app/services/predict.py

import json
import io
from string import Template
from fastapi import Depends, UploadFile
import asyncio
from PIL import Image
import sqlite3

from app.api.dto.kg_query import KGQueryRequest, QueryContext, PredictedLabel
from app.core.dependencies import get_all_models
from app.core.type import Node
from app.models.crop_clip import EfficientNetModule
from app.models.gemini_caller import GeminiGenerator
from app.models.knowledge_graph import KnowledgeGraphUtils
from app.utils.constant import EXTRACTED_NODES
from app.utils.data_mapping import VECTOR_EMBEDDINGS_DB_PATH, DataMapping
from app.utils.extract_entity import clean_text, extract_entities
from app.utils.prompt import EXTRACT_NODES_FROM_IMAGE_PROMPT, EXTRACT_NODES_FROM_TEXT_PROMPT, GET_CAPTION_FROM_IMAGE_PROMPT, GET_STATEMENT_FROM_DISEASE_KG, GET_STATEMENT_FROM_ENV_FACTORS_KG, INSTRUCTION
from app.utils.main import unique_captions_by_disease

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if hasattr(obj, 'model_dump'):  # Pydantic v2 BaseModel
            return obj.model_dump()
        elif hasattr(obj, 'dict'):  # Pydantic v1 BaseModel
            return obj.dict()
        elif isinstance(obj, (list, tuple)):
            return [self.default(item) if hasattr(item, 'model_dump') or hasattr(item, 'dict') else item for item in obj]
        return super().default(obj)

def convert_to_json_serializable(obj):
    """Convert objects containing Node instances to JSON serializable format"""
    try:
        if hasattr(obj, 'model_dump'):  # Pydantic v2 BaseModel
            return obj.model_dump()
        elif hasattr(obj, 'dict'):  # Pydantic v1 BaseModel
            return obj.dict()
        elif isinstance(obj, list):
            return [convert_to_json_serializable(item) for item in obj]
        elif isinstance(obj, dict):
            return {key: convert_to_json_serializable(value) for key, value in obj.items()}
        elif isinstance(obj, tuple):
            return [convert_to_json_serializable(item) for item in obj]
        elif obj is None:
            return None
        else:
            # Try to convert basic types
            try:
                json.dumps(obj)  # Test if it's JSON serializable
                return obj
            except (TypeError, ValueError):
                # If it's not serializable, convert to string as fallback
                print(f"Warning: Converting non-serializable object {type(obj)} to string: {obj}")
                return str(obj)
    except Exception as e:
        print(f"Error in convert_to_json_serializable for object {type(obj)}: {e}")
        return str(obj)

extracted_nodes = [
    Node(
        id=node['id'],
        label=node['label'],
        name=node['name'],
        properties={'description': node['description']},
        score=None
    ) for node in EXTRACTED_NODES
]

class PredictService:
    def __init__(self, models):
        self.models = models

    async def predict_image(self, image: UploadFile):
        efficientnet_model: EfficientNetModule = self.models["efficientnet_model"]
        image_content = image.file.read()
        pil_image = Image.open(Image.io.BytesIO(image_content)).convert('RGB')
        return efficientnet_model.predict_image(pil_image)

    async def retrieve_kg(self, request: KGQueryRequest):
        try:
            kg: KnowledgeGraphUtils = self.models["knowledge_graph"]
            if not request.context:
                request.context = QueryContext()
            if request.crop_id:
                request.context.crop_id = request.crop_id
            if request.additional_info:
                additional_nodes = await self.__get_nodes_from_additional_info_async(
                    request.additional_info, self.models["data_mapper"]
                )
                if request.context.nodes is None:
                    request.context.nodes = []
                request.context.nodes = request.context.nodes + additional_nodes
                for node in request.context.nodes:
                    if node.score is None:
                        node.score = 0.9

            env_task = asyncio.create_task(
                kg.get_disease_from_env_factors(request.context.crop_id, request.context.nodes)
            )
            symptom_task = asyncio.create_task(
                kg.get_disease_from_symptoms(request.context.crop_id, request.context.nodes)
            )

            env_results, symptom_results = await asyncio.gather(env_task, symptom_task)
            context = request.context
            context.nodes.extend([env_result["disease"] for env_result in env_results])
            context.nodes.extend([symptom_result["disease"] for symptom_result in symptom_results])
            print(context.nodes)
            context.nodes.sort(key=lambda x: x.score, reverse=True)

