import os
import json
import numpy as np
import torch
from PIL import Image, ImageDraw
import gradio as gr
from openai import OpenAI
from geopy.geocoders import Nominatim
from staticmap import StaticMap, CircleMarker, Polygon
from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline
import spaces
import logging
from pydantic import BaseModel, ValidationError, Field
from typing import List, Union

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Initialize APIs
openai_client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
geolocator = Nominatim(user_agent="geoapi")

# Define Pydantic models for GeoJSON validation
class Geometry(BaseModel):
    type: str
    coordinates: Union[List[List[float]], List[List[List[float]]]]

class Feature(BaseModel):
    type: str = "Feature"
    properties: dict
    geometry: Geometry

class FeatureCollection(BaseModel):
    type: str = "FeatureCollection"
    features: List[Feature]

# Function to fetch coordinates
@spaces.GPU
def get_geo_coordinates(location_name):
    try:
        location = geolocator.geocode(location_name)
        if location:
            return [location.longitude, location.latitude]
        return None
    except Exception as e:
        logger.error(f"Error fetching coordinates for {location_name}: {e}")
        return None

# Function to process OpenAI chat response
@spaces.GPU
def process_openai_response(query):
    response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "You are a skilled assistant answering geographical and historical questions in JSON format."
            },
            {
                "role": "user",
                "content": query
            }
        ],
        temperature=1,
        max_tokens=2048,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

# Generate GeoJSON from OpenAI response
@spaces.GPU
def generate_geojson(response):
    feature_type = response['output']['feature_representation']['type']
    city_names = response['output']['feature_representation']['cities']
    properties = response['output']['feature_representation']['properties']

    coordinates = []
    for city in city_names:
        coord = get_geo_coordinates(city)
        if coord:
            coordinates.append(coord)

    if feature_type == "Polygon":
        coordinates.append(coordinates[0])  # Close the polygon

    geojson_data = {
        "type": "FeatureCollection",
        "features": [
            {
                "type": "Feature",
                "properties": properties,
                "geometry": {
                    "type": feature_type,
                    "coordinates": [coordinates] if feature_type == "Polygon" else coordinates
                }
            }
        ]
    }

    # Validate the generated GeoJSON using Pydantic
    try:
        validated_geojson = FeatureCollection(**geojson_data)
        logger.info("GeoJSON validation successful.")
        return validated_geojson.dict()
    except ValidationError as e:
        logger.error(f"GeoJSON validation failed: {e}")
        raise

# Generate static map image
@spaces.GPU
def generate_static_map(geojson_data, invisible=False):
    m = StaticMap(600, 600)
    logger.info(f"GeoJSON data: {geojson_data}")

    for feature in geojson_data["features"]:
        geom_type = feature["geometry"]["type"]
        coords = feature["geometry"]["coordinates"]

        if geom_type == "Point":
            m.add_marker(CircleMarker((coords[0][0], coords[0][1]), '#1C00ff00' if invisible else 'blue', 1000))
        elif geom_type in ["MultiPoint", "LineString"]:
            for coord in coords:
                m.add_marker(CircleMarker((coord[0], coord[1]), '#1C00ff00' if invisible else 'blue', 1000))
        elif geom_type in ["Polygon", "MultiPolygon"]:
            for polygon in coords:
                m.add_polygon(Polygon([(c[0], c[1]) for c in polygon], '#1C00ff00' if invisible else 'blue', 3))

    return m.render()

# ControlNet pipeline setup
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16)
pipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
)
pipeline.to('cuda')

@spaces.GPU
def make_inpaint_condition(init_image, mask_image):
    init_image = np.array(init_image.convert("RGB")).astype(np.float32) / 255.0
    mask_image = np.array(mask_image.convert("L")).astype(np.float32) / 255.0

    assert init_image.shape[0:1] == mask_image.shape[0:1], "image and image_mask must have the same image size"
    init_image[mask_image > 0.5] = -1.0  # set as masked pixel
    init_image = np.expand_dims(init_image, 0).transpose(0, 3, 1, 2)
    init_image = torch.from_numpy(init_image)
    return init_image

@spaces.GPU
def generate_satellite_image(init_image, mask_image, prompt):
    control_image = make_inpaint_condition(init_image, mask_image)
    result = pipeline(
        prompt=prompt, 
        image=init_image, 
        mask_image=mask_image, 
        control_image=control_image,
        strength=0.42,
        guidance_scale=62
    )
    return result.images[0]

# Gradio UI
@spaces.GPU
def handle_query(query):
    response = process_openai_response(query)
    geojson_data = generate_geojson(response)

    map_image = generate_static_map(geojson_data)
    empty_map_image = generate_static_map(geojson_data, invisible=True)

    difference = np.abs(np.array(map_image.convert("RGB")) - np.array(empty_map_image.convert("RGB")))
    threshold = 10
    mask = (np.sum(difference, axis=-1) > threshold).astype(np.uint8) * 255

    mask_image = Image.fromarray(mask, mode="L")
    satellite_image = generate_satellite_image(
        empty_map_image, mask_image, response['output']['feature_representation']['properties']['description']
    )

    return map_image, empty_map_image, satellite_image, mask_image, response

def update_query(selected_query):
    return selected_query

query_options = [
    "Area covering south asian subcontinent",
    "Due to considerable rainfall in the up- and mid- stream areas of Kala Oya, the Rajanganaya reservoir is now spilling at a rate of 17,000 cubic feet per second, the department said."
]

with gr.Blocks() as demo:
    with gr.Row():
        selected_query = gr.Dropdown(label="Select Query", choices=query_options, value=query_options[-1])
        query_input = gr.Textbox(label="Enter Query", value=query_options[-1])
        selected_query.change(update_query, inputs=selected_query, outputs=query_input)
        submit_btn = gr.Button("Submit")
    with gr.Row():
        map_output = gr.Image(label="Map Visualization")
        empty_map_output = gr.Image(label="Empty Visualization")
    with gr.Row():
        satellite_output = gr.Image(label="Generated Satellite Image")
        mask_output = gr.Image(label="Mask")
        image_prompt = gr.Textbox(label="Image Prompt Used")
    submit_btn.click(handle_query, inputs=[query_input], outputs=[map_output, empty_map_output, satellite_output, mask_output, image_prompt])

if __name__ == "__main__":
    demo.launch()