Create app2.py

app2.py

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+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()