Update app.py

app.py

@@ -6,10 +6,12 @@ from decord import cpu, VideoReader, bridge
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers import BitsAndBytesConfig
 
-MODEL_PATH = "THUDM/cogvlm2-video-llama3-chat"
+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
 
+
+
 def get_step_info(step_number):
     """Returns detailed information about a manufacturing step."""
     step_details = {
@@ -20,7 +22,12 @@ def get_step_info(step_number):
                 "0-1 second": "Machine starts bead insertion process.",
                 "1-3 seconds": "Beads are aligned and positioned.",
                 "3-4 seconds": "Final adjustment and confirmation of bead placement."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Delay in bead insertion",
+                "Lack of raw material",
+                "Machine malfunction during bead alignment"
+            ]
         },
         2: {
             "Name": "Inner Liner Apply",
@@ -29,7 +36,12 @@ def get_step_info(step_number):
                 "0-1 second": "Machine applies the first layer of the liner.",
                 "1-3 seconds": "Technician checks alignment and adjusts if needed.",
                 "3-4 seconds": "Final inspection and confirmation."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Technician adjusting inner liner alignment",
+                "Person rebuilding defective tire sections",
+                "Machine alignment issues"
+            ]
         },
         3: {
             "Name": "Ply1 Apply",
@@ -37,7 +49,12 @@ def get_step_info(step_number):
             "Video_substeps_expected": {
                 "0-2 seconds": "First ply is loaded onto the machine.",
                 "2-4 seconds": "Technician inspects and adjusts ply placement."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Manual adjustment of ply placement",
+                "Technician repairing defective ply sections",
+                "Ply loading issues"
+            ]
         },
         4: {
             "Name": "Bead Set",
@@ -46,7 +63,12 @@ def get_step_info(step_number):
                 "0-3 seconds": "Bead is positioned and pre-set.",
                 "3-6 seconds": "Machine secures the bead in place.",
                 "6-8 seconds": "Technician confirms the bead alignment."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Delay in bead positioning",
+                "Lack of raw material",
+                "Machine securing process failure"
+            ]
         },
         5: {
             "Name": "Turnup",
@@ -54,7 +76,12 @@ def get_step_info(step_number):
             "Video_substeps_expected": {
                 "0-2 seconds": "Turnup process begins with machine handling.",
                 "2-4 seconds": "Technician inspects the turnup and makes adjustments if necessary."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Delay in turnup handling",
+                "Lack of raw material",
+                "Technician adjustment delays"
+            ]
         },
         6: {
             "Name": "Sidewall Apply",
@@ -63,7 +90,12 @@ def get_step_info(step_number):
                 "0-5 seconds": "Sidewall material is positioned by the machine.",
                 "5-10 seconds": "Technician checks for alignment and begins application.",
                 "10-14 seconds": "Final adjustments and confirmation of sidewall placement."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Person repairing sidewall",
+                "Person rebuilding defective tire sections",
+                "Sidewall positioning issues"
+            ]
         },
         7: {
             "Name": "Sidewall Stitching",
@@ -72,19 +104,30 @@ def get_step_info(step_number):
                 "0-2 seconds": "Stitching process begins automatically.",
                 "2-4 seconds": "Technician inspects stitching for any irregularities.",
                 "4-5 seconds": "Machine completes stitching process."
-            }
+            },
+            "Potential_Delay_Reasons": [
+                "Delay in stitching process",
+                "Technician repairing stitching irregularities",
+                "Machine stitching malfunction"
+            ]
         },
         8: {
             "Name": "Carcass Unload",
             "Standard Time": "7 seconds",
             "Video_substeps_expected": {
                 "0-3 seconds": "Technician unloads(removes) carcass(tire) from the machine."
-            }
+            },
+            "Potential_Delay_reasons": [
+                "Person not available in time(in 3 sec) to remove carcass.",
+                "Person is doing bead(ring) insertion before carcass unload causing unload to be delayed by more than 3 sec"
+            ]
         }
     }
     
     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')
@@ -166,46 +209,67 @@ def predict(prompt, video_data, temperature, model, tokenizer):
     
     return response
 
-def get_analysis_prompt(step_number, possible_reasons):
+def get_analysis_prompt(step_number):
     """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.
+    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. Your task is to determine the most likely cause of the delay from the following possible reasons:
-{', '.join(possible_reasons)}
+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 reasons: 
+  - {analysis}
+Following are the subactivities needs to happen in this step.
 
 {get_step_info(step_number)}
 
-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."""
+Please provide your output in the following format:
+Output_Examples = {
+     ["Delay in Bead Insertion", "Lack of raw material"],
+     ["Inner Liner Adjustment by Technician", "Person rebuilding defective Tire Sections"],
+     ["Manual Adjustment in Ply1 Apply", "Technician repairing defective Tire Sections"],
+     ["Delay in Bead Set", "Lack of raw material"],
+     ["Delay in Turnup", "Lack of raw material"],
+     ["Person Repairing Sidewall", "Person rebuilding defective Tire Sections"],
+     ["Delay in Sidewall Stitching", "Lack of raw material"],
+     ["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 the most likely cause of delay in the selected manufacturing step."""
+    """Analyzes video to predict possible issues based on the 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)
+        prompt = get_analysis_prompt(step_number)
         temperature = 0.3
         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:
@@ -249,4 +313,4 @@ def create_interface():
 
 if __name__ == "__main__":
     demo = create_interface()
-    demo.queue().launch(share=True)
+    demo.queue().launch(share=True)