Delete llm.py

llm.py

@@ -1,78 +0,0 @@
-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 generates helpful, and detailed testcases for software/website testing.
-    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:
-
-    Test cases are created independently for each image.
-    The functionality from each image is fully covered in its own set of test cases.
-    Any assumptions you make are clearly stated.
-    The focus is on usability, navigation, and feature correctness as demonstrated in the UI of the uploaded images.
-
-    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