******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I just finished watching '12 Years a Slave' from 2013 and I'm curious to dig a little deeper into it. Could you help me find more details about this film like the genre and who directed it? Then, could you compose a brief summary based on this information? To top it off, I want to see an intriguing image representation inspired by the summary you provided. 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 in the `mnm` codebase that can retrieve factual information about movies. This function will likely be part of the information retrieval functions mentioned in the codebase description. I will search for a function that can provide details such as genre and director for a given movie title. Once I have the movie details, I will look for a function that can generate a text summary based on the information retrieved. Finally, I will search for an image editing function that can generate an image based on a text description, which will be the summary of the movie details. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: movie) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: movie) 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=[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...'} """ ... } --- file_path=mnm/tool_api.py lines=[491, 534] type=FUNCTION content={ def image_crop(image, object, ckpt=None): """ Crops an image to the region within a specified bounding box. Args: image: The image to be cropped. object: A dictionary containing the bounding box coordinates with the key 'bbox'. ckpt: Optional; a checkpoint or state to use for additional processing (not used in the current implementation). Returns: A dictionary with the key 'image' containing the cropped image. Raises: ValueError: If the bounding box is a string and cannot be converted to a list of coordinates. Examples: # Assuming `img` is an image object and `obj` is a dict with bbox coordinates cropped_image_result = image_crop(img, obj) cropped_image = cropped_image_result['image'] """ ... } --- (8 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_weather(lon: str, lat: 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 get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `search_movie` function from the `mnm/tool_api.py` file seems to be the right choice for retrieving movie information such as the title, year, genre, and director. I will use this function to get the details for the movie '12 Years a Slave' from 2013. After obtaining the movie details, I will use the `text_generation` function to compose a brief summary based on the information retrieved. Once I have the summary, I will need to find a function that can generate an image based on a text description. The `image_crop` function is not suitable for this task as it requires a bounding box and crops an image rather than generating one. I will search again to find a function that can generate an image from a text description. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: generate image description) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: generate image 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 } --- (31 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 31 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 get_full_path_data(filename): ... # 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The `image_generation` function from the `mnm/tool_api.py` file is suitable for generating an image based on a text description. I will use this function to create an image representation inspired by the summary of the movie details. Now that I have identified the necessary functions (`search_movie`, `text_generation`, and `image_generation`), I can proceed to write the code to retrieve the movie details, generate a summary, and then create an image based on that summary. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import search_movie, text_generation, image_generation # Retrieve movie details movie_details = search_movie(movie_title='12 Years a Slave', movie_year='2013') # Generate a summary based on the movie details summary = text_generation(text=movie_details['text']) # Generate an image based on the summary image_representation = image_generation(text=summary['text']) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': '\nAs he struggles to survive and maintain his dignity in the face of brutal treatment, Solomon becomes entangled in the lives of those around him, both fellow slaves and their masters. Over the course of 12 years, Solomon faces unimaginable challenges and hardships, but he never loses hope of one day regaining his freedom.\n\nThrough his harrowing journey, Solomon bears witness to the horrors of slavery and the humanity that endures even in the darkest of times. With powerful performances from a talented cast, including Chiwetel Ejiofor, Michael Fassbender, and Lupita Nyong\'o, "12 Years a Slave" is a poignant and unforgettable exploration of one man\'s resilience in the face of unfathomable cruelty.\n\nAs Solomon\'s story unfolds, viewers are forced to confront the painful legacy of slavery in America and the enduring legacy of those who fought against it. "12 Years a Slave" is a gripping and emotional reminder of the atrocities of the past and the strength of the human spirit in the face of injustice.'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"search_movie": , "text_generation": , "image_generation": , "movie_details": {'text': 'Title: 12 Years a Slave\nYear: 2013\nGenre: Biography, Drama, History\nDirector: Steve McQueen\nPlot: In the antebellum United States, Solomon Northup, a free Black man from upstate New York, is abducted and sold into slavery.\n'}, "summary": {'text': '\nAs he struggles to survive and maintain his dignity in the face of brutal treatment, Solomon becomes entangled in the lives of those around him, both fellow slaves and their masters. Over the course of 12 years, Solomon faces unimaginable challenges and hardships, but he never loses hope of one day regaining his freedom.\n\nThrough his harrowing journey, Solomon bears witness to the horrors of slavery and the humanity that endures even in the darkest of times. With powerful performan..., "image_representation": {'image': }} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The code execution was successful. The `search_movie` function retrieved the movie details, the `text_generation` function generated a summary based on those details, and the `image_generation` function created an image representation inspired by the summary. The output shows the generated summary text, and the `image_representation` variable contains the generated image. I will now provide a code summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import search_movie, text_generation, image_generation output0 = search_movie(movie_title='12 Years a Slave', movie_year='2013') output1 = text_generation(text=output0['text']) output2 = image_generation(text=output1['text']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully, and the user has acknowledged receipt of the summary. ACTION TYPE: done ACTION CONTENT: True -----Response----- None