from autogen import GroupChatManager
import json
import re, os
import networkx as nx

from agents import create_parse_agents, create_graph_agents, language_summary_agents, calculation_summary_agents
from agents import is_termination_msg, is_termination_require, gpt4_config
from corrector_agents import get_corrector_agents
from refiner_agents import get_refiner_agents

from chats import InputParserGroupChat, RequirementGroupChat, LanguageGroupChat, CalculationGroupChat, SceneGraphGroupChat, SchemaGroupChat, LayoutCorrectorGroupChat, ObjectDeletionGroupChat, LayoutRefinerGroupChat

from utils import get_room_priors, extract_list_from_json
from utils import preprocess_scene_graph, build_graph, remove_unnecessary_edges, handle_under_prepositions, get_conflicts, get_size_conflicts, get_object_from_scene_graph
from utils import get_object_from_scene_graph, get_rotation, get_cluster_objects, clean_and_extract_edges
from utils import get_cluster_size
from utils import get_possible_positions, is_point_bbox, calculate_overlap, get_topological_ordering, place_object, get_depth, get_visualization
import openshape
import torch
import numpy as np
import transformers
import threading
import multiprocessing
import sys, shutil
import pandas as pd
from torch.nn import functional as F
import objaverse
import trimesh
import certifi
import ssl

ssl._create_default_https_context = ssl._create_unverified_context
os.environ['SSL_CERT_FILE'] = certifi.where()

class Generator:
    def __init__(self, layout_elements=['south_wall', 'north_wall', 'west_wall', 'east_wall', 'middle of the room', 'ceiling'], room_dimensions=[5.0, 5.0, 3.0], result_file="./results/layout_w_cot.json"):
        
        self.room_dimensions = room_dimensions
        self.room_priors = get_room_priors(self.room_dimensions)
        
        self.layout_elements = list(layout_elements)
        self.result_file = result_file
        self.scene_graph = None
        self.cot_info = {}

        os.environ["TOKENIZERS_PARALLELISM"] = "false"

        meta = json.load(
            open('./embeddings/objaverse_meta.json')
        )
        self.meta = {x['u']: x for x in meta['entries']}

        deser = torch.load('./embeddings/objaverse.pt')
        self.us = deser['us']
        self.feats = deser['feats']

        local_assets = pd.read_excel("./assets/copy.xlsx", skiprows=2)
        captions = local_assets["caption_clip"].tolist()

        file_paths = []
        bbx_values = []
        for index, row in local_assets.iterrows():
            model_name = row['name_en']
            model_path = os.path.join("./assets/lvm_2032fbx", f"{model_name}.fbx")
            file_paths.append(model_path)
            bbx_values.append(row['bbx'])

        self.caption_to_file = [
            {
                "caption": caption,
                "file_path": path,
                "bbx": bbx
            }
            for caption, path, bbx in zip(captions, file_paths, bbx_values)
        ]
        

        self.clip_model, self.clip_prep = transformers.CLIPModel.from_pretrained(
            "./ckpts/CLIP-ViT-bigG-14-laion2B-39B-b160k",
            low_cpu_mem_usage=True, torch_dtype=torch.float16,
            offload_state_dict=True,
        ), transformers.CLIPProcessor.from_pretrained("./ckpts/CLIP-ViT-bigG-14-laion2B-39B-b160k")

        self.local_embeddings = torch.load("./embeddings/local.pt")


    def parse_input(self, user_input, max_number_of_objects):
        self.user_input = user_input
        self.max_number_of_objects = max_number_of_objects
        user_proxy, requirements_analyzer, substructure_analyzer, substructure_analyzer_checker, interior_designer, designer_checker = create_parse_agents(self.max_number_of_objects)

        init_groupchat = RequirementGroupChat(
            agents=[user_proxy, requirements_analyzer, substructure_analyzer, interior_designer, designer_checker],
            messages=[],
            max_round=16
        )

        manager = GroupChatManager(groupchat=init_groupchat, llm_config=gpt4_config, is_termination_msg=is_termination_require)

        user_proxy.initiate_chat(
            manager,
            message=f"""
            The room has the size {self.room_dimensions[0]}m x {self.room_dimensions[1]}m x {self.room_dimensions[2]}m
            User Input (in triple backquotes):
            ```
            {self.user_input}
            ```
            Room layout elements in the room (in triple backquotes):
            ```
            ['south_wall', 'north_wall', 'west_wall', 'east_wall', 'middle of the room', 'ceiling']
            ```
            json
            """,
        )

