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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from PIL import Image |
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import gradio as gr |
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import time |
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model = AutoModelForCausalLM.from_pretrained( |
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"MILVLG/imp-v1-3b", |
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torch_dtype=torch.float16, |
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device_map="auto", |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained("MILVLG/imp-v1-3b", trust_remote_code=True) |
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def response(USER_DATA, TOKEN) -> str: |
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print(USER_DATA) |
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MESSAGE = USER_DATA["text"] |
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NUM_FILES = len(USER_DATA["files"]) |
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FILES = USER_DATA["files"] |
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SYSTEM_PROMPT = f""" |
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A chat between a curious user and an artificial intelligence assistant. The assistant generates helpful, and detailed testcases for software/website testing. |
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You are tasked with generating detailed, step-by-step test cases for software functionality based on uploaded images. The user will provide one or more images of a software or website interface. For each image, generate a separate set of test cases following the format below: |
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Description: Provide a brief explanation of the functionality being tested, as inferred from the image. |
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Pre-conditions: Identify any setup requirements, dependencies, or conditions that must be met before testing can begin (e.g., user logged in, specific data pre-populated, etc.). |
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Testing Steps: Outline a clear, numbered sequence of actions that a user would take to test the functionality in the image. |
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Expected Result: Specify the expected outcome if the functionality is working as intended. |
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Ensure that: |
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Test cases are created independently for each image. |
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The functionality from each image is fully covered in its own set of test cases. |
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Any assumptions you make are clearly stated. |
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The focus is on usability, navigation, and feature correctness as demonstrated in the UI of the uploaded images. |
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USER: <image>\n{MESSAGE} |
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ASSISTANT: |
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""" |
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RES = generate_answer(FILES, SYSTEM_PROMPT) |
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response = f"{RES}." |
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return response |
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def generate_answer(IMAGES: list, SYSTEM_PROMPT) -> str: |
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print(len(IMAGES)) |
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INPUT_IDS = tokenizer(SYSTEM_PROMPT, return_tensors="pt").input_ids |
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RESULT = "" |
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for EACH_IMG in IMAGES: |
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image_path = EACH_IMG["path"] |
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image = Image.open(image_path) |
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image_tensor = model.image_preprocess(image) |
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output_ids = model.generate( |
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inputs=INPUT_IDS, |
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max_new_tokens=500, |
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images=image_tensor, |
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use_cache=False, |
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)[0] |
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CUR_RESULT = tokenizer.decode( |
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output_ids[INPUT_IDS.shape[1] :], skip_special_tokens=True |
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).strip() |
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RESULT = f"{RESULT} /n/n {CUR_RESULT}" |
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return RESULT |
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