import time
import io
import gradio as gr
import cv2
import base64
import openai

from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.schema import StrOutputParser
from PIL import Image

global_dict = {}


def failure():
    raise gr.Error("This should fail!")

def validate_api_key(api_key):
    client = openai.OpenAI(api_key=api_key)

    try:
        # Make your OpenAI API request here
        response = client.completions.create(
            prompt="Hello world",
            model="gpt-3.5-turbo-instruct"
        )
    except openai.RateLimitError as e:
        # Handle rate limit error (we recommend using exponential backoff)
        print(f"OpenAI API request exceeded rate limit: {e}")
        response = None
        pass
    except openai.APIConnectionError as e:
        # Handle connection error here
        print(f"Failed to connect to OpenAI API: {e}")
        response = None
        pass
    except openai.APIError as e:
        # Handle API error here, e.g. retry or log
        print(f"OpenAI API returned an API Error: {e}")
        response = None
        pass

    if response:
        return True
    else:
        raise gr.Error(f"OpenAI API returned an API Error")


def _process_video(image_file):
    # Read and process the video file
    video = cv2.VideoCapture(image_file)

    base64Frames = []
    while video.isOpened():
        success, frame = video.read()
        if not success:
            break
        _, buffer = cv2.imencode(".jpg", frame)
        base64Frames.append(base64.b64encode(buffer).decode("utf-8"))
    video.release()
    if len(base64Frames) > 400:
        raise gr.Warning(f"Video's play time is too long. (>10s)")
    print(len(base64Frames), "frames read.")

    if not base64Frames:
        raise gr.Error(f"Cannot open the video.")
    return base64Frames


def _make_video_batch(image_file, total_batch_percent):

    frames = _process_video(image_file)

    TOTAL_FRAME_COUNT = len(frames)
    BATCH_SIZE = 5
    TOTAL_BATCH_SIZE = int(TOTAL_FRAME_COUNT * total_batch_percent / 100)
    BATCH_STEP = int(TOTAL_FRAME_COUNT / TOTAL_BATCH_SIZE)
    
    base64FramesBatch = []

    for idx in range(0, TOTAL_FRAME_COUNT, BATCH_STEP * BATCH_SIZE):
        # print(f'## {idx}')
        temp = []
        for i in range(BATCH_SIZE):
            # print(f'# {idx + BATCH_STEP * i}')
            if (idx + BATCH_STEP * i) < TOTAL_FRAME_COUNT:
                temp.append(frames[idx + BATCH_STEP * i])
            else:
                continue
        base64FramesBatch.append(temp)
    
    for idx, batch in enumerate(base64FramesBatch):
        # assert len(batch) <= BATCH_SIZE
        print(f'##{idx} - batch_size: {len(batch)}')

    global_dict.setdefault('batched_frames', base64FramesBatch)
    return base64FramesBatch


def show_batches(image_file, total_batch_size):
    
    batched_frames = _make_video_batch(image_file, total_batch_size)
    
    images = []
    for i, l in enumerate(batched_frames):
        print(f"#### Batch_{i+1}")
        for j, img in enumerate(l):
            print(f'## Image_{j+1}')
            image_bytes = base64.b64decode(img.encode("utf-8"))
            # Convert the bytes to a stream (file-like object)
            image_stream = io.BytesIO(image_bytes)
            # Open the image as a PIL image
            image = Image.open(image_stream)
            images.append((image, f"batch {i+1}"))
        print("-"*100)
    
    return images


def call_gpt_vision(api_key, instruction):
    frames = global_dict.get('batched_frames')
    openai.api_key = api_key

    full_result = []

    for idx, batch in enumerate(frames):
        PROMPT_MESSAGES = [
            {
                "role": "system",
                "content": "You will evaluate the behavior of the person in the sequences of images. They show discrete parts of the whole continuous behavior. You should only evaluate the parts you can rate based on the given images. Remember, you're evaluating the given parts to evaluate the whole continuous behavior, and you'll connect them later to evaluate the whole. Never add your own judgment. Evlaute only in the contents of images themselves. If you can't evaluate it, just answer '(Unevaluable)'"
            },
            {
                "role": "user",
                "content": [
                    "Evaluate the behavior's actions based on the <CRITERIA> provided.\n\n" + instruction,
                    *map(lambda x: {"image": x, "resize": 300}, batch),
                ],
            },
        ]
        
        params = {
        "model": "gpt-4-vision-preview",
        "messages": PROMPT_MESSAGES,
        "max_tokens": 1024,
        }

        try:
            result = openai.chat.completions.create(**params)
            print(result.choices[0].message.content)
            full_result.append(result)
        except Exception as e:
            print(f"Error: {e}")
            pass
        