            # Tính toán final_labels bằng trung bình có trọng số
            if context.predicted_labels:
                print("Got predicted labels")
                context.final_labels = self.calculate_final_labels(
                    context.predicted_labels,
                    env_results,
                    symptom_results,
                    context.crop_id
                )

            return {
                "context": context,
                "env_results": env_results,
                "symptom_results": symptom_results
            }

        except Exception as e:
            print(e)
            raise e

    def calculate_final_labels(self, predicted_labels, env_result, symptom_result, crop_id):
        """
        Tính toán final_labels bằng trung bình có trọng số từ:
        - predicted_labels: Kết quả từ CLIP model (weight: 0.4)
        - env_result: Kết quả từ environmental factors (weight: 0.3)
        - symptom_result: Kết quả từ symptoms (weight: 0.3)
        """
        # Weight
        ENV_WEIGHT = 0.3
        SYMPTOM_WEIGHT = 0.2

        # Dictionary để tích lũy scores cho mỗi disease/crop combination
        label_scores = {}

        # 1. Điểm từ CLIP model
        for label in predicted_labels:
            key = f"{label.crop_id}_{label.label}"
            print(f"CLIP key: {key} score: {label.confidence}")
            if key not in label_scores:
                label_scores[key] = {
                    "crop_id": label.crop_id,
                    "label": label.label,
                    "total_score": 0,
                    "count": 0
                }
            label_scores[key]["total_score"] += label.confidence
            label_scores[key]["count"] += 1

        # 2. Điểm từ symptoms
        for symptom in symptom_result:
            disease = symptom.get("disease")
            if disease and hasattr(disease, 'score'):
                key = f"{crop_id}_{disease.id}"
                print(f"Symptom key: {key} score: {disease.score}")
                if key not in label_scores:
                    label_scores[key] = {
                        "crop_id": crop_id,
                        "label": disease.id,
                        "total_score": 0,
                        "count": 0
                    }
                label_scores[key]["total_score"] += disease.score * SYMPTOM_WEIGHT * (1-label_scores[key]["total_score"])

        # 3. Điểm từ environmental factors
        for env in env_result:
            disease = env.get("disease")
            if disease and hasattr(disease, 'score'):
                # Giả sử disease có thông tin về crop và label
                key = f"{crop_id}_{disease.id}"
                print(f"Env key: {key} score: {disease.score}")
                if key not in label_scores:
                    label_scores[key] = {
                        "crop_id": crop_id,
                        "label": disease.id,
                        "total_score": 0,
                        "count": 0
                    }
                label_scores[key]["total_score"] += disease.score * ENV_WEIGHT * (1-label_scores[key]["total_score"])

        # Tạo final_labels từ kết quả tính toán
        final_labels = []
        for key, data in label_scores.items():
            final_confidence = data["total_score"]

            final_labels.append(PredictedLabel(
                crop_id=data["crop_id"],
                label=data["label"],
                confidence=min(final_confidence, 1.0)  # Đảm bảo không vượt quá 1.0
            ))

        # Sắp xếp theo confidence giảm dần và lọc ngưỡng
        final_labels.sort(key=lambda x: x.confidence, reverse=True)
        print(final_labels)
        return [label for label in final_labels if label.confidence > 0.1]  # Lọc ngưỡng thấp

    # TODO:
    async def get_nodes_from_image(self, image: UploadFile):
        try:
            gemini = GeminiGenerator()
            symptoms = self.models["data_mapper"].get_embedding_by_label("Symptom")
            symptom_list = [f"- id:{node.id} - name:{node.name}" for node in symptoms]
            symptom_list = "\n".join(symptom_list)
            prompt = Template(EXTRACT_NODES_FROM_IMAGE_PROMPT).substitute(symptom_list=symptom_list)
            image_content = image.file.read()
            pil_image = Image.open(io.BytesIO(image_content)).convert('RGB')

            ids = gemini.generate(prompt, image=pil_image)
            ids = (json.loads(clean_text(ids.text)))["ids"]
            print(ids)
            nodes = []
            for id in ids:
                node = next((symptom for symptom in symptoms if symptom.id == id), None)
                nodes.append(node)
            return nodes
        except Exception as e:
            print(f"Error while extract knowledge entities from image: {str(e)}")
            return []

