********************************************************************************
Step 1
********************************************************************************

------Action------
THOUGHT:
None
ACTION TYPE:
None
ACTION CONTENT:
None

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'm working on a historical fiction novel set around July 20. Could you find some amazing real-life occurrences that happened on this date and then generate a vivid, elaborate narrative based on those facts? Afterwards, could you create a concise summary of the narrative that I could use as an outline?"
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 retrieve factual information about real-life occurrences that happened on July 20. I will search for a function in the `mnm` codebase that can retrieve historical events for a given date. Once I have the facts, I can then look for a function that can generate a narrative based on those facts. Finally, I will need to find a function that can create a summary of the narrative.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: retrieve) AND (text: historical OR text: facts OR text: events)
(type: FUNCTION) AND (text: generate) AND (text: narrative OR text: story)
(type: FUNCTION) AND (text: summarize)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: retrieve) AND (text: historical OR text: facts OR text: events)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[985, 1011]
type=FUNCTION
content={
def wikipedia_simple_search(text: str, ckpt=None):
    """
    Performs a basic search on Wikipedia to retrieve a summary of the most relevant page.
    
    Args:
        text (str): The search query text.
        ckpt: Unused parameter, can be anything (default is None).
    
    Returns:
        dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page.
    
    Raises:
        This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable.
    
    Examples:
        result = wikipedia_simple_search("Python programming language")
        # result might be something like:
        # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'}
    """
    ...
}
---
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=[413, 461]
type=FUNCTION
content={
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']):
    """
    Segments an image into different parts using a pre-trained MaskFormer model and outputs segmentation masks along with additional information for each segment.
    
    Args:
        image: The input image to be segmented.
        ckpt: A string or a dictionary key that specifies the pre-trained model checkpoint to be used for segmentation. Defaults to a pre-selected model checkpoint from a MODEL_SELECTION dictionary.
    
    Returns:
        A dictionary containing the original image and a list of objects. Each object in the list is a dictionary with keys 'mask', 'label', 'bbox', and 'inst_id', representing the segmentation mask, label, bounding box, and instance ID, respectively.
    
    Raises:
        ImportError: If required modules (numpy, torch, transformers, PIL) are not installed.
    
    Examples:
        # Assuming `image` is a PIL image and `MODEL_SELECTION` is defined
        segmentation_result = image_segmentation(image)
        for obj in segmentation_result['objects']:
            print(obj['label'], obj['bbox'])
    """
    ...
}
---
(28 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 28 classes/functions found in unexpanded results (search again with the same query to see details):
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def get_year_fact(year: 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

QUERY:
(type: FUNCTION) AND (text: generate) AND (text: narrative OR text: story)

CODE BLOCKS:
---
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
}
---
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
}
---
(10 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 10 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 text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: summarize)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[143, 163]
type=FUNCTION
content={
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']):
    """
    Summarizes a given text into a few sentences using a pre-trained model.
    
    Args:
        text (str): The text to be summarized.
        ckpt: The checkpoint or model identifier to use for the summarization pipeline. Defaults to a pre-selected model checkpoint.
    
    Returns:
        dict: A dictionary containing the summarized text under the key 'text'.
    
    Examples:
        summary = text_summarization("The quick brown fox jumps over the lazy dog.")
        print(summary)  # Outputs a summarized version of the input text.
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[536, 556]
type=FUNCTION
content={
def image_crop_left(image, ckpt=None):
    """
    Crops the left half of the provided image and returns it in a formatted dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        Assuming `image` is a PIL Image object:
        >>> result = image_crop_left(image)
        >>> result['image']  # This will contain the left half of the original image.
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[581, 602]
type=FUNCTION
content={
def image_crop_top(image, ckpt=None):
    """
    Crops the top half of an image and returns it in a dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        >>> from PIL import Image
        >>> original_image = Image.open('path/to/image.jpg')
        >>> cropped_image_info = image_crop_top(original_image)
        >>> cropped_image = cropped_image_info['image']
    """
    ...
}
---
(10 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 10 classes/functions found in unexpanded results (search again with the same query to see details):
def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py

********************************************************************************
Step 3
********************************************************************************

------Action------
THOUGHT:
The search results have provided the necessary functions to fulfill the user's request. The `get_date_fact` function can retrieve historical events for a given date. The `text_generation` function can generate a narrative based on text input. Lastly, the `text_summarization` function can create a summary of the narrative. I will now write code to use these functions in sequence to complete the task.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_generation, text_summarization

# Retrieve historical events that happened on July 20
date_facts = get_date_fact(date="July 20")

# Generate a narrative based on the historical events
narrative = text_generation(text=date_facts['text'])

# Create a concise summary of the narrative
summary = text_summarization(text=narrative['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': "On May 30, 2003, Burma's ruling military junta placed opposition leader Daw Aung San Suu Kyi under house arrest. This move was widely criticized by the international community, as Suu Kyi had been a vocal critic of the military government and a leading figure in the pro-democracy movement in Burma.\n\nDuring her time under house arrest, Suu Kyi was confined to her residence in Rangoon and was cut off from the outside world. She was allowed occasional visits from family members and was closely monitored by government security forces.\n\nSuu Kyi's house arrest was just one of the many repressive measures taken by the Burmese military government to suppress dissent and maintain its grip on power. Despite the challenges she faced, Suu Kyi continued to be a symbol of resistance and hope for the Burmese people.\n\nShe was released from house arrest in 2010, but her political career continued to be marred by various restrictions and challenges imposed by the government. Nevertheless, Suu Kyi remained a prominent figure in Burmese politics and a leading voice for democracy and human rights in the country."}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_date_fact": <function get_date_fact at 0x7f40cf4e7d00>, "text_generation": <function text_generation at 0x7f40cf4e6f80>, "text_summarization": <function text_summarization at 0x7f40cf4e7010>, "date_facts": {'text': "Burma's ruling junta puts opposition leader Daw Aung San Suu Kyi under house arrest", 'year': 1989, 'number': 202, 'found': True, 'type': 'date'}, "narrative": {'text': "On May 30, 2003, Burma's ruling military junta placed opposition leader Daw Aung San Suu Kyi under house arrest. This move was widely criticized by the international community, as Suu Kyi had been a vocal critic of the military government and a leading figure in the pro-democracy movement in Burma.\n\nDuring her time under house arrest, Suu Kyi was confined to her residence in Rangoon and was cut off from the outside world. She was allowed occasional visits from family members and was ..., "summary": {'text': 'Daw Aung San Suu Kyi was a leading figure in the pro-democracy movement in Burma. She was placed under house arrest by the military government in 2003. She has been released from house arrest in 2010.'}}

********************************************************************************
Step 4
********************************************************************************

------Action------
THOUGHT:
The code execution was successful, and we have obtained a narrative and a summary based on the historical event of Daw Aung San Suu Kyi's house arrest. I will now provide a code summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_generation, text_summarization
output0 = get_date_fact(date="July 20")
output1 = text_generation(text=output0['text'])
output2 = text_summarization(text=output1['text'])

-----Response-----
RESPONSE (User Message):
Summary received and stored.

********************************************************************************
Step 5
********************************************************************************

------Action------
THOUGHT:
The user has acknowledged receipt of the summary. The task is complete.
ACTION TYPE:
done
ACTION CONTENT:
True

-----Response-----
None