        if 'full_result' not in global_dict:
            global_dict.setdefault('full_result', full_result)
        else:
            global_dict['full_result'] = full_result
        
        print(f'### BATCH_{idx+1}')
        print('-'*100)
        time.sleep(2)

        yield f'### BATCH_{idx+1}\n' + "-"*50 + "\n" + result.choices[0].message.content +  "\n" + "-"*50


def get_full_result():
    full_result = global_dict.get('full_result')
    
    result_text = ""

    for idx, res in enumerate(full_result):
        result_text += f'<Evaluation_{idx+1}>\n'
        result_text += res.choices[0].message.content
        result_text += "\n"
        result_text += "-"*5
        result_text += "\n"
    
    global_dict.setdefault('result_text', result_text)

    return result_text


def get_final_anser(api_key, result_text):
    chain = ChatOpenAI(model="gpt-4", max_tokens=1024, temperature=0, api_key=api_key)
    prompt = PromptTemplate.from_template(
    """
    You see the following list of texts that evaluate forward roll:
    {evals}
    Write an full text that synthesizes and summarizes the contents of all the text above.
    Each evaluates a specific part, and you should combine them based on what was evaluated in each part.
    The way to combine them is 'or', not 'and', which means you only need to evaluate the parts of a post that are rated based on that.
    Concatenate based on what was evaluated, if anything.

    Example:
    an overview of evaluations
    1. Specific assessments for each item
    2.
    3.
    ....
    Overall opinion

    Total score : 1~10 / 10

    Output:
    """
    )
    runnable = prompt | chain | StrOutputParser()

    final_eval = runnable.invoke({"evals": result_text})
    return final_eval


# Define the Gradio app
def main():
    with gr.Blocks() as demo:
        gr.Markdown("# GPT-4 Vision for Evaluation")
        gr.Markdown("## 1st STEP. Make Batched Snapshots")
        with gr.Row():
            with gr.Column(scale=1):
                api_key_input = gr.Textbox(
                    label="Enter your OpenAI API Key",
                    info="Your API Key must be allowed to use GPT-4 Vision",
                    placeholder="sk-*********...",
                    lines=1
                )
                video_upload = gr.File(label="Upload your video (under 10 second video is the best..!)")
                # batch_size = gr.Number(
                #     label="Number of images in one batch",
                #     value=2,
                #     minimum=2,
                #     maximum=5
                # )
                total_batch_percent = gr.Number(
                    label="Percentage(%) of batched image frames to total frames",
                    value=10,
                    minimum=10,
                    maximum=40,
                    step=5
                )
                process_button = gr.Button("Process")
            
            with gr.Column(scale=1):
                gallery = gr.Gallery(
                    label="Batched Snapshots of Video",
                    columns=[5],
                    rows=[1],
                    object_fit="contain",
                    height="auto"
                )
        gr.Markdown("## 2nd STEP. Set Evaluation Criteria")
        with gr.Row():
            with gr.Column(scale=1):
                instruction_input = gr.Textbox(
                    label="Evaluation Criteria",
                    info="Enter your evaluation criteria here...",
                    placeholder="<CRITERIA>\nThe correct way to do a forward roll is as follows:\n1. From standing, bend your knees and straighten your arms in front of you.\n2. Place your hands on the floor, shoulder width apart with fingers pointing forward and your chin on your chest.\n3. Rock forward, straighten legs and transfer body weight onto shoulders.\n4. Rock forward on a rounded back placing both feet on the floor.\n5. Stand using arms for balance, without hands touching the floor.",
                    lines=7)
                submit_button = gr.Button("Submit")

            with gr.Column(scale=1):
                output_box = gr.Textbox(
                    label="Batched Generated Response...(Streaming)",
                    lines=10,
                    interactive=False
                )
        gr.Markdown("## 3rd STEP. Summarize and Get Result")
        with gr.Row():
            with gr.Column(scale=1):
                output_box_fin = gr.Textbox(
                    label="FULL Response",
                    info="You can edit partial evaluation in here...",
                    lines=10,
                    interactive=True)
                submit_button_2 = gr.Button("Submit")

            with gr.Column(scale=1):
                output_box_fin_fin = gr.Textbox(label="FINAL EVALUATION", lines=10, interactive=True)


        process_button.click(fn=validate_api_key, inputs=api_key_input, outputs=None).success(fn=show_batches, inputs=[video_upload, total_batch_percent], outputs=gallery)
        submit_button.click(fn=call_gpt_vision, inputs=[api_key_input, instruction_input], outputs=output_box).then(get_full_result, None, output_box_fin)
        submit_button_2.click(fn=get_final_anser, inputs=[api_key_input, output_box_fin], outputs=output_box_fin_fin)

    demo.launch()

if __name__ == "__main__":
    main()