app.py

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

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 = {
    "Step 1": ["Delay in Bead Insertion", "Lack of raw material"],
    "Step 2": ["Inner Liner Adjustment by Technician", "Person rebuilding defective Tire Sections"],
    "Step 3": ["Manual Adjustment in Ply1 apply", "Technician repairing defective Tire Sections"],
    "Step 4": ["Delay in Bead set", "Lack of raw material"],
    "Step 5": ["Delay in Turnup", "Lack of raw material"],
    "Step 6": ["Person Repairing sidewall", "Person rebuilding defective Tire Sections"],
    "Step 7": ["Delay in sidewall stitching", "Lack of raw material"],
    "Step 8": ["No person available to load Carcass", "No person available to collect tire"]
}

def analyze_video_step(step_name, observed_time, issues=""):
    """
    Analyzes a video step based on its name and observed time.

    Parameters:
    step_name (str): The name of the step.
    observed_time (int): Observed time taken for the step (in seconds).
    issues (str): Any specific issues noted during the analysis.

    Returns:
    str: Analysis result for the provided step.
    """
    match step_name:
        case "Bead Insertion":
            standard_time = 4
            analysis = "Missing beads, technician errors, or machinery malfunction.Technician is unavailable at the time of bead insertion."
        case "Inner Liner Apply":
            standard_time = 4
            analysis = "Manual intervention or alignment issues.If technician is manually repairing inner liner."
        case "Ply1 Apply":
            standard_time = 4
            analysis = "Manual adjustment suggesting improper placement or misalignment."
        case "Bead Set":
            standard_time = 8
            analysis = "Bead misalignment, machine pauses, or technician involvement."
        case "Turnup":
            standard_time = 4
            analysis = "Material misalignment or equipment issues."
        case "Sidewall Apply":
            standard_time = 14
            analysis = "Material damage or improper application.Technician repairing sidewall."
        case "Sidewall Stitching":
            standard_time = 5
            analysis = "Machine speed inconsistencies or manual correction."
        case "Carcass Unload":
            standard_time = 7
            analysis = "Absence of technician or delayed involvement.Technician not available to collect tire or load carcass"
        case _:
            return "Invalid step name. Please provide a valid step name."

    if observed_time > standard_time:
        return (
            f"Step: {step_name}\n"
            f"Standard Time: {standard_time} seconds\n"
            f"Observed Time: {observed_time} seconds\n"
            f"Analysis: Delay detected. Potential issues: {analysis} {issues}"
        )
    else:
        return (
            f"Step: {step_name}\n"
            f"Standard Time: {standard_time} seconds\n"
            f"Observed Time: {observed_time} seconds\n"
            "Analysis: Step completed within standard time."
        )

def load_video(video_data, strategy='chat'):
    """Loads and processes video data into a format suitable for model input."""
    bridge.set_bridge('torch')
    num_frames = 24
    
    if isinstance(video_data, str): 
        decord_vr = VideoReader(video_data, ctx=cpu(0))
    else:  
        decord_vr = VideoReader(io.BytesIO(video_data), 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():
    """Loads the pre-trained model and tokenizer with quantization configurations."""
    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):
    """Generates predictions based on the video and textual prompt."""
    video = load_video(video_data, strategy='chat')
    
    inputs = model.build_conversation_input_ids(
        tokenizer=tokenizer,
        query=prompt,
        images=[video],
        history=[],
        template_version='chat'
    )
    
    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_analysis_prompt(step_number, possible_reasons):
    """Constructs the prompt for analyzing delay reasons based on the selected step."""
    return f"""You are an AI expert system specialized in analyzing manufacturing processes and identifying production delays in tire manufacturing. Your role is to accurately classify delay reasons based on visual evidence from production line footage.
    Task Context:
    You are analyzing video footage from Step {step_number} of a tire manufacturing process where a delay has been detected. Your task is to determine the most likely cause of the delay from the following possible reasons:
    {', '.join(possible_reasons)}
    Required Analysis:
     Carefully observe the video for visual cues indicating production interruption.
     If no person is visible in any of the frames, the reason probably might be due to his absence.
     If a person is visible in the video and is observed touching and modifying the layers of the tire, it means there is a issue with tyre being patched hence he is repairing it.
     Compare observed evidence against each possible delay reason.
     Select the most likely reason based on visual evidence.
    Please provide your analysis in the following format:
    1. Selected Reason: [State the most likely reason from the given options]
    2. Visual Evidence: [Describe specific visual cues that support your selection]
    3. Reasoning: [Explain why this reason best matches the observed evidence]
    4. Alternative Analysis: [Brief explanation of why other possible reasons are less likely]
    Important: Base your analysis solely on visual evidence from the video. Focus on concrete, observable details rather than assumptions. Clearly state if no person or specific activity is observed."""

model, tokenizer = load_model()

def inference(video, step_number, observed_time, issues=""):
    """Analyzes video and additional step data for delay analysis."""
    try:
        if not video:
            return "Please upload a video first."
        
        possible_reasons = DELAY_REASONS[step_number]
        prompt = get_analysis_prompt(step_number, possible_reasons)
        temperature = 0.8
        video_response = predict(prompt, video, temperature, model, tokenizer)
        step_analysis = analyze_video_step(step_number, observed_time, issues)
        
        return f"Video Analysis:\n{video_response}\n\nStep Analysis:\n{step_analysis}"
    except Exception as e:
        return f"An error occurred during analysis: {str(e)}"

def create_interface():
    """Creates the Gradio interface for the Manufacturing Delay Analysis System with examples."""
    with gr.Blocks() as demo:
        gr.Markdown("""
        # Manufacturing Delay Analysis System
        Upload a video of the manufacturing step and select the step number. 
        The system will analyze the video and determine the most likely cause of delay.
        """)
        
        with gr.Row():
            with gr.Column():
                video = gr.Video(label="Upload Manufacturing Video", sources=["upload"])
                step_number = gr.Dropdown(
                    choices=list(DELAY_REASONS.keys()),
                    label="Manufacturing Step"
                )
                analyze_btn = gr.Button("Analyze Delay", variant="primary")
            
            with gr.Column():
                output = gr.Textbox(label="Analysis Result", lines=10)
        
        # Add examples
        examples = [
            ["7838_step2_2_eval.mp4", "Step 2"],
            ["7838_step6_2_eval.mp4", "Step 6"],
            ["7838_step8_1_eval.mp4", "Step 8"],
            ["7993_step6_3_eval.mp4", "Step 6"],
            ["7993_step8_3_eval.mp4", "Step 8"]
            
        ]
        
        gr.Examples(
            examples=examples,
            inputs=[video, step_number],
            cache_examples=False
        )
        
        analyze_btn.click(
            fn=inference,
            inputs=[video, step_number],
            outputs=[output]
        )
    
    return demo

if __name__ == "__main__":
    model, tokenizer = load_model()
    demo = create_interface()
    demo.queue().launch(share=True)