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","Lack of raw material"],
    "Step 4": ["Delay in Bead set","Lack of raw material"],
    "Step 5": ["Delay in Turnup","Lack of raw material"],
    "Step 6": ["Person repairing sidewall","Lack of raw material"],
    "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 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:
Delay in Bead Insertion

Standard Time: 4 seconds
Analysis: Observe the bead placement process. If the insertion exceeds 4 seconds, identify potential issues such as missing beads, technician errors, or machinery malfunction.
Inner Layer Adjustment by Technician

Standard Time: 4 seconds
Analysis: Check for manual intervention during the inner layer application. If adjustment is required, it may indicate improper alignment or issues with the layer material.
Manual Adjustment in Ply1 Apply

Standard Time: 4 seconds
Analysis: Determine if the technician is manually adjusting the first ply. Manual intervention might suggest improper ply placement or machine misalignment.
Delay in Bead Set Step

Standard Time: 8 seconds
Analysis: Observe the bead setting process. Delays may result from bead misalignment, machine pauses, or lack of technician involvement.
Delay in Turnup Step

Standard Time: 4 seconds
Analysis: Examine the turnup step for any technician involvement or pauses in machine operation. Reasons for delays might include material misalignment or equipment issues.
Technician Repairing Sidewall

Standard Time: 14 seconds
Analysis: If a technician is repairing the sidewall, this may indicate material damage or improper initial application. Look for signs of excessive manual handling.
Delay in Sidewall Stitching

Standard Time: 5 seconds
Analysis: Observe the stitching process. Delays could occur due to machine speed inconsistencies or technician intervention for correction.
Technician Availability During Carcass Unload

Standard Time: 7 seconds
Analysis: Ensure a technician is present for the carcass unload. If absent, note their return time and identify potential reasons for their absence.

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."""


# Load model globally
model, tokenizer = load_model()

def inference(video, step_number):
    """Analyzes video to predict the most likely cause of delay in the selected manufacturing step."""
    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
        response = predict(prompt, video, temperature, model, tokenizer)
        
        return response
    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.mp4", "Step 2"],
            ["7838_step6_2.mp4", "Step 6"],
            ["7838_step8_1.mp4", "Step 8"],
            ["7993_step6_3.mp4", "Step 6"],
            ["7993_step8_3.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__":
    demo = create_interface()
    demo.launch(share=True)