app.py

from flask import Flask, request, render_template, session, url_for, redirect, jsonify
# from flask_session import Session  <--- REMOVED
from langchain_core.messages import HumanMessage, AIMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import os
import logging
import re
import traceback
import base64
import shutil
import zipfile
from dotenv import load_dotenv
from huggingface_hub import hf_hub_download
from PIL import Image

# --- Core Application Imports ---
from src.medical_swarm import run_medical_swarm
from src.utils import load_rag_system, standardize_query, get_standalone_question, parse_agent_response, markdown_bold_to_html
from langchain_google_genai import ChatGoogleGenerativeAI

# Setup logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Load environment variables
load_dotenv()

# These are your "customers". You give them a key.
# In a real app, this would be in a database.
VALID_API_KEYS = {
    "anakmail_key_123": "Dr. Amelia (Premium Plan, Unlimited)",
    "irfan_key_456": "Irfan (Admin, Unlimited)",
    "sistem_gelap": "Demo User (Free Tier, 10 requests)"
}
    
# --- 1. NEW HELPER FUNCTIONS TO FIX 'TypeError' ---
def hydrate_history(raw_history_list: list) -> list:
    """Converts a list of dicts from session back into LangChain Message objects."""
    history = []
    if not raw_history_list:
        return history
    for item in raw_history_list:
        if item.get('type') == 'human':
            history.append(HumanMessage(content=item.get('content', '')))
        elif item.get('type') == 'ai':
            history.append(AIMessage(content=item.get('content', '')))
    return history

def dehydrate_history(history_messages: list) -> list:
    """Converts LangChain Message objects into a JSON-serializable list of dicts."""
    raw_list = []
    for msg in history_messages:
        if isinstance(msg, HumanMessage):
            raw_list.append({'type': 'human', 'content': msg.content})
        elif isinstance(msg, AIMessage):
            raw_list.append({'type': 'ai', 'content': msg.content})
    return raw_list

# --- 2. DATABASE SETUP FUNCTION (For Deployment) ---
def setup_database():
    """Downloads and unzips the ChromaDB folder from Hugging Face Datasets."""
    DATASET_REPO_ID = "WanIrfan/atlast-db" 
    ZIP_FILENAME = "chroma_db.zip"
    DB_DIR = "chroma_db"
    if os.path.exists(DB_DIR) and os.listdir(DB_DIR):
        logger.info("✅ Database directory already exists. Skipping download.")
        return
    logger.info(f"📥 Downloading database from HF Hub: {DATASET_REPO_ID}")
    try:
        zip_path = hf_hub_download(repo_id=DATASET_REPO_ID, filename=ZIP_FILENAME, repo_type="dataset")
        logger.info(f"📦 Unzipping database from {zip_path}...")
        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
            zip_ref.extractall(".")
        logger.info("✅ Database setup complete!")
        if os.path.exists(zip_path):
            os.remove(zip_path)
    except Exception as e:
        logger.error(f"❌ CRITICAL ERROR setting up database: {e}", exc_info=True)

# --- RUN DATABASE SETUP *BEFORE* INITIALIZING THE APP ---
setup_database()

# --- STANDARD FLASK APP INITIALIZATION ---
app = Flask(__name__)
app.secret_key = "a_really_strong_static_secret_key_12345" 
# --- REMOVED flask_session CONFIG ---

google_api_key = os.getenv("GOOGLE_API_KEY")
if not google_api_key:
    logger.warning("⚠️ GOOGLE_API_KEY not found.")
else:
    logger.info("GOOGLE_API_KEY loaded successfully.")

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.05, google_api_key=google_api_key)

# --- LOAD RAG SYSTEMS (AFTER DB SETUP) ---
logger.info("🌟 Starting Multi-Domain AI Assistant...")
try:
    rag_systems = {
        'medical': load_rag_system(collection_name="medical_csv_Agentic_retrieval", domain="medical"),
        'islamic': load_rag_system(collection_name="islamic_texts_Agentic_retrieval", domain="islamic"),
        'insurance': load_rag_system(collection_name="etiqa_Agentic_retrieval", domain="insurance")
    }
except Exception as e:
    logger.error(f"❌ FAILED to load RAG systems. Error: {e}", exc_info=True)
    rag_systems = {'medical': None, 'islamic': None, 'insurance': None}

app.rag_systems = rag_systems
app.llm = llm

logger.info("\n📊 SYSTEM STATUS:")
for domain, system in rag_systems.items():
    status = "✅ Ready" if system else "❌ Failed (DB missing?)" 
    logger.info(f"   {domain}: {status}")

