Update app.py

app.py

@@ -160,15 +160,23 @@ def predict(prompt, video_data, temperature, model, tokenizer):
 def get_analysis_prompt(step_number, possible_reasons):
     """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.
-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 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.
-"""
+    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."""
+
+model, tokenizer = load_model()
 
 def inference(video, step_number, observed_time, issues=""):
     """Analyzes video and additional step data for delay analysis."""
@@ -187,11 +195,11 @@ def inference(video, step_number, observed_time, issues=""):
         return f"An error occurred during analysis: {str(e)}"
 
 def create_interface():
-    """Creates the Gradio interface for the Manufacturing Delay Analysis System."""
+    """Creates the Gradio interface for the Manufacturing Delay Analysis System with examples."""
     with gr.Blocks() as demo:
         gr.Markdown("""
         # Manufacturing Delay Analysis System
-        Upload a video of the manufacturing step, input observed time, and note specific issues.
+        Upload a video of the manufacturing step and select the step number. 
         The system will analyze the video and determine the most likely cause of delay.
         """)
         
@@ -202,16 +210,30 @@ 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, observed_time, issues],
+            inputs=[video, step_number],
             outputs=[output]
         )