        # correction = init_groupchat.messages[-2]
        # pattern = r'```json\s*([^`]+)\s*```'
        # match = re.search(pattern, correction["content"], re.DOTALL).group(1)
        # self.designer_response = json.loads(match)
        self.designer_response = json.loads(init_groupchat.messages[-2]["content"])
        self.cot_info["parse_cot"] = self.designer_response["chain_of_thought"]
        # reason_designer, blocks_designer = extract_list_from_json(designer_response, 'Reason'), extract_list_from_json(designer_response, 'Objects')
        # self.reason_designer = reason_designer

    def retrieve_local_assets(self):
        

        print("Locking...")
        sys.clip_move_lock = threading.Lock()
        print("Locked.")

        if torch.cuda.is_available():
            with sys.clip_move_lock:
                self.clip_model.cuda()
        torch.set_grad_enabled(False)
        

        def preprocess(input_string):
            wo_numericals = re.sub(r'\d', '', input_string)
            output = wo_numericals.replace("_", " ")
            return output

        def retrieve_local(query_embedding, top=1, sim_th=0.5):
            query_embedding = F.normalize(query_embedding.detach().cpu(), dim=-1).squeeze()
            sims = []
            for embedding in torch.split(self.local_embeddings, 10240):
                sims.append(query_embedding @ F.normalize(embedding.float(), dim=-1).T)
            sims = torch.cat(sims)
            sims, indices = torch.sort(sims, descending=True)
            results = []
            for i, sim in zip(indices, sims):
                if sim > sim_th:
                    results.append({
                        "caption": self.caption_to_file[i]["caption"],
                        "file_path": self.caption_to_file[i]["file_path"],
                        "bbx": self.caption_to_file[i]["bbx"],
                        "sim": sim.item()
                    })
                    if len(results) >= top:
                        break
            return results

        def retrieve(embedding, top=1, sim_th=0.1, filter_fn=None):
            sims = []
            embedding = F.normalize(embedding.detach().cpu(), dim=-1).squeeze()
            for chunk in torch.split(self.feats, 10240):
                sims.append(embedding @ F.normalize(chunk.float(), dim=-1).T)
            sims = torch.cat(sims)
            sims, idx = torch.sort(sims, descending=True)
            sim_mask = sims > sim_th
            sims = sims[sim_mask]
            idx = idx[sim_mask]
            results = []
            for i, sim in zip(idx, sims):
                if self.us[i] in self.meta:
                    if filter_fn is None or filter_fn(self.meta[self.us[i]]):
                        results.append(dict(self.meta[self.us[i]], sim=sim))
                        if len(results) >= top:
                            break
            return results

        def get_filter_fn():
            face_min = 0
            face_max = 34985808
            anim_min = 0
            anim_max = 563
            anim_n = not (anim_min > 0 or anim_max < 563)
            face_n = not (face_min > 0 or face_max < 34985808)
            filter_fn = lambda x: (
                (anim_n or anim_min <= x['anims'] <= anim_max)
                and (face_n or face_min <= x['faces'] <= face_max)
            )
            return filter_fn            

        def get_model_dimensions(file_path):
            mesh = trimesh.load(file_path)
            bounding_box = mesh.bounding_box.extents
            length = bounding_box[0] / 100
            width = bounding_box[2] / 100
            height = bounding_box[1] / 100
            return length, width, height

        # Extract objects from designer_response
        objects = extract_list_from_json(self.designer_response, 'objects')
        for obj in objects:
            text = preprocess("A high-poly " + obj['object_id']) + f" with {obj['material']} material and in {obj['style']} style, high quality"
            device = self.clip_model.device
            tn = self.clip_prep(
                text=[text], return_tensors='pt', truncation=True, max_length=76
            ).to(device)
            enc = self.clip_model.get_text_features(**tn).float().cpu()

            retrieved_local = retrieve_local(enc, top=1, sim_th=0.5)
            if retrieved_local:
                retrieved_obj = retrieved_local[0]
                print("Retrieved object: ", retrieved_obj["file_path"])

