Update app.py

app.py

@@ -166,40 +166,46 @@ def predict(prompt, video_data, temperature, model, tokenizer):
     
     return response
 
-def get_analysis_prompt(step_number):
+def get_analysis_prompt(step_number, possible_reasons):
     """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"]
-    video_substeps = step_info["Video_substeps_expected"]
+    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)}
+
+{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."""
 
-    return f"""
-You are an AI expert system specializing in manufacturing process analysis. 
-Analyze the video for Step {step_number}: {step_name}, with a standard time of {standard_time}.
-Observe for deviations in the expected substeps: {video_substeps}.
-Provide delay analysis based on visual evidence, such as worker actions, material handling, or equipment delays.
-"""
 
 model, tokenizer = load_model()
 
 def inference(video, step_number):
-    """Analyzes video to predict possible issues based on the manufacturing step."""
+    """Analyzes video to predict the most likely cause of delay in the selected manufacturing step."""
     try:
         if not video:
             return "Please upload a video first."
         
-        prompt = get_analysis_prompt(step_number)
+        possible_reasons = DELAY_REASONS[step_number]
+        prompt = get_analysis_prompt(step_number, possible_reasons)
         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: