******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I'm doing research on the movie "Green Book" from the year 2018 but I need an easily digestible summary of its storyline and main themes, as well as a visual representation that would help me grasp its overall ambiance and mood. Any files mentioned in the query will be located at /root/input_data. E.g. if the query mentions example.png, it will be located at /root/input_data/example.png. Use the code base located at `/root/mnm/mnm` to solve this query. Your current directory is `/root/playground`. If the import path in retrieved code block says `testing/dir_name/file_name.py` and you want to import variable, function or class called `obj` from this file, then import using `from testing.dir_name.file_name import obj`. The code base path has either been installed via pip or has been already been added to the system path via ``` import sys sys.path.extend(['/root/mnm']) ``` ******************************************************************************** Step 2 ******************************************************************************** ------Action------ THOUGHT: First, I need to find a function that can retrieve a summary of the movie "Green Book" from the year 2018. I will search for a function related to retrieving information about movies. After obtaining the summary, I will look for a function that can generate a visual representation based on text, which could be used to grasp the overall ambiance and mood of the movie. ACTION TYPE: search ACTION CONTENT: text: movie AND (text: summary OR text: information) text: visual representation OR text: generate image -----Response----- RESPONSE (Retrieval Result): QUERY: text: movie AND (text: summary OR text: information) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[943, 971] type=FUNCTION content={ def search_movie(movie_title: str, movie_year: str, ckpt=None): """ Retrieve basic movie information, including title, year, genre, and director. """ # paren_start = input['movie_title'].find('(') url = "http://www.omdbapi.com/" params = { "t": movie_title, "y": movie_year, "plot": "short", "r": "json", "apikey": OMDB_API_KEY, } response = requests.get(url, params=params) result_formatted = response.json() selected_keys = ["Title", "Year", "Genre", "Director", "Plot"] desc = "" if len(result_formatted) > 0: for k, v in result_formatted.items(): # print(k, v) if k in selected_keys and len(v) > 0: desc += f"{k}: {v}\n" else: desc = "Movie not found!" result_formatted = {"text": desc} return result_formatted } --- file_path=mnm/tool_api.py lines=[973, 983] type=FUNCTION content={ def get_weather(lon: str, lat: str, ckpt=None): """ Provides weather forecast data based on specific geographical coordinates. """ url = "http://www.7timer.info/bin/api.pl" params = {"lon": lon, "lat": lat, "product": "civil", "output": "json"} response = requests.get(url, params=params) result_formatted = {"objects": response.json()["dataseries"]} return result_formatted } --- file_path=mnm/tool_api.py lines=[111, 141] type=FUNCTION content={ def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): """ Generates a continuation of the given input text using a specified language model. Args: text (str): The input text prompt to be continued. ckpt (str, optional): The identifier for the language model to be used. Defaults to the model specified in MODEL_SELECTION["text_generation"]. Returns: dict: A dictionary containing the generated text under the key 'text'. Raises: KeyError: If the OPENAI_API_KEY environment variable is not set. APIError: If the OpenAI API encounters an issue with the request. Examples: >>> generated_text = text_generation("Once upon a time") >>> print(generated_text) {'text': '...continued text from the model...'} """ ... } --- (9 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 8 classes/functions found in unexpanded results (search again with the same query to see details): def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py QUERY: text: visual representation OR text: generate image CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[348, 375] type=FUNCTION content={ def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): """ Processes an image and a question about the image, then generates an answer using a pre-trained model. Args: image: The image to be processed. question: The question related to the image. ckpt: The checkpoint or model identifier for the pre-trained model. Defaults to MODEL_SELECTION["visual_question_answering"]. Returns: A dictionary with a key 'text' containing the generated answer to the question. Examples: >>> answer = visual_question_answering(my_image, "What is in this image?") >>> print(answer['text']) "A cat sitting on a windowsill." """ ... } --- file_path=mnm/tool_api.py lines=[228, 249] type=FUNCTION content={ def image_generation(text: str, ckpt=MODEL_SELECTION["image_generation"]): """ It takes a text prompt and generates an image that matches the text description. """ if ckpt == "stabilityai/stable-diffusion-xl-base-1.0": text = text_processing(text) from diffusers import DiffusionPipeline generator = DiffusionPipeline.from_pretrained( pretrained_model_name_or_path=ckpt, torch_dtype=torch.float16, use_safetensors=True, variant="fp16", ).to(DEVICE) result = generator(prompt=text).images[0] result_formatted = {"image": result} return result_formatted } --- file_path=mnm/tool_api.py lines=[251, 267] type=FUNCTION content={ def image_captioning( image, ckpt=MODEL_SELECTION["image_captioning"] ): # alternative: nlpconnect/vit-gpt2-image-captioning (testing, blip is better than vit-gpt2)z """ It takes an image and generates a text caption of the image. """ image = image_processing(image) pipe = pipeline("image-to-text", model=ckpt) result = pipe( image ) # [{'generated_text': 'there is a small white dog sitting next to a cell phone'}] result_formatted = {"text": result[0]["generated_text"]} return result_formatted } --- (37 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 33 classes/functions found in unexpanded results (search again with the same query to see details): def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `search_movie` from `tool_api.py` can be used to retrieve basic movie information including the plot, which will serve as a summary of the storyline and main themes for "Green Book". For the visual representation, the function `image_generation` from `tool_api.py` can generate an image based on a text description. I will use the plot summary obtained from `search_movie` as input for `image_generation` to create a visual representation of the movie's ambiance and mood. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import search_movie, image_generation # Retrieve the summary of the movie "Green Book" movie_summary = search_movie(movie_title="Green Book", movie_year="2018") # Generate a visual representation based on the movie's summary visual_representation = image_generation(text=movie_summary['text']) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"search_movie": , "image_generation": , "movie_summary": {'text': 'Title: Green Book\nYear: 2018\nGenre: Biography, Comedy, Drama\nDirector: Peter Farrelly\nPlot: A working-class Italian-American bouncer becomes the driver for an African-American classical pianist on a tour of venues through the 1960s American South.\n'}, "visual_representation": {'image': }} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The movie summary and visual representation have been successfully retrieved and stored in the variables `movie_summary` and `visual_representation`, respectively. The summary includes the title, year, genre, director, and plot of "Green Book". The visual representation is an image generated based on the plot summary. I will now provide a code summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import search_movie, image_generation movie_summary = search_movie(movie_title="Green Book", movie_year="2018") visual_representation = image_generation(text=movie_summary['text']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully. The user has received and stored the summary and visual representation for the movie "Green Book". ACTION TYPE: done ACTION CONTENT: True -----Response----- None