                # destination_folder = os.path.join(os.getcwd(), f"Assets/")
                # if not os.path.exists(destination_folder):
                #     os.makedirs(destination_folder)
                source_file = retrieved_obj["file_path"]
                file_extension = os.path.splitext(source_file)[1]
                # destination_path = os.path.join(destination_folder, f"{obj['object_id']}{file_extension}")
                # shutil.copy(source_file, destination_path)
                # print(f"File moved to {destination_path}")

                if retrieved_obj["sim"] > 0.5:
                    length, width, height = map(float, retrieved_obj["bbx"].split(','))
                    obj['bounding_box_size'] = {'Length': length, 'Width': width, 'Height': height}
            else:
                retrieved_obj = retrieve(enc, top=1, sim_th=0.1, filter_fn=get_filter_fn())[0]
                print(f"Retrieved object from Objaverse: {retrieved_obj['u']}")
                processes = multiprocessing.cpu_count()
                objaverse_objects = objaverse.load_objects(
                    uids=[retrieved_obj['u']],
                    download_processes=processes
                )
                # destination_folder = os.path.join(os.getcwd(), f"Assets/")
                # if not os.path.exists(destination_folder):
                #     os.makedirs(destination_folder)
                for item_id, file_path in objaverse_objects.items():
                    # destination_path = f"{destination_folder}{obj['object_id']}.glb"
                    # shutil.move(file_path, destination_path)
                    # print(f"File {item_id} moved from {file_path} to {destination_path}")

                    if retrieved_obj["sim"] > 0.18:
                        length, width, height = get_model_dimensions(file_path)
                        obj['bounding_box_size'] = {'Length': length, 'Width': width, 'Height': height}

        self.designer_response['objects'] = objects
        print(self.designer_response)

    def create_scene_graph(self):
        cot_data_1 = []
        user_proxy, interior_architect, schema_engineer = create_graph_agents()
        
        scene_graph_groupchat = SceneGraphGroupChat(
            agents =[user_proxy, interior_architect, schema_engineer],
            messages=[],
            max_round=10
        )

        cot_data, json_info, json_data = {}, {}, {}
        blocks_designer = extract_list_from_json(self.designer_response, 'objects')

        for d_block in blocks_designer:
            object_id = d_block["object_id"]
            prompt = str(d_block)

            manager_scene_graph = GroupChatManager(groupchat=scene_graph_groupchat,
                                       llm_config=gpt4_config,
                                       human_input_mode="NEVER",
                                       is_termination_msg=is_termination_msg)

            user_proxy.initiate_chat(
                manager_scene_graph,
                message=f"""
                The room has the size {self.room_dimensions[0]}m x {self.room_dimensions[1]}m x {self.room_dimensions[2]}m
                User Input (in triple backquotes):
                ```
                {self.user_input}
                ```
                Room layout elements in the room (in triple backquotes):
                ```
                ['south_wall', 'north_wall', 'west_wall', 'east_wall', 'middle of the floor', 'ceiling']
                ```
                Previously placed objects in the room (in triple backquotes):
                ```
                {json_data}
                ```
                Object to be placed (in triple backticks):
                ```
                {prompt}
                ```
                """,
            )

            if not json_info:
                json_info["objects_in_room"] = []
            json_info["objects_in_room"] += json.loads(scene_graph_groupchat.messages[-2]["content"])["objects_in_room"]
            object_data = json.loads(scene_graph_groupchat.messages[-2]["content"])["objects_in_room"][0]

            if 'new_object_id' in object_data:
                del object_data['new_object_id']

            json_data[str(object_id)] = object_data

            if str(object_id) not in cot_data:
                cot_data[str(object_id)] = []

            indices_to_collect = list(range(1, len(scene_graph_groupchat.messages), 2))
            for idx in indices_to_collect:
                cot_data[str(object_id)].append(json.loads(scene_graph_groupchat.messages[idx]["content"])["chain_of_thought"])

            user_proxy.reset(), interior_architect.reset(), schema_engineer.reset(), scene_graph_groupchat.reset()

        self.cot_info["scene_graph_cot"] = cot_data
        self.scene_graph = json_info
        self.conflict_data = []

