Spaces:
Runtime error
Runtime error
File size: 2,734 Bytes
20279d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import gradio as gr
import time
model = AutoModelForCausalLM.from_pretrained(
"MILVLG/imp-v1-3b",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("MILVLG/imp-v1-3b", trust_remote_code=True)
def response(USER_DATA, TOKEN) -> str:
print(USER_DATA)
MESSAGE = USER_DATA["text"]
NUM_FILES = len(USER_DATA["files"])
FILES = USER_DATA["files"]
SYSTEM_PROMPT = f"""
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed testcase related to image uploaded. 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:
'''Description: Provide a brief explanation of the functionality being tested, as inferred from the image.
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.).
Testing Steps: Outline a clear, numbered sequence of actions that a user would take to test the functionality in the image.
Expected Result: Specify the expected outcome if the functionality is working as intended.'''
Ensure that:
Testcases should be related to validation of data, component interactions, navigation, etc.
Each testcase should have it's own Description, Pre-conidtions, Testing Steps, Expected Result.
USER: <image>\n{MESSAGE}
ASSISTANT:
"""
RES = generate_answer(FILES, SYSTEM_PROMPT)
response = f"{RES}."
return response
for i in range(len(response)):
time.sleep(0.025)
yield response[: i + 1]
def generate_answer(IMAGES: list, SYSTEM_PROMPT) -> str:
print(len(IMAGES))
INPUT_IDS = tokenizer(SYSTEM_PROMPT, return_tensors="pt").input_ids
RESULT = ""
for EACH_IMG in IMAGES:
image_path = EACH_IMG["path"]
image = Image.open(image_path)
image_tensor = model.image_preprocess(image)
output_ids = model.generate(
inputs=INPUT_IDS,
max_new_tokens=500,
images=image_tensor,
use_cache=False,
)[0]
CUR_RESULT = tokenizer.decode(
output_ids[INPUT_IDS.shape[1] :], skip_special_tokens=True
).strip()
RESULT = f"{RESULT} /n/n {CUR_RESULT}"
return RESULT
|