# --- FLASK WEB UI ROUTES ---
@app.route("/")
def homePage():
    session.clear() # Clear all keys
    return render_template("homePage.html")

# --- MEDICAL PAGE ---
@app.route("/medical", methods=["GET", "POST"])
def medical_page():
    if request.method == "GET":
        latest_response = session.pop('latest_medical_response', {}) 
        return render_template("medical_page.html", 
                               history=session.get('medical_history', []),
                               answer=latest_response.get('answer', ""),
                               thoughts=latest_response.get('thoughts', ""),
                               validation=latest_response.get('validation', ""),
                               source=latest_response.get('source', ""))
    
    answer, thoughts, validation, source = "", "", "", ""
    raw_history_list = session.get('medical_history', [])
    history_for_agent = hydrate_history(raw_history_list)
    current_medical_document = session.get('current_medical_document', "")
    query = ""
    
    try:
        query=standardize_query(request.form.get("query", ""))
        has_image = 'image' in request.files and request.files['image'].filename
        has_document = 'document' in request.files and request.files['document'].filename
        
        if not (query or has_image or has_document):
             raise ValueError("No query or file provided.")

        if has_document:
            logger.info("Processing Document with Medical Swarm")
            file = request.files['document']
            document_text = file.read().decode("utf-8")
            session['current_medical_document'] = document_text
            current_medical_document = document_text
            swarm_answer = run_medical_swarm(current_medical_document, query)
            answer = markdown_bold_to_html(swarm_answer)
            thoughts = "Swarm analysis complete."
            validation = (True, "Swarm output generated.")
            source = "Medical Swarm"
            history_for_agent.append(HumanMessage(content=f"[Document Uploaded] Query: '{query}'"))
            history_for_agent.append(AIMessage(content=answer))

        elif has_image :
            logger.info("Processing Multimodal RAG: Query + Image")
            file = request.files['image']
            upload_dir = "Uploads"
            os.makedirs(upload_dir, exist_ok=True)
            image_path = os.path.join(upload_dir, file.filename)
            try:
                file.save(image_path); file.close()
                with open(image_path, "rb") as img_file:
                    img_data = base64.b64encode(img_file.read()).decode("utf-8")
                vision_prompt = f"Analyze image. Query: '{query}'"
                message = HumanMessage(content=[{"type": "text", "text": vision_prompt}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_data}"}])
                visual_prediction = llm.invoke([message]).content
                enhanced_query = (f'User Query: "{query}" Context from Image: "{visual_prediction}"')
                agent = rag_systems['medical']
                if not agent: raise Exception("Medical RAG system not loaded.")
                response_dict = agent.answer(enhanced_query, chat_history=history_for_agent)
                answer, thoughts, validation, source = parse_agent_response(response_dict)
                history_for_agent.append(HumanMessage(content=query + " [Image Attached]"))
                history_for_agent.append(AIMessage(content=answer))
            finally:
                if os.path.exists(image_path):
                    try: os.remove(image_path)
                    except Exception as e: logger.warning(f"Could not remove {image_path}. Error: {e}")
            
        elif query:
            history_doc_context = history_for_agent
            if current_medical_document:
                history_doc_context = [HumanMessage(content=f"Document Context:\n{current_medical_document}")] + history_for_agent
            else:
                logger.info("Processing Text RAG query for Medical domain")
            
            standalone_query = get_standalone_question(query, history_doc_context, llm)
            logger.info(f"Standalone Query : {standalone_query}")
            agent = rag_systems['medical']
            if not agent: raise Exception("Medical RAG system not loaded.")
            response_dict = agent.answer(standalone_query, chat_history=history_doc_context)
            answer, thoughts, validation, source = parse_agent_response(response_dict)
            history_for_agent.append(HumanMessage(content=query))
            history_for_agent.append(AIMessage(content=answer))
    
    except Exception as e:
        logger.error(f"Error on /medical page: {e}", exc_info=True)
        answer = f"An error occurred: {e}"
        thoughts = traceback.format_exc()
        validation = (False, "Exception")
        source = "Application Error"
        history_for_agent.append(HumanMessage(content=query if query else "Failed request"))
        history_for_agent.append(AIMessage(content=answer))
    
    session['medical_history'] = dehydrate_history(history_for_agent)
    session['latest_medical_response'] = {'answer': answer, 'thoughts': thoughts, 'validation': validation, 'source': source}
    session.modified = True
                             
    logger.info(f"DEBUG: Saving to session: ANSWER='{answer[:50]}...'")
    return redirect(url_for('medical_page'))

@app.route("/medical/clear")
def clear_medical_chat():
    session.pop('medical_history', None)
    session.pop('current_medical_document', None)
    return redirect(url_for('medical_page'))