        # TODO: Modify
        scene_graph = preprocess_scene_graph(json_info["objects_in_room"], cot_data_1)
        G = build_graph(scene_graph)
        G = remove_unnecessary_edges(G, cot_data_1)
        G, scene_graph = handle_under_prepositions(G, scene_graph, cot_data_1)
        conflicts = get_conflicts(G, scene_graph, cot_data_1)

        print("-------------------CONFLICTS-------------------")
        for conflict in conflicts:
            print(conflict)
            print("\n\n")
        self.conflict_data.append(conflicts)

        user_proxy, spatial_corrector_agent, json_schema_debugger, object_deletion_agent = get_corrector_agents()

        while len(conflicts) > 0:
            spatial_corrector_agent.reset(), json_schema_debugger.reset()
            groupchat = LayoutCorrectorGroupChat(
                agents  =[user_proxy, spatial_corrector_agent, json_schema_debugger],
                messages=[],
                max_round=15
            )
            manager = GroupChatManager(groupchat=groupchat, llm_config=gpt4_config, is_termination_msg=is_termination_msg)
            user_proxy.initiate_chat(
                manager,
                message=f"""
                {conflicts[0]}
                """,
            )
            correction = groupchat.messages[-2]
            pattern = r'```json\s*([^`]+)\s*```' # Match the json object
            match = re.search(pattern, correction["content"], re.DOTALL).group(1)
            correction_json = json.loads(match)
            self.conflict_data.append(correction_json)
            corr_obj = get_object_from_scene_graph(correction_json["corrected_object"]["new_object_id"], scene_graph)
            corr_obj["is_on_the_floor"] = correction_json["corrected_object"]["is_on_the_floor"]
            corr_obj["facing"] = correction_json["corrected_object"]["facing"]
            corr_obj["placement"] = correction_json["corrected_object"]["placement"]
            G = build_graph(scene_graph)
            conflicts = get_conflicts(G, scene_graph, cot_data_1)

        size_conflicts = get_size_conflicts(G, scene_graph, cot_data_1, self.user_input, self.room_priors)

        print("-------------------SIZE CONFLICTS-------------------")
        for conflict in size_conflicts:
            print(conflict)
            print("\n\n")
        self.conflict_data.append(size_conflicts)

        while len(size_conflicts) > 0:
            object_deletion_agent.reset()
            groupchat = ObjectDeletionGroupChat(
                agents  =[user_proxy, object_deletion_agent],
                messages=[],
                max_round=2
            )
            manager = GroupChatManager(groupchat=groupchat, llm_config=gpt4_config, is_termination_msg=is_termination_msg)
            user_proxy.initiate_chat(
                manager,
                message=f"""
                {size_conflicts[0]}
                """,
            )
            correction = groupchat.messages[-1]
            correction_json = json.loads(correction["content"])
            object_to_delete = correction_json["object_to_delete"]
            descendants = nx.descendants(G, object_to_delete)
            objs_to_delete = descendants.union({object_to_delete})
            print("Objs to Delete: ", objs_to_delete)
            self.conflict_data.append(f"Objs to Delete: {objs_to_delete}")
            scene_graph = [x for x in scene_graph if x["new_object_id"] not in objs_to_delete]
            for obj in objs_to_delete:
                G.remove_node(obj)

            size_conflicts = get_size_conflicts(G, scene_graph, cot_data_1, self.user_input, self.room_priors)

        self.scene_graph["objects_in_room"] = scene_graph

    def summary_language(self):
        user_proxy, language_architect = language_summary_agents()

        groupchat = LanguageGroupChat(
            agents=[user_proxy, language_architect],
            messages=[],
            max_round=2
        )

        manager = GroupChatManager(groupchat=groupchat, llm_config=gpt4_config, is_termination_msg=is_termination_msg)

        user_proxy.initiate_chat(
            manager,
            message=f"""
            The room has the size {self.room_dimensions[0]}m x {self.room_dimensions[1]}m x {self.room_dimensions[2]}m
            User Input (in triple backquotes):
            ```
            **chain of thought for requirements_analyzer, substructure_analyzer and interior_designer**
            {self.cot_info["parse_cot"]}
            ```
            **chain of thought for object placement**
            {self.cot_info["scene_graph_cot"]}
            ```
            **conflict data**
            {self.conflict_data}
            ```
            **scene graph**
            {self.scene_graph}
            ```
            Room layout elements in the room (in triple backquotes):
            ```
            ['south_wall', 'north_wall', 'west_wall', 'east_wall', 'middle of the room', 'ceiling']
            ```
            json
            """,
        )

        self.language_sum = groupchat.messages[-1]["content"]

