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VishalD1234
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Update app.py
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app.py
CHANGED
@@ -111,22 +111,37 @@ Task Context:
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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:
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{', '.join(possible_reasons)}
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Required Analysis:
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Carefully observe the video for visual cues indicating production interruption.Analyse frames and contours around the objects: 'h-stock_left','h-stock_right','conveyor1','conveyor2','compressor_metal','person','orange_roller_metal_left','orange_roller_metal_right','white_down_roller_left','white_down_roller_right','vaccum_blue'.
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If no person is visible in any of the frames, the reason probably might be due to his absence.
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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 repatching it.
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Compare observed evidence against each possible delay reason.
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Select the most likely reason based on visual evidence.
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Please provide your analysis in the following format:
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1. Selected Reason: [State the most likely reason from the given options]
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2. Visual Evidence: [Describe specific visual cues that support your selection]
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3. Reasoning: [Explain why this reason best matches the observed evidence]
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4. Alternative Analysis: [Brief explanation of why other possible reasons are less likely]
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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."""
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# Load model globally
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model, tokenizer = load_model()
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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:
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{', '.join(possible_reasons)}
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Required Analysis:
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Analyze the delay in movement of the following objects from the provided manufacturing video:
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1. **h_stock_left**: Identified by contours with the color **Green**.
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2. **h_stock_right**: Identified by contours with the color **Pink**.
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3. **compressor_metal**: Identified by contours with the color **Orange**.
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4. **conveyor2**: Identified by contours with the color **Blue**.
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5. **white_down_roller_left**: Identified by contours with the color **White**.
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6. **conveyor1**: Identified by contours with the color **Brown**.
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Steps for analysis:
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- Extract the contours for each specified color.
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- Track the movement of each object across frames in the video.
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- Identify and measure any delays or inconsistencies in their expected movement patterns.
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Note:
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- Ensure precise detection of contours based on the given color definitions.
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- Account for occlusions, overlaps, and changes in visibility.
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- Include a summary report of detected delays for each object.
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Carefully observe the video for visual cues indicating production interruption.Analyse frames and contours around the objects: 'h-stock_left','h-stock_right','conveyor1','conveyor2','compressor_metal','person','orange_roller_metal_left','orange_roller_metal_right','white_down_roller_left','white_down_roller_right','vaccum_blue'.
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If no person is visible in any of the frames, the reason probably might be due to his absence.
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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 repatching it.
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Compare observed evidence against each possible delay reason.
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Select the most likely reason based on visual evidence.
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Please provide your analysis in the following format:
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1. Selected Reason: [State the most likely reason from the given options]
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2. Visual Evidence: [Describe specific visual cues that support your selection]
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3. Reasoning: [Explain why this reason best matches the observed evidence]
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4. Alternative Analysis: [Brief explanation of why other possible reasons are less likely]
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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."""
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# Load model globally
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model, tokenizer = load_model()
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