    async def __get_nodes_from_additional_info_async(self, additional_info: str, data_mapper: DataMapping):
        entities = extract_entities(additional_info)

        if not entities:
            return []
        tasks = []
        for entity in entities:
            task = asyncio.create_task(
                data_mapper.get_top_result_by_text_async(entity.name, 3),
                name=f"query_entity_{entity.name}"
            )
            tasks.append(task)
        results = await asyncio.gather(*tasks, return_exceptions=True)

        top_results: list[Node] = []
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                continue
            for node in result:
                top_results.append(node)
        return top_results

    def get_embedding_by_id_threadsafe(self, id):
        # Mỗi thread tạo connection riêng
        conn = sqlite3.connect(VECTOR_EMBEDDINGS_DB_PATH, check_same_thread=False)
        cursor = conn.cursor()
        try:
            cursor.execute("SELECT * FROM embeddings WHERE e_index = ?", (id,))
            result = cursor.fetchone()
            return result
        finally:
            cursor.close()  # Đóng connection sau khi dùng xong
            conn.close()


    async def retrieve_kg_text(self, request: KGQueryRequest):
        try:
            nodes = await self.get_nodes_from_text(request.additional_info)
            kg: KnowledgeGraphUtils = self.models["knowledge_graph"]
            env_task = asyncio.create_task(
                kg.get_disease_from_env_factors(request.crop_id, nodes)
            )
            symptom_task = asyncio.create_task(
                kg.get_disease_from_symptoms(request.crop_id, nodes)
            )

            env_results, symptom_results = await asyncio.gather(env_task, symptom_task)

            best_label = request.context.predicted_labels[0].label
            best_env_result = next((result for result in env_results if result["disease"].id == best_label), None)
            best_env_result_str = str(best_env_result)
            best_symptom_result = next((result for result in symptom_results if result["disease"].id == best_label), None)
            best_symptom_result_str = str(best_symptom_result)
            prompt1 = None
            prompt2 = None

            result1 = None
            result2 = None

            if best_env_result:
                prompt1 = Template(GET_STATEMENT_FROM_ENV_FACTORS_KG).substitute(context=best_env_result_str)
            if best_symptom_result:
                prompt2 = Template(GET_STATEMENT_FROM_DISEASE_KG).substitute(context=best_symptom_result_str)
            gemini = GeminiGenerator()
            print(prompt1)
            if prompt1:
                result1 = gemini.generate(prompt1)
            if prompt2:
                result2 = gemini.generate(prompt2)

            return {
                "env_results": env_results,
                "symptom_results": symptom_results,
                "env_statement": result1.text if result1 else None,
                "symptom_statement": result2.text if result2 else None
            }
        except Exception as e:
            print(e)
            raise e

    async def get_nodes_from_text(self, text: str):
        try:


            gemini = GeminiGenerator()
            node_list = [f"  + id:{node.id}, name:{node.name}, description:{node.properties.get('description', '')}" for node in extracted_nodes]
            prompt = Template(EXTRACT_NODES_FROM_TEXT_PROMPT).substitute(text=text, node_list=node_list)
            ids = gemini.generate(prompt)
            print(ids)

            ids = (json.loads(clean_text(ids.text)))["ids"]
            print(ids)
            nodes = [next((node for node in extracted_nodes if node.id == id), None) for id in ids]
            return nodes
        except Exception as e:
            print(e)

    # async def get_all_nodes(self):
    #     try:
    #         kg: KnowledgeGraphUtils = self.models["knowledge_graph"]
    #         list_nodes = await kg.get_all_nodes()
    #         return [dict(node[0], **{"label": "Symptom"}) for node in list_nodes]
    #     except Exception as e:
    #         print(e)
    #         return []

    async def get_caption(self, image: UploadFile):
        try:
            gemini = GeminiGenerator()
            prompt = Template(GET_CAPTION_FROM_IMAGE_PROMPT).substitute(caption_list=unique_captions_by_disease)
            image_content = image.file.read()
            pil_image = Image.open(io.BytesIO(image_content)).convert('RGB')
            return (json.loads(clean_text(gemini.generate(prompt, image=pil_image).text)))["caption"]
        except Exception as e:
            print(e)
            return None

def get_predict_service(models = Depends(get_all_models)):
    return PredictService(models)