Update app.py

app.py

@@ -178,7 +178,22 @@ def audio_function2():
 "Did the technician mention the national parts and labor warranty?"
 "Did the technician mention the 7-day price promise during the video?"
 "Did the technician thank the customer for choosing Parkway Volkswagen?"
-"Did the technician provide a clear NANO statement at the end of the video?" """.format(str2) 
+"Did the technician provide a clear NANO statement at the end of the video?"
+"Does the video show the vehicle staged on a raised ramp?"
+"Does the video show the area around the vehicle clean and organized?"
+"Does the video show the vehicle’s bonnet open and upright?"
+"Does the technician wear gloves during the video?"
+"Does the video show protective items (e.g., seat covers, mats) being used on the vehicle?"
+"Does the video show suitable props like a pointer or tire depth gauge being used?"
+"Does the video show the technician starting at the nearest point of reference on the vehicle?"
+"Does the video demonstrate the use of the Augmented Reality (AR) function?"
+"Did the technician verbally explain the condition of at least two items?" / "Does the video show evidence of at least two items (e.g., tires, brakes) being inspected?"
+"Did the technician explain the percentage wear of tire treads or brake pads?" / "Does the video show measurement of tire treads or brake pads?"
+"Does the video show the technician removing a wheel to demonstrate brake condition clearly?"
+"Did the technician provide additional context regarding brake or tire wear?" / "Does the video visually demonstrate brake or tire wear with context?"
+"Did the technician explain the consequences of any identified repair areas?" / "Does the video show repair areas or consequences visually?"
+"Did the technician verbally compare a new part to a worn part?" / "Does the video show a side-by-side comparison of a new part and a worn part?"
+"Does the video include or reference supporting documents (e.g., photographs of identified items)?" """.format(str2) 
     
     # model = model.eval()
     response, history = model.chat(tokenizer, prompt, history=[])