_content.strip()}" for d in unique_docs])
-
-    def _answer_fn(query: str, chat_history: List[Dict[str, str]], scenario_tag: Optional[str] = None, emotion_tag: Optional[str] = None, topic_tag: Optional[str] = None) -> Dict[str, Any]:
-        
-        router_messages = [{"role": "user", "content": RAG_ROUTER_PROMPT.format(query=query)}]
-        query_type = call_llm(router_messages, temperature=0.0).strip().lower()
-
-        system_message = system_template.format(tone=tone, language=language, patient_name=patient_name or "the patient", caregiver_name=caregiver_name or "the caregiver", guardrails=SAFETY_GUARDRAILS)
-        messages = [{"role": "system", "content": system_message}]
-        messages.extend(chat_history)
-
-        if "factual_question" in query_type:
-            retriever_personal = vs_personal.as_retriever(search_kwargs={"k": 2})
-            retriever_general = vs_general.as_retriever(search_kwargs={"k": 2})
-            all_docs = retriever_personal.invoke(query) + retriever_general.invoke(query)
-            context = _format_docs(all_docs, "(No relevant information found in the memory journal.)")
-            user_prompt = factual_template.format(context=context, query=query, language=language)
-            messages.append({"role": "user", "content": user_prompt})
-            answer = call_llm(messages, temperature=temperature)
-            return {"answer": answer, "sources": _format_sources(all_docs)}
-
-        elif "general_knowledge_question" in query_type:
-            user_prompt = general_knowledge_template.format(query=query, language=language)
-            messages.append({"role": "user", "content": user_prompt})
-            answer = call_llm(messages, temperature=temperature)
-            return {"answer": answer, "sources": ["General Knowledge"]}
-
-        elif "general_conversation" in query_type:
-            user_prompt = general_conversation_template.format(query=query, language=language)
-            messages.append({"role": "user", "content": user_prompt})
-            answer = call_llm(messages, temperature=temperature)
-            return {"answer": answer, "sources": []}
-
-        else: # Default to caregiving scenario
-            search_filter = {}
-            # Note: scenario_tag from NLU is a list, but RAG filter expects a single string for now.
-            if isinstance(scenario_tag, list) and scenario_tag:
-                search_filter["behaviors"] = scenario_tag[0].lower()
-            elif isinstance(scenario_tag, str):
-                search_filter["behaviors"] = scenario_tag.lower()
-            
-            if emotion_tag: search_filter["emotion"] = emotion_tag.lower()
-            if topic_tag: search_filter["topic_tags"] = topic_tag.lower()
-
-            personal_docs = vs_personal.similarity_search(query, k=3, filter=search_filter if search_filter else None)
-            general_docs = vs_general.similarity_search(query, k=3, filter=search_filter if search_filter else None)
-            
-            personal_context = _format_docs(personal_docs, "(No relevant personal memories found.)")
-            general_context = _format_docs(general_docs, "(No general guidance found.)")
-            
-            all_docs = personal_docs + general_docs
-            first_emotion = next((doc.metadata.get("emotion") for doc in all_docs if doc.metadata.get("emotion")), emotion_tag)
-            emotions_context = render_emotion_guidelines(first_emotion)
-
-            is_tagged_scenario = scenario_tag or emotion_tag or first_emotion
-            template = ANSWER_TEMPLATE_ADQ if is_tagged_scenario else ANSWER_TEMPLATE_CALM
-            
-            display_scenario_tag = scenario_tag[0] if isinstance(scenario_tag, list) and scenario_tag else scenario_tag
-            
-            if template == ANSWER_TEMPLATE_ADQ:
-                user_prompt = template.format(general_context=general_context, personal_context=personal_context, query=query, scenario_tag=display_scenario_tag, emotions_context=emotions_context, role=role, language=language)
-            else:
-                combined_context = f"General Guidance:\n{general_context}\n\nPersonal Memories:\n{personal_context}"
-                user_prompt = template.format(context=combined_context, query=query, language=language)
-
-            messages.append({"role": "user", "content": user_prompt})
-            answer = call_llm(messages, temperature=temperature)
-            
-            high_risk_scenarios = ["exit_seeking", "wandering", "elopement"]
-            if display_scenario_tag and display_scenario_tag.lower() in high_risk_scenarios:
-                answer += f"\n\n---\n{RISK_FOOTER}"
-
-            return {"answer": answer, "sources": _format_sources(all_docs)}
-
-    return _answer_fn
-
-def answer_query(chain, question: str, **kwargs) -> Dict[str, Any]:
-    if not callable(chain): return {"answer": "[Error: RAG chain is not callable]", "sources": []}
-    try:
-        return chain(question, **kwargs)
-    except Exception as e:
-        print(f"ERROR in answer_query: {e}")
-        return {"answer": f"[Error executing chain: {e}]", "sources": []}
-
-# -----------------------------
-# TTS & Transcription
-# -----------------------------
-def synthesize_tts(text: str, lang: str = "en"):
-    if not text or gTTS is None: return None
-    try:
-        fd, path = tempfile.mkstemp(suffix=".mp3")
-        os.close(fd)
-        tts = gTTS(text=text, lang=(lang or "en"))
-        tts.save(path)
-        return path
-    except Exception:
-        return None
-
-def transcribe_audio(filepath: str, lang: str = "en"):
-    client = _openai_client()
-    if not client:
-        return "[Transcription failed: API key not configured]"
-    api_args = {"model": "whisper-1"}
-    if lang and lang != "auto":
-        api_args["language"] = lang
-    with open(filepath, "rb") as audio_file:
-        transcription = client.audio.transcriptions.create(file=audio_file, **api_args)
-    return transcription.text