# --- ISLAMIC PAGE ---
@app.route("/islamic", methods=["GET", "POST"])
def islamic_page():
    if request.method == "GET":
        latest_response = session.pop('latest_islamic_response', {})
        return render_template("islamic_page.html",
                                history=session.get('islamic_history', []),
                                answer=latest_response.get('answer', ""),
                                thoughts=latest_response.get('thoughts', ""),
                                validation=latest_response.get('validation', ""),
                                source=latest_response.get('source', ""))
    
    answer, thoughts, validation, source = "", "", "", ""
    raw_history_list = session.get('islamic_history', [])
    history_for_agent = hydrate_history(raw_history_list)
    query = ""
    try:
        query = standardize_query(request.form.get("query", ""))
        has_image = 'image' in request.files and request.files['image'].filename
        if not (query or has_image):
             raise ValueError("No query or file provided.")
        final_query = query
        
        if has_image:
            logger.info("Processing Multimodal RAG query for Islamic domain")
            file = request.files['image']
            upload_dir = "Uploads"
            os.makedirs(upload_dir, exist_ok=True)
            image_path = os.path.join(upload_dir, file.filename)
            try:
                file.save(image_path); file.close() 
                with open(image_path, "rb") as img_file:
                    img_base64 = base64.b64encode(img_file.read()).decode("utf-8")
                vision_prompt = f"Analyze image. Query: '{query}'"
                message = HumanMessage(content=[{"type": "text", "text": vision_prompt}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_base64}"}])
                visual_prediction = llm.invoke([message]).content
                final_query = (f'User Query: "{query}" Context from Image: "{visual_prediction}"')
            finally:
                if os.path.exists(image_path):
                    try: os.remove(image_path)
                    except Exception as e: logger.warning(f"Could not remove {image_path}. Error: {e}")
            history_for_agent.append(HumanMessage(content=query + " [Image Attached]"))

        elif query:
            logger.info("Processing Text RAG query for Islamic domain")
            final_query = get_standalone_question(query, history_for_agent, llm)
            history_for_agent.append(HumanMessage(content=query))
            
        agent = rag_systems['islamic']
        if not agent: raise Exception("Islamic RAG system is not loaded.")
        response_dict = agent.answer(final_query, chat_history=history_for_agent[:-1])
        answer, thoughts, validation, source = parse_agent_response(response_dict)
        history_for_agent.append(AIMessage(content=answer))

    except Exception as e:
        logger.error(f"Error on /islamic page: {e}", exc_info=True)
        answer = f"An error occurred: {e}"; thoughts = traceback.format_exc(); validation = (False, "Exception"); source = "Application Error"
        if not (has_image or query): history_for_agent.append(HumanMessage(content="Failed request"))
        else: history_for_agent.append(HumanMessage(content=query))
        history_for_agent.append(AIMessage(content=answer))

    session['islamic_history'] = dehydrate_history(history_for_agent)
    session['latest_islamic_response'] = {'answer': answer, 'thoughts': thoughts, 'validation': validation, 'source': source}
    session.modified = True
    logger.info(f"DEBUG: Saving to session: ANSWER='{answer[:50]}...'")
    return redirect(url_for('islamic_page'))

@app.route("/islamic/clear")
def clear_islamic_chat():
    session.pop('islamic_history', None)
    return redirect(url_for('islamic_page'))

# --- INSURANCE PAGE ---
@app.route("/insurance", methods=["GET", "POST"])
def insurance_page():
    if request.method == "GET" :
        latest_response = session.pop('latest_insurance_response',{})
        return render_template("insurance_page.html",
                                history=session.get('insurance_history', []),
                                answer=latest_response.get('answer', ""),
                                thoughts=latest_response.get('thoughts', ""),
                                validation=latest_response.get('validation', ""),
                                source=latest_response.get('source', ""))
    
    answer, thoughts, validation, source = "", "", "", ""
    raw_history_list = session.get('insurance_history', [])
    history_for_agent = hydrate_history(raw_history_list)
    query = ""
    try:
        query = standardize_query(request.form.get("query", ""))
        if not query:
            raise ValueError("No query provided.")
        
        standalone_query = get_standalone_question(query, history_for_agent, llm)
        agent = rag_systems['insurance']
        if not agent: raise Exception("Insurance RAG system is not loaded.")
        
