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 for Each Manufacturing Step
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 get_step_info(step_number):
    """Returns detailed information about a manufacturing step."""
    step_details = {
        1: {
            "Name": "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."
        },
        2: {
            "Name": "Inner Liner Apply",
            "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."
        },
        3: {
            "Name": "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."
        },
        4: {
            "Name": "Bead Set",
            "Standard Time": "8 seconds",
            "Analysis": "Observe the bead setting process. Delays may result from bead misalignment, machine pauses, or lack of technician involvement."
        },
        5: {
            "Name": "Turnup",
            "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."
        },
        6: {
            "Name": "Sidewall Apply",
            "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."
        },
        7: {
            "Name": "Sidewall Stitching",
            "Standard Time": "5 seconds",
            "Analysis": "Observe the stitching process. Delays could occur due to machine speed inconsistencies or technician intervention for correction."
        },
        8: {
            "Name": "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."
        }
    }
    
    return step_details.get(step_number, {"Error": "Invalid step number. Please provide a valid step number."})


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):
    """Constructs the prompt for analyzing delay reasons based on the selected step."""
    step_info = get_step_info(step_number)
    
    if "Error" in step_info:
        return step_info["Error"]
    
    step_name = step_info["Name"]
    standard_time = step_info["Standard Time"]
    analysis = step_info["Analysis"]

    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. The step is called {step_name}, and its standard time is {standard_time}.
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 their absence.
- If a person is visible in the video and is observed touching and modifying the layers of the tire, it indicates an issue with tire patching, and the person might be repairing it.
- Compare observed evidence against the following possible delay reason: 
  - {analysis}

Please provide your analysis in the following format:
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"]
}
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):
    """Analyzes video to predict possible issues based on the manufacturing step."""
    try:
        if not video:
            return "Please upload a video first."
        
        prompt = get_analysis_prompt(step_number)
        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 Analysis System."""
    with gr.Blocks() as demo:
        gr.Markdown("""
        # Manufacturing Analysis System
        Upload a video of the manufacturing step and select the step number. 
        The system will analyze the video and provide observations.
        """)
        
        with gr.Row():
            with gr.Column():
                video = gr.Video(label="Upload Manufacturing Video", sources=["upload"])
                step_number = gr.Dropdown(
                    choices=[f"Step {i}" for i in range(1, 9)],
                    label="Manufacturing Step"
                )
                analyze_btn = gr.Button("Analyze", variant="primary")
            
            with gr.Column():
                output = gr.Textbox(label="Analysis Result", lines=10)
        
        gr.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"]    
            ],
            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.queue().launch(share=True)