    def create_layout(self, debug=False):
        # self.scene_graph = {'objects_in_room': [{'new_object_id': 'pool_table_1', 'style': 'modern', 'material': 'wood', 'functionality': 'playing', 'color': 'black', 'size_in_meters': {'length': 2.84, 'width': 1.42, 'height': 0.8}, 'is_on_the_floor': True, 'facing': 'north_wall', 'placement': {'room_layout_elements': [{'layout_element_id': 'middle of the room', 'preposition': 'on'}], 'objects_in_room': []}}, {'new_object_id': 'overhead_light_1', 'style': 'modern', 'material': 'metal', 'functionality': 'lighting', 'color': 'silver', 'size_in_meters': {'length': 1.0, 'width': 0.3, 'height': 0.3}, 'is_on_the_floor': False, 'facing': 'downwards', 'placement': {'room_layout_elements': [{'layout_element_id': 'ceiling', 'preposition': 'on'}], 'objects_in_room': [{'object_id': 'pool_table_1', 'preposition': 'above', 'is_adjacent': False}]}}, {'new_object_id': 'bar_stool_1', 'style': 'modern', 'material': 'metal', 'functionality': 'seating', 'color': 'black', 'size_in_meters': {'length': 0.45, 'width': 0.45, 'height': 0.75}, 'is_on_the_floor': True, 'facing': 'north_wall', 'placement': {'room_layout_elements': [], 'objects_in_room': [{'object_id': 'pool_table_1', 'preposition': 'right of', 'is_adjacent': False}]}}, {'new_object_id': 'bar_stool_2', 'style': 'modern', 'material': 'metal', 'functionality': 'seating', 'color': 'black', 'size_in_meters': {'length': 0.45, 'width': 0.45, 'height': 0.75}, 'is_on_the_floor': True, 'facing': 'north_wall', 'placement': {'room_layout_elements': [], 'objects_in_room': [{'object_id': 'pool_table_1', 'preposition': 'left of', 'is_adjacent': False}]}}, {'new_object_id': 'rug_1', 'style': 'modern', 'material': 'fabric', 'functionality': 'decor', 'color': 'grey', 'size_in_meters': {'length': 3.0, 'width': 2.0, 'height': 0.01}, 'is_on_the_floor': True, 'facing': 'north_wall', 'placement': {'room_layout_elements': [{'layout_element_id': 'middle of the room', 'preposition': 'on'}], 'objects_in_room': [{'object_id': 'pool_table_1', 'preposition': 'under', 'is_adjacent': False}]}}, {'new_object_id': 'scoreboard_1', 'style': 'modern', 'material': 'electronic', 'functionality': 'score keeping', 'color': 'black', 'size_in_meters': {'length': 0.6, 'width': 0.02, 'height': 0.4}, 'is_on_the_floor': False, 'facing': 'north_wall', 'placement': {'room_layout_elements': [{'layout_element_id': 'west_wall', 'preposition': 'on'}], 'objects_in_room': []}}]}

        cot_data = []
        G = build_graph(self.scene_graph["objects_in_room"])
        nodes = G.nodes()

        cot_data.append("Calculate constraint area for non-layout objects only.")
        for node in nodes:
            if node not in self.layout_elements:
                cluster_size, _ = get_cluster_size(node, G, self.scene_graph["objects_in_room"], cot_data)
                node_obj = get_object_from_scene_graph(node, self.scene_graph["objects_in_room"])
                cluster_size = {"x_neg" : cluster_size["left of"], "x_pos" : cluster_size["right of"], "y_neg" : cluster_size["behind"], "y_pos" : cluster_size["in front"]}
                node_obj["cluster"] = {"constraint_area" : cluster_size}
                cot_data.append(f"The constraint area for {node} is {cluster_size}.")

        self.scene_graph = self.scene_graph["objects_in_room"] + self.room_priors

        prior_ids = ["south_wall", "north_wall", "east_wall", "west_wall", "ceiling", "middle of the room"]
        point_bbox = dict.fromkeys([item["new_object_id"] for item in self.scene_graph], False)