        response_dict = agent.answer(standalone_query, chat_history=history_for_agent)
        answer, thoughts, validation, source = parse_agent_response(response_dict)
        history_for_agent.append(HumanMessage(content=query))
        history_for_agent.append(AIMessage(content=answer))

    except Exception as e:
        logger.error(f"Error on /insurance page: {e}", exc_info=True)
        answer = f"An error occurred: {e}"; thoughts = traceback.format_exc(); validation = (False, "Exception"); source = "Application Error"
        history_for_agent.append(HumanMessage(content=query))
        history_for_agent.append(AIMessage(content=answer))
        
    session['insurance_history'] = dehydrate_history(history_for_agent)
    session['latest_insurance_response'] = {'answer': answer, 'thoughts': thoughts, 'validation': validation, 'source': source}
    session.modified = True
    logger.debug(f"Redirecting after saving latest response.")
    return redirect(url_for('insurance_page'))

@app.route("/insurance/clear")
def clear_insurance_chat():
    session.pop('insurance_history', None)
    return redirect(url_for('insurance_page'))

@app.route("/about", methods=["GET"])
def about():
    return render_template("about.html")

# --- (Metrics routes remain unchanged) ---
@app.route('/metrics/<domain>')
def get_metrics(domain):
    try:
        if domain == "medical" and rag_systems['medical']:
            stats = rag_systems['medical'].metrics_tracker.get_stats()
        elif domain == "islamic" and rag_systems['islamic']:
            stats = rag_systems['islamic'].metrics_tracker.get_stats()
        elif domain == "insurance" and rag_systems['insurance']:
            stats = rag_systems['insurance'].metrics_tracker.get_stats()
        elif not rag_systems.get(domain):
            return jsonify({"error": f"{domain} RAG system not loaded"}), 500
        else:
            return jsonify({"error": "Invalid domain"}), 400
        return jsonify(stats)
    except Exception as e:
        return jsonify({"error": str(e)}), 500

@app.route('/metrics/reset/<domain>', methods=['POST'])
def reset_metrics(domain):
    try:
        if domain == "medical" and rag_systems['medical']:
            rag_systems['medical'].metrics_tracker.reset_metrics()
        elif domain == "islamic" and rag_systems['islamic']:
            rag_systems['islamic'].metrics_tracker.reset_metrics()
        elif domain == "insurance" and rag_systems['insurance']:
            rag_systems['insurance'].metrics_tracker.reset_metrics()
        elif not rag_systems.get(domain):
            return jsonify({"error": f"{domain} RAG system not loaded"}), 500
        else:
            return jsonify({"error": "Invalid domain"}), 400
        return jsonify({"success": True, "message": f"Metrics reset for {domain}"})
    except Exception as e:
        return jsonify({"error": str(e)}), 500
        
# Helper function to check API key
API_USAGE = {}

def check_api_key(request_data):
    api_key = request_data.get("api_key")
    
    # 1. Check if key exists
    if not api_key or api_key not in VALID_API_KEYS:
        return False, {"error": "Invalid API key"}, 401
    
    # 2. Initialize counter for this key if new
    if api_key not in API_USAGE:
        API_USAGE[api_key] = 0
        
    # 3. Check Quota (The "Selling" Logic)
    if api_key == "sistem_gelap" and API_USAGE[api_key] >= 10:
        logger.warning(f"Quota exceeded for user: {VALID_API_KEYS[api_key]}")
        return False, {"error": "Quota exceeded. Free tier is limited to 10 requests."}, 429
        
    # 4. Increment Counter
    API_USAGE[api_key] += 1
    logger.info(f"User {VALID_API_KEYS[api_key]} used {API_USAGE[api_key]} requests.")
    
    return True, None, None

# Helper function to save and process uploaded files (Base64)
def process_base64_file(base64_string, file_type):
    try:
        # Decode the base64 string
        file_bytes = base64.b64decode(base64_string)
        
        # Save to a temporary file
        upload_dir = "Uploads"
        os.makedirs(upload_dir, exist_ok=True)
        # Use a unique filename
        temp_filename = f"{file_type}_{int(time.time())}.tmp"
        temp_path = os.path.join(upload_dir, temp_filename)
        
        with open(temp_path, 'wb') as f:
            f.write(file_bytes)
            
        logger.info(f"Saved temporary {file_type} to {temp_path}")
        return temp_path
    except Exception as e:
        logger.error(f"Error decoding/saving base64 file: {e}")
        return None
# --- 3. NEW API-ONLY ROUTES ---

@app.route("/api/medical", methods=["POST"])
def medical_api():
    try:
        data = request.json
        is_valid, error_response, status_code = check_api_key(data)
        if not is_valid:
            return jsonify(error_response), status_code
            
        query = data.get("query")
        if not query:
            return jsonify({"error": "No query provided"}), 400
        
