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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I'm curious about the movie 'Green Book' which was released in 2018. Can you provide me with an overview of the film? Once you've done that, maybe you could also give me a concise summary that I could read in under a minute. One thing I've been wondering is who the director of 'Green Book' is. Can you answer that for me?
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. I will search for a function that can provide an overview of the movie 'Green Book'. Once I have the overview, I will look for a function that can generate a concise summary. Lastly, I will search for a function that can answer specific questions, such as the director of the movie.
ACTION TYPE:
search
ACTION CONTENT:
text: movie AND text: overview
text: summary AND text: movie
text: director AND text: movie

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: movie AND text: overview

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']
    """
    ...
}
---
(All code blocks matching the query were returned.)

QUERY:
text: summary AND text: movie

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[627, 676]
type=FUNCTION
content={
def background_blur(image, object, ckpt=None):
    """
    Applies a background blur effect to an image, keeping specified objects in the foreground sharp.
    
    Args:
        image: A PIL Image object representing the input image.
        object: A dictionary containing the object(s) and their associated mask(s) to keep in focus.
        ckpt: Optional; a checkpoint or model state to use for advanced processing (not used in the current implementation).
    
    Returns:
        A dictionary with a key 'image' containing the resulting PIL Image object with the background blurred.
    
    Raises:
        ImportError: If the required modules (cv2, numpy, PIL) are not available.
    
    Examples:
        # Assuming 'input_image' is a PIL Image and 'foreground_object' is a dictionary with a mask.
        result = background_blur(input_image, foreground_object)
        blurred_image = result['image']
        blurred_image.show()
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[678, 719]
type=FUNCTION
content={
def color_pop(image, object, ckpt=None):
    """
    Applies a "color pop" effect to an image, where the specified object is in color and the rest of the image is converted to black and white.
    
    Args:
        image: The input image to be processed.
        object: A dictionary containing the object and its mask. The mask should be a binary mask where the object is white (1) and the rest is black (0).
        ckpt: Optional; a checkpoint for the model, not used in the current implementation.
    
    Returns:
        A dictionary with the key 'image' containing the PIL Image with the color pop effect applied.
    
    Raises:
        ImportError: If the required modules 'cv2' (OpenCV) or 'numpy' are not installed.
    
    Examples:
        # Assuming 'image' is a PIL Image and 'object_mask' is a binary mask of the object
        object_info = {'mask': object_mask}
        result = color_pop(image, object_info)
        result_image = result['image']
        result_image.show()
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[1013, 1038]
type=CONDITIONAL
content={
if __name__ == '__main__':
    res = text_summarization({'text': 'John Taylor, who had supported her through college, was interested in cotton.'})
    print(res, '\n')
    pass
}
---
(All code blocks matching the query were returned.)

QUERY:
text: director AND text: movie

CODE BLOCKS:
---
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=[24, 49]
type=FUNCTION
content={
def save_output(task_idx, node_idx, output_dict, result_folder):
    new_dict = deepcopy(output_dict)
    full_result_path = os.path.join(result_folder, str(task_idx))
    if not os.path.exists(full_result_path):
        os.makedirs(full_result_path)
    for key, output in output_dict.items():
        # Determine the type of output and set the appropriate file extension
        if isinstance(output, Image.Image):
            file_path = os.path.join(full_result_path, f"node-{node_idx}.jpg")
            output.save(file_path)
            new_dict[key] = file_path

    print("Output dict to be saved:", new_dict)
    output_dict_path_json = os.path.join(full_result_path, f"node-{node_idx}.json")
    output_dict_path_pickle = os.path.join(full_result_path, f"node-{node_idx}.pkl")
    try:
        with open(output_dict_path_json, "w") as file:
            json.dump(new_dict, file)
        return output_dict_path_json
    except TypeError as e:
        if os.path.exists(output_dict_path_json):
            os.remove(output_dict_path_json)
        with open(output_dict_path_pickle, "wb") as file:
            pickle.dump(new_dict, file)
        return output_dict_path_pickle
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The function `search_movie` from the `tool_api.py` file seems to be the right choice for retrieving basic movie information, including title, year, genre, and director. This function will provide an overview of the movie 'Green Book'. For the concise summary, I did not find a direct function for summarizing movie information, but I can use the `text_generation` function to generate a summary based on the overview provided by `search_movie`. The director information can also be obtained from the `search_movie` function. I will now write the code to use these functions to get the desired information.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import search_movie, text_generation

# Retrieve basic movie information including the director
movie_info = search_movie(movie_title='Green Book', movie_year='2018')

# Generate a concise summary of the movie information
summary_prompt = f"Summarize the following movie information in under a minute read: {movie_info['text']}"
movie_summary = text_generation(text=summary_prompt)

# Extract the director's name from the movie information
director = movie_info['text'].split("Director: ")[1].split("\n")[0]

movie_info, movie_summary, director

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': '"Green Book" is a 2018 biography, comedy, and drama film directed by Peter Farrelly. The story follows a working-class Italian-American bouncer who becomes the driver for an African-American classical pianist on a tour through the racially segregated American South in the 1960s. The film highlights their unlikely friendship and the challenges they face while navigating the deep-seated racism of the time.'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"search_movie": <function search_movie at 0x7f9e8f6e3eb0>, "text_generation": <function text_generation at 0x7f9e8f6e2dd0>, "movie_info": {'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'}, "summary_prompt": Summarize the following movie information in under a minute read: Title: Green Book
Year: 2018
Genre: Biography, Comedy, Drama
Director: Peter Farrelly
Plot: 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.
, "movie_summary": {'text': '"Green Book" is a 2018 biography, comedy, and drama film directed by Peter Farrelly. The story follows a working-class Italian-American bouncer who becomes the driver for an African-American classical pianist on a tour through the racially segregated American South in the 1960s. The film highlights their unlikely friendship and the challenges they face while navigating the deep-seated racism of the time.'}, "director": Peter Farrelly}

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

------Action------
THOUGHT:
The code execution was successful. We have obtained the overview of the movie 'Green Book', a concise summary that can be read in under a minute, and the name of the director, Peter Farrelly. 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
movie_info = search_movie(movie_title='Green Book', movie_year='2018')
summary_prompt = f"Summarize the following movie information in under a minute read: {movie_info['text']}"
movie_summary = text_generation(text=summary_prompt)
director = movie_info['text'].split("Director: ")[1].split("\n")[0]

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

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

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

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