        # Place the objects that have an absolute position
        for item in self.scene_graph:
            if item["new_object_id"] in prior_ids:
                continue
            possible_pos = get_possible_positions(item["new_object_id"], self.scene_graph, self.room_dimensions, cot_data)
            # Determine the overlap based on the possible positions
            overlap = None
            if len(possible_pos) == 1:
                overlap = possible_pos[0]
            elif len(possible_pos) > 1:
                overlap = possible_pos[0]
                for pos in possible_pos[1:]:
                    overlap = calculate_overlap(overlap, pos)
            # If the overlap is a point bbox, assign the position
            if overlap is not None and is_point_bbox(overlap) and len(possible_pos) > 0:
                item["position"] = {"x" : overlap[0], "y" : overlap[2], "z" : overlap[4]}
                point_bbox[item["new_object_id"]] = True

        scene_graph_wo_layout = [item for item in self.scene_graph if item["new_object_id"] not in self.layout_elements]

        depth_scene_graph = get_depth(scene_graph_wo_layout)
        max_depth = max(depth_scene_graph.values())

        topological_order = get_topological_ordering(scene_graph_wo_layout)
        topological_order = [item for item in topological_order if item not in self.layout_elements]

        d = 1
        count = 0
        while d <= max_depth and count < 20:
            count += 1
            error_flag = False

            nodes = [node for node in topological_order if depth_scene_graph[node] == d]
            if debug:
                print(f"Nodes at depth {d}: ", nodes)

            errors = {}

            cot_data.append(f"Place objects: {[node for node in nodes]}.")
            for node in nodes:
                if point_bbox[node]:
                    continue

                obj = next(item for item in scene_graph_wo_layout if item["new_object_id"] == node)
                cot_data.append(f"Place the object {obj['new_object_id']} at the depth {d}.")
                errors = place_object(obj, self.scene_graph, self.room_dimensions, cot_data, errors={}, debug=debug)

                if debug:
                    print(f"Errors for {obj['new_object_id']}: ", errors)

                # cot_data.append(f"Check whether there are any errors in placing {obj['new_object_id']}.")
                if errors:
                    if d > 1:
                        d -= 1
                        cot_data.append(f"Errors occur for {obj['new_object_id']}: {errors}. Reduce depth to {d}.")
                        if debug:
                            print("Reducing depth to: ", d)
                    else:
                        cot_data.append(f"Errors occur for {obj['new_object_id']} with depth 1: {errors}. The layout creation failed.")
                        print(f"Errors occur for {obj['new_object_id']} with depth 1: {errors}. The layout creation failed.")
                        self.calculation_data = []
                        return errors

                    error_flag = True
                    cot_data.append(f"Delete positions for objects at or beyond the current depth {d} in order to reposition the objects.")
                    for del_item in scene_graph_wo_layout:
                        if depth_scene_graph[del_item["new_object_id"]] >= d:
                            if "position" in del_item.keys() and not point_bbox[del_item["new_object_id"]]:
                                if debug:
                                    print("Deleting position for: ", del_item["new_object_id"])
                                del del_item["position"]
                    errors = {}
                    break
                # else:
                #     cot_data.append(f"No error is found.")

            if not error_flag:
                d += 1

        cot_data.append("Save the scene graph.")
        self.calculation_data = cot_data
        print(cot_data)
        print("\n")

        os.makedirs("./results", exist_ok=True)
        jsonname = re.sub(r'[^a-zA-Z0-9]', '_', self.user_input) + '.json'
        self.result_file = os.path.join("./results", jsonname)
        with open(self.result_file, "w") as file:
            json.dump(self.scene_graph, file, indent=4)

    def summary_calculation(self):
        if self.calculation_data:
            user_proxy, calculation_architect = calculation_summary_agents()
            groupchat = CalculationGroupChat(
                agents=[user_proxy, calculation_architect],
                messages=[],
                max_round=2
            )
            manager = GroupChatManager(groupchat=groupchat, llm_config=gpt4_config, is_termination_msg=is_termination_msg)

            user_proxy.initiate_chat(
                manager,
                message=f"""
                The room has the size {self.room_dimensions[0]}m x {self.room_dimensions[1]}m x {self.room_dimensions[2]}m
                User Input (in triple backquotes):
                ```
                {self.calculation_data}
                ```
                Room layout elements in the room (in triple backquotes):
                ```
                ['south_wall', 'north_wall', 'west_wall', 'east_wall', 'middle of the room', 'ceiling']
                ```
                json
                """,
            )

            self.calculation_sum = groupchat.messages[-1]["content"]

            os.makedirs("./Results_data", exist_ok=True)
            filename = re.sub(r'[^a-zA-Z0-9]', '_', self.user_input) + '.md'
            full_path = os.path.join("./Results_data", filename)
            with open(full_path, 'w', encoding='utf-8') as file:
                file.write(self.language_sum)
                file.write('\n\n## 6. **Object Placement**\n')
                file.write(self.calculation_sum)
        else:
            pass