import gradio as gr
import io
import numpy as np
import torch
from decord import cpu, VideoReader, bridge
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import BitsAndBytesConfig
import json

MODEL_PATH = "THUDM/cogvlm2-llama3-caption"
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16

DELAY_REASONS = {
    "step1": {"reasons": ["Delay in Bead Insertion","Lack of raw material"]},
    "step2": {"reasons": ["Inner Liner Adjustment by Technician","Person rebuilding defective Tire Sections"]},
    "step3": {"reasons": ["Manual Adjustment in Ply1 apply","Technician repairing defective Tire Sections"]},
    "step4": {"reasons": ["Delay in Bead set","Lack of raw material"]},
    "step5": {"reasons": ["Delay in Turnup","Lack of raw material"]},
    "step6": {"reasons": ["Person Repairing sidewall","Person rebuilding defective Tire Sections"]},
    "step7": {"reasons": ["Delay in sidewall stitching","Lack of raw material"]},
    "step8": {"reasons": ["No person available to load Carcass","No person available to collect tire"]}
}

with open('delay_reasons.json', 'w') as f:
    json.dump(DELAY_REASONS, f, indent=4)

def load_video(video_data, strategy='chat'):
    bridge.set_bridge('torch')
    mp4_stream = video_data
    num_frames = 24
    decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))
    frame_id_list = []
    total_frames = len(decord_vr)
    timestamps = [i[0] for i in decord_vr.get_frame_timestamp(np.arange(total_frames))]
    max_second = round(max(timestamps)) + 1
    
    for second in range(max_second):
        closest_num = min(timestamps, key=lambda x: abs(x - second))
        index = timestamps.index(closest_num)
        frame_id_list.append(index)
        if len(frame_id_list) >= num_frames:
            break

    video_data = decord_vr.get_batch(frame_id_list)
    video_data = video_data.permute(3, 0, 1, 2)
    return video_data

def load_model():
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=TORCH_TYPE,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4"
    )
    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_PATH,
        torch_dtype=TORCH_TYPE,
        trust_remote_code=True,
        quantization_config=quantization_config,
        device_map="auto"
    ).eval()
    return model, tokenizer

def predict(prompt, video_data, temperature, model, tokenizer):
    strategy = 'chat'
    video = load_video(video_data, strategy=strategy)
    history = []
    inputs = model.build_conversation_input_ids(
        tokenizer=tokenizer,
        query=prompt,
        images=[video],
        history=history,
        template_version=strategy
    )
    inputs = {
        'input_ids': inputs['input_ids'].unsqueeze(0).to(DEVICE),
        'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to(DEVICE),
        'attention_mask': inputs['attention_mask'].unsqueeze(0).to(DEVICE),
        'images': [[inputs['images'][0].to(DEVICE).to(TORCH_TYPE)]],
    }
    gen_kwargs = {
        "max_new_tokens": 2048,
        "pad_token_id": 128002,
        "top_k": 1,
        "do_sample": False,
        "top_p": 0.1,
        "temperature": temperature,
    }
    with torch.no_grad():
        outputs = model.generate(**inputs, **gen_kwargs)
        outputs = outputs[:, inputs['input_ids'].shape[1]:]
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

def get_base_prompt():
    return """You are an expert AI model trained to analyze and interpret manufacturing processes. 
    The task is to evaluate video footage of specific steps in a tire manufacturing process. 
    The process has 8 total steps, but only delayed steps are provided for analysis.
    
    **Your Goal:**
    1. Analyze the provided video.
    2. Identify possible reasons for the delay in the manufacturing step shown in the video.
    3. Provide a clear explanation of the delay based on observed factors.
    
    **Context:** 
    Tire manufacturing involves 8 steps, and delays may occur due to machinery faults, 
    raw material availability, labor efficiency, or unexpected disruptions.
    
    **Output:**
    Explain why the delay occurred in this step. Include specific observations 
    and their connection to the delay."""

def inference(video, step_number, selected_reason):
    if not video:
        return "Please upload a video first."
    model, tokenizer = load_model()
    video_data = video.read()
    base_prompt = get_base_prompt()
    full_prompt = f"{base_prompt}\n\nAnalyzing Step {step_number}\nPossible reason: {selected_reason}"
    temperature = 0.3
    response = predict(full_prompt, video_data, temperature, model, tokenizer)
    return response

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            video = gr.Video(label="Video Input", sources=["upload"])
            step_number = gr.Dropdown(choices=[f"Step {i}" for i in range(1, 9)], label="Manufacturing Step", value="Step 1")
            reason = gr.Dropdown(choices=DELAY_REASONS["step1"]["reasons"], label="Possible Delay Reason", value=DELAY_REASONS["step1"]["reasons"][0])
            analyze_btn = gr.Button("Analyze Delay", variant="primary")
        with gr.Column():
            output = gr.Textbox(label="Analysis Result")

    def update_reasons(step):
        step_num = step.lower().replace(" ", "")
        return gr.Dropdown(choices=DELAY_REASONS[step_num]["reasons"])

    step_number.change(fn=update_reasons, inputs=[step_number], outputs=[reason])
    analyze_btn.click(fn=inference, inputs=[video, step_number, reason], outputs=[output])

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