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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I am organizing a fun activity for my film society where we guess movies from images indirectly related to the movie. Can you first provide some key details for 'The Shape of Water' from 2017? Using a combination of its genre and director as a clue, can you then generate an intriguing image for the audience? And finally, could you determine the classification of the generated image to aid in our guessing game?
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 key details about the movie 'The Shape of Water' from 2017. I will search for a function in the codebase that can retrieve information about movies. Once I have the movie details, I will use the genre and director to generate an image. Finally, I will search for a function that can classify images to help in the guessing game.
ACTION TYPE:
search
ACTION CONTENT:
text: "The Shape of Water" AND (type: FUNCTION) AND (text: movie)
text: genre AND director AND (type: FUNCTION) AND (text: movie)
text: generate AND (type: FUNCTION) AND (text: image)
text: classify AND (type: FUNCTION) AND (text: image)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: "The Shape of Water" AND (type: FUNCTION) AND (text: movie)

CODE BLOCKS:
No code blocks found.

QUERY:
text: genre AND director AND (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...'}
    """
    ...
}
---
(All code blocks matching the query were returned.)

QUERY:
text: generate AND (type: FUNCTION) AND (text: image)

CODE BLOCKS:
---
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
}
---
file_path=mnm/tool_api.py
lines=[348, 375]
type=FUNCTION
content={
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']):
    """
    Processes an image and a question about the image, then generates an answer using a pre-trained model.
    
    Args:
        image: The image to be processed.
        question: The question related to the image.
        ckpt: The checkpoint or model identifier for the pre-trained model. Defaults to MODEL_SELECTION["visual_question_answering"].
    
    Returns:
        A dictionary with a key 'text' containing the generated answer to the question.
    
    Examples:
        >>> answer = visual_question_answering(my_image, "What is in this image?")
        >>> print(answer['text'])
        "A cat sitting on a windowsill."
    """
    ...
}
---
(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 automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # 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 image_crop(image, object, ckpt=None): ... # 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

QUERY:
text: classify AND (type: FUNCTION) AND (text: image)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[325, 346]
type=FUNCTION
content={
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']):
    """
    Classifies an image into one of the predefined categories using a Vision Transformer (ViT) model.
    
    Args:
        image: The image to be classified, which will be processed by `image_processing` function.
        ckpt (str, optional): The checkpoint or model identifier to use for loading the pre-trained model. Defaults to `MODEL_SELECTION["image_classification"]`.
    
    Returns:
        dict: A dictionary with a key "text" that maps to the predicted category of the image.
    
    Examples:
        >>> result = image_classification(my_image)
        >>> print(result)
        {'text': 'cat'}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[731, 765]
type=FUNCTION
content={
def tag(image, objects, ckpt=None):
    """
    Tags objects in an image with bounding boxes and class labels.
    
    Args:
        image: The image to be processed and tagged.
        objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object.
        ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation.
    
    Returns:
        A dictionary with a key 'image' containing the tagged image.
    
    Examples:
        # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label'
        result = tag(image, objects)
        tagged_image = result['image']
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[377, 411]
type=FUNCTION
content={
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']):
    """
    Performs object detection on an image using a DETR (DEtection TRansformer) model.
    
    Args:
        image: The image on which to perform object detection.
        ckpt: A string representing the model checkpoint to use for object detection. Defaults to a value from MODEL_SELECTION["object_detection"].
    
    Returns:
        A dictionary with two keys:
        - 'image': The processed image.
        - 'objects': A list of dictionaries, each containing a 'bbox' key with the bounding box coordinates and a 'label' key with the object's label.
    
    Raises:
        ImportError: If the required modules 'torch' or 'transformers' are not installed.
    
    Examples:
        >>> result = object_detection(my_image)
        >>> print(result['objects'])
        [{'bbox': [x1, y1, x2, y2], 'label': 'object_label'}, ...]
    """
    ...
}
---
(14 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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
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_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search results provided the necessary functions to complete the task. I will use the `search_movie` function to retrieve details about 'The Shape of Water'. Then, I will use the `image_generation` function to generate an image based on the genre and director of the movie. Finally, I will use the `image_classification` function to classify the generated image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import search_movie, image_generation, image_classification

# Retrieve movie details
movie_details = search_movie("The Shape of Water", "2017")

# Generate an image based on the genre and director
genre_director_description = f"{movie_details['text'].split('Genre: ')[1].split('\\n')[0]} by {movie_details['text'].split('Director: ')[1].split('\\n')[0]}"
generated_image = image_generation(genre_director_description)

# Classify the generated image
image_classification_result = image_classification(generated_image['image'])

-----Response-----
RESPONSE (Execution Result):
EXECUTION ERROR:
Traceback (most recent call last):
  File "/root/codenav/codenav/environments/code_env.py", line 88, in exec_with_informative_errors
    exec(code_str, global_vars)
  File "<string>", line 7
    genre_director_description = f"{movie_details['text'].split('Genre: ')[1].split('\\n')[0]} by {movie_details['text'].split('Director: ')[1].split('\\n')[0]}"
                                                                                                                                                                 ^
SyntaxError: f-string expression part cannot include a backslash

STATIC ANALYSIS ERRORS:
TypeCheckingError(code_ref="genre_director_description = f"{movie_details['text'].split('Genre: ')[1].split('\\n')[0]} by {movie_details['text'].split('Director: ')[1].split('\\n')[0]}"", line_num=7, error="f-string expression part cannot include a backslash  [syntax]")

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

------Action------
THOUGHT:
The execution error indicates a syntax error in the f-string due to the inclusion of a backslash. I will correct the f-string by removing the backslashes and using the newline character '\n' directly in the split method. I will also ensure that the movie details are accessed correctly from the returned dictionary.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import search_movie, image_generation, image_classification

# Retrieve movie details
movie_details = search_movie("The Shape of Water", "2017")

# Extract genre and director from the movie details
movie_info = movie_details['text']
genre = movie_info.split('Genre: ')[1].split('\n')[0]
director = movie_info.split('Director: ')[1].split('\n')[0]
genre_director_description = f"{genre} by {director}"

# Generate an image based on the genre and director
generated_image = image_generation(genre_director_description)

# Classify the generated image
image_classification_result = image_classification(generated_image['image'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"search_movie": <function search_movie at 0x7f3115130160>, "image_generation": <function image_generation at 0x7f31151132e0>, "image_classification": <function image_classification at 0x7f3115113490>, "movie_details": {'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'}, "movie_info": Title: The Shape of Water
Year: 2017
Genre: Drama, Fantasy, Romance
Director: Guillermo del Toro
Plot: 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.
, "genre": Drama, Fantasy, Romance, "director": Guillermo del Toro, "genre_director_description": Drama, Fantasy, Romance by Guillermo del Toro, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F31DB2EDBA0>}, "image_classification_result": {'text': 'cuirass'}}

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

------Action------
THOUGHT:
The code executed without errors, and I have retrieved the movie details, generated an image based on the genre and director, and classified the generated image. The classification result is 'cuirass'. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import search_movie, image_generation, image_classification
output0 = search_movie("The Shape of Water", "2017")
output1 = image_generation("Drama, Fantasy, Romance by Guillermo del Toro")
output2 = image_classification(output1['image'])

-----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