Update app.py

app.py

@@ -6,23 +6,76 @@ 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"]
+    "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')
@@ -111,24 +164,14 @@ 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."""
+- 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 means there is an issue with the tire being patched, hence they are repairing it.
+Compare observed evidence against each possible delay reason.
+"""
 
-
-# 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."""
+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."
@@ -136,18 +179,19 @@ def inference(video, step_number):
         possible_reasons = DELAY_REASONS[step_number]
         prompt = get_analysis_prompt(step_number, possible_reasons)
         temperature = 0.8
-        response = predict(prompt, video, temperature, model, tokenizer)
+        video_response = predict(prompt, video, temperature, model, tokenizer)
+        step_analysis = analyze_video_step(step_number, observed_time, issues)
         
-        return response
+        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."""
+    """Creates the Gradio interface for the Manufacturing Delay Analysis System."""
     with gr.Blocks() as demo:
         gr.Markdown("""
         # Manufacturing Delay Analysis System
-        Upload a video of the manufacturing step and select the step number. 
+        Upload a video of the manufacturing step, input observed time, and note specific issues.
         The system will analyze the video and determine the most likely cause of delay.
         """)
         
@@ -158,36 +202,22 @@ def create_interface():
                     choices=list(DELAY_REASONS.keys()),
                     label="Manufacturing Step"
                 )
+                observed_time = gr.Number(label="Observed Time (seconds)")
+                issues = gr.Textbox(label="Specific Issues (optional)")
                 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],
+            inputs=[video, step_number, observed_time, issues],
             outputs=[output]
         )
     
     return demo
 
 if __name__ == "__main__":
+    model, tokenizer = load_model()
     demo = create_interface()
-    demo.queue().launch(share=True)
-
+    demo.queue().launch(share=True)