        # Hydrate history from the JSON payload
        raw_history = data.get("history", [])
        history_for_agent = hydrate_history(raw_history)
        
        agent = rag_systems['medical']
        if not agent:
            return jsonify({"error": "Medical RAG system not loaded"}), 500
            
        # --- Handle File Uploads (Base64) ---
        enhanced_query = query
        temp_file_path = None
        
        if data.get("document_base64"):
            logger.info("API: Processing base64 document for Swarm")
            doc_text = base64.b64decode(data.get("document_base64")).decode('utf-8')
            swarm_answer = run_medical_swarm(doc_text, query)
            response_dict = {
                "answer": markdown_bold_to_html(swarm_answer),
                "thoughts": "Swarm analysis complete.",
                "validation": (True, "Swarm output generated."),
                "source": "Medical Swarm",
                "response_time": 0 # Not tracked for swarm in this path
            }
            return jsonify(response_dict)
            
        elif data.get("image_base64"):
            logger.info("API: Processing base64 image")
            temp_file_path = process_base64_file(data.get("image_base64"), "image")
            if not temp_file_path:
                return jsonify({"error": "Invalid base64 image data"}), 400
            
            with open(temp_file_path, "rb") as img_file:
                img_data = base64.b64encode(img_file.read()).decode("utf-8")
            
            vision_prompt = f"Analyze image. Query: '{query}'"
            message = HumanMessage(content=[{"type": "text", "text": vision_prompt}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_data}"}])
            visual_prediction = llm.invoke([message]).content
            enhanced_query = (f'User Query: "{query}" Context from Image: "{visual_prediction}"')

        # Run the agent
        response_dict = agent.answer(enhanced_query, chat_history=history_for_agent)

        # Clean up temp file
        if temp_file_path and os.path.exists(temp_file_path):
            os.remove(temp_file_path)
        
        # Return the full, clean JSON response
        return jsonify(response_dict)
        
    except Exception as e:
        logger.error(f"Error on /api/medical: {e}", exc_info=True)
        return jsonify({"error": str(e)}), 500

@app.route("/api/islamic", methods=["POST"])
def islamic_api():
    try:
        data = request.json
        is_valid, error_response, status_code = check_api_key(data)
        if not is_valid: return jsonify(error_response), status_code
        
        query = data.get("query")
        if not query: return jsonify({"error": "No query provided"}), 400
        
        raw_history = data.get("history", [])
        history_for_agent = hydrate_history(raw_history)
        
        agent = rag_systems['islamic']
        if not agent: return jsonify({"error": "Islamic RAG system not loaded"}), 500
            
        enhanced_query = query
        temp_file_path = None
        
        if data.get("image_base64"):
            logger.info("API: Processing base64 image")
            temp_file_path = process_base64_file(data.get("image_base64"), "image")
            if not temp_file_path:
                return jsonify({"error": "Invalid base64 image data"}), 400
            
            with open(temp_file_path, "rb") as img_file:
                img_data = base64.b64encode(img_file.read()).decode("utf-8")
            
            vision_prompt = f"Analyze image. Query: '{query}'"
            message = HumanMessage(content=[{"type": "text", "text": vision_prompt}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{img_data}"}])
            visual_prediction = llm.invoke([message]).content
            enhanced_query = (f'User Query: "{query}" Context from Image: "{visual_prediction}"')

        response_dict = agent.answer(enhanced_query, chat_history=history_for_agent)
        
        if temp_file_path and os.path.exists(temp_file_path):
            os.remove(temp_file_path)
            
        return jsonify(response_dict)
        
    except Exception as e:
        logger.error(f"Error on /api/islamic: {e}", exc_info=True)
        return jsonify({"error": str(e)}), 500

@app.route("/api/insurance", methods=["POST"])
def insurance_api():
    try:
        data = request.json
        is_valid, error_response, status_code = check_api_key(data)
        if not is_valid: return jsonify(error_response), status_code
        
        query = data.get("query")
        if not query: return jsonify({"error": "No query provided"}), 400
        
        raw_history = data.get("history", [])
        history_for_agent = hydrate_history(raw_history)
        
        agent = rag_systems['insurance']
        if not agent: return jsonify({"error": "Insurance RAG system not loaded"}), 500
        
        response_dict = agent.answer(query, chat_history=history_for_agent)
        return jsonify(response_dict)
        
    except Exception as e:
        logger.error(f"Error on /api/insurance: {e}", exc_info=True)
        return jsonify({"error": str(e)}), 500
        
if __name__ == "__main__":
    logger.info("Starting Flask app for deployment testing...")
    app.run(host="0.0.0.0", port=7860, debug=False)