******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I'm curious about the movie 'The Shape of Water' released in 2017. Could you provide me with an understanding of its details including genre, director, etc.? Once that's done, can we generate a narrative description that talks about the main plot following the pool worker's journey? Following the text, it would also be interesting to see an image that encapsulates the atmosphere and mood of the movie. 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: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (Invalid Action): InvalidAction(reason='Action should always contain thought.\nAction type None is not supported.\n') ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: First, I need to find a function in the `mnm` codebase that can retrieve information about the movie 'The Shape of Water' from 2017. I will search for a function related to retrieving movie information. ACTION TYPE: search ACTION CONTENT: text: movie AND (text: details OR text: information) -----Response----- RESPONSE (Retrieval Result): QUERY: text: movie AND (text: details 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 get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The function `search_movie` from `tool_api.py` seems to be the right function to retrieve information about the movie 'The Shape of Water' from 2017. I will use this function to get the movie details. After that, I will use the `text_generation` function to generate a narrative description of the main plot following the pool worker's journey. Once we have the text, I will look for a function that can generate an image based on the text description to encapsulate the atmosphere and mood of the movie. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import search_movie, text_generation # Retrieve movie information movie_info = search_movie(movie_title='The Shape of Water', movie_year='2017') # Generate a narrative description of the main plot narrative_description = text_generation(text="The main plot follows the journey of a pool worker in the movie 'The Shape of Water'.") -----Response----- RESPONSE (Execution Result): STDOUT: {'text': "The pool worker, Elisa, is a mute woman who works at a top-secret government facility in Baltimore during the Cold War era. One day, a mysterious amphibious creature is brought to the facility for study and Elisa forms a unique bond with the creature, communicating with it through sign language.\n\nAs Elisa learns more about the creature, she becomes determined to rescue it from the cruel experiments being conducted on it. With the help of her friends, including her co-worker Zelda and her neighbor Giles, Elisa hatches a plan to free the creature and help it escape to the ocean.\n\nAlong the way, Elisa faces obstacles from the facility's security chief, Strickland, who is determined to capture the creature for his own purposes. Despite these challenges, Elisa's determination and courage lead to a thrilling and emotional conclusion as she fights to protect the creature she has come to care for deeply.\n\nThe Shape of Water is a captivating and visually stunning film that explores themes of love, acceptance, and the power of connections that transcend language and barriers. It garnered critical acclaim and won numerous awards, including the Academy Award for Best Picture."} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"search_movie": , "text_generation": , "movie_info": {'text': 'Title: The Shape of Water\nYear: 2017\nGenre: Drama, Fantasy, Romance\nDirector: Guillermo del Toro\nPlot: At a top secret research facility in the 1960s, a lonely janitor forms a unique relationship with an amphibious creature that is being held in captivity.\n'}, "narrative_description": {'text': "The pool worker, Elisa, is a mute woman who works at a top-secret government facility in Baltimore during the Cold War era. One day, a mysterious amphibious creature is brought to the facility for study and Elisa forms a unique bond with the creature, communicating with it through sign language.\n\nAs Elisa learns more about the creature, she becomes determined to rescue it from the cruel experiments being conducted on it. With the help of her friends, including her co-worker Zelda and...} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: We have successfully retrieved the movie information and generated a narrative description of the main plot. Now, I need to find a function in the `mnm` codebase that can generate an image based on a text description to encapsulate the atmosphere and mood of the movie 'The Shape of Water'. ACTION TYPE: search ACTION CONTENT: text: generate AND text: image AND text: description -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate AND text: image AND text: description CODE BLOCKS: --- 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=[195, 226] type=FUNCTION content={ def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): """ Performs automatic speech recognition on an audio file using a pre-trained model. Args: audio (str): The file path to the audio file to be transcribed. ckpt (str, optional): The checkpoint identifier of the pre-trained model to use. Defaults to the model specified in MODEL_SELECTION["automatic_speech_recognition"]. Returns: dict: A dictionary containing the transcription of the audio file with the key 'text'. Raises: ImportError: If the required libraries (librosa, transformers) are not installed. Examples: >>> transcription = automatic_speech_recognition("path/to/audio/file.wav") >>> print(transcription) {'text': 'The transcribed text of the audio.'} """ ... } --- 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 } --- (11 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 9 classes/functions found in unexpanded results (search again with the same query to see details): def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def get_location(city: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The function `image_generation` from `tool_api.py` is suitable for generating an image based on a text description. I will use this function to create an image that encapsulates the atmosphere and mood of the movie 'The Shape of Water' using the narrative description we generated earlier. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Generate an image based on the narrative description image_result = image_generation(text=narrative_description['text']) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "image_result": {'image': }} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The image has been successfully generated. We have now completed all parts of the user's query: retrieved movie information, generated a narrative description, and created an image that reflects the mood of the movie 'The Shape of Water'. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import search_movie, text_generation, image_generation movie_info = search_movie(movie_title='The Shape of Water', movie_year='2017') narrative_description = text_generation(text="The main plot follows the journey of a pool worker in the movie 'The Shape of Water'.") image_result = image_generation(text=narrative_description['text']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully. ACTION TYPE: done ACTION CONTENT: True -----Response----- None