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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I'm about to start an essay on the 2012 movie 'Argo' but can't recall all the details. Can you bring up the key information, condense the plotline into a crisp summary, and create a representative image that illustrates the film's essence?
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 the 2012 movie 'Argo'. I will search for a function in the codebase that can retrieve information about movies. Once I have the information, I will look for a function that can summarize text to condense the plotline. Lastly, I will search for a function that can generate an image based on a text description to create a representative image illustrating the film's essence.
ACTION TYPE:
search
ACTION CONTENT:
text: movie AND (text: information OR text: details)
text: summarize
text: generate image AND text: description

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: movie AND (text: information OR text: details)

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

QUERY:
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=[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=[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'])
    """
    ...
}
---
(16 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 14 classes/functions found in unexpanded results (search again with the same query to see details):
def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py
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 image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py

QUERY:
text: generate 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
}
---
(14 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 12 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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search results have provided us with the necessary functions to complete the user's query. We can use `search_movie` to retrieve information about the movie 'Argo', `text_summarization` to condense the plotline into a summary, and `image_generation` to create a representative image based on a text description. I will start by using `search_movie` to get the key information about the movie 'Argo'.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import search_movie

# Retrieve information about the movie 'Argo'
movie_info = search_movie(movie_title="Argo", movie_year="2012")
print(movie_info)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Title: Argo\nYear: 2012\nGenre: Biography, Drama, Thriller\nDirector: Ben Affleck\nPlot: Acting under the cover of a Hollywood producer scouting a location for a science fiction film, a CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979.\n'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"search_movie": <function search_movie at 0x7f5e70738310>, "movie_info": {'text': 'Title: Argo\nYear: 2012\nGenre: Biography, Drama, Thriller\nDirector: Ben Affleck\nPlot: Acting under the cover of a Hollywood producer scouting a location for a science fiction film, a CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979.\n'}}

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

------Action------
THOUGHT:
We have successfully retrieved the movie information. Now, I will use the `text_summarization` function to condense the plotline of 'Argo' into a crisp summary.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_summarization

# Extract the plot from the movie information
plot = movie_info['text'].split('Plot: ')[1].strip()

# Summarize the plot of the movie 'Argo'
plot_summary = text_summarization(text=plot)
print(plot_summary)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'A CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979. Acting under the cover of a Hollywood producer scouting a location for a science fiction film,'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_summarization": <function text_summarization at 0x7f5e70723250>, "plot": Acting under the cover of a Hollywood producer scouting a location for a science fiction film, a CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979., "plot_summary": {'text': 'A CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979. Acting under the cover of a Hollywood producer scouting a location for a science fiction film,'}}

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

------Action------
THOUGHT:
The plot summary has been successfully generated. Next, I will use the `image_generation` function to create a representative image that illustrates the essence of the film 'Argo' based on the summary.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Use the summary of the plot as a description to generate an image
description = plot_summary['text']
generated_image = image_generation(text=description)
print(generated_image)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F58DB7F8340>}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f5e70723490>, "description": A CIA agent launches a dangerous operation to rescue six Americans in Tehran during the U.S. hostage crisis in Iran in 1979. Acting under the cover of a Hollywood producer scouting a location for a science fiction film,, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F58DB7F8340>}}

********************************************************************************
Step 6
********************************************************************************

------Action------
THOUGHT:
The representative image for the film 'Argo' has been successfully generated. The task is now complete, and I will provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import search_movie, text_summarization, image_generation
output0 = search_movie(movie_title="Argo", movie_year="2012")
output1 = text_summarization(text=output0['text'].split('Plot: ')[1].strip())
output2 = image_generation(text=output1['text'])

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

********************************************************************************
Step 7
********************************************************************************

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

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