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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I have an image COCO_train2014_000000256855.jpg. Can you help me understand the text presented in the image, find related information on Wikipedia and determine the overall sentiment conveyed?"
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 transcribe the text from the image using the OCR function from the tool_api. Then, I will search for a function that can retrieve related information from Wikipedia based on the transcribed text. Finally, I will look for a function that can determine the sentiment of the text. I will start by searching for the OCR function to transcribe the text from the image.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: transcribe)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: transcribe)

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

Prototypes for the next 7 out of 7 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_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The OCR function is named `optical_character_recognition` in the tool_api. I will use this function to transcribe the text from the image. After obtaining the transcribed text, I will search for a function to retrieve related information from Wikipedia and then a function to determine the sentiment of the text.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import optical_character_recognition

# Transcribe text from the image using OCR
image_path = '/root/input_data/COCO_train2014_000000256855.jpg'
transcribed_text = optical_character_recognition(image_path)
print(transcribed_text)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Qenn'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"optical_character_recognition": <function optical_character_recognition at 0x7f2c0f963370>, "image_path": /root/input_data/COCO_train2014_000000256855.jpg, "transcribed_text": {'text': 'Qenn'}}

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

------Action------
THOUGHT:
The text "Qenn" has been successfully transcribed from the image. Next, I will search for a function that can retrieve related information from Wikipedia based on this transcribed text.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: wikipedia)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: wikipedia)

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=[269, 323]
type=FUNCTION
content={
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']):
    """
    Edits an image based on a given text prompt using a specified model checkpoint.
    
    Args:
        image: The input image to be edited.
        prompt (str): The text prompt describing the desired edits.
        ckpt: The model checkpoint to use for image editing. Defaults to MODEL_SELECTION["image_editing"].
    
    Returns:
        A dictionary with the key 'image' containing the edited image.
    
    Raises:
        ImportError: If the necessary libraries for the chosen model are not installed.
    
    Examples:
        # Using the default checkpoint
        edited_image = image_editing(input_image, "A cat with a party hat.")
        
        # Using a specific checkpoint
        edited_image = image_editing(input_image, "A dog on a skateboard.", ckpt="timbrooks/instruct-pix2pix")
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[165, 176]
type=FUNCTION
content={
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']):
    """
    Classifies a given text into a category based on the model's vocabulary.
    
    Args:
        text (str): The text to be classified.
        ckpt: The model checkpoint to use for classification. Defaults to MODEL_SELECTION["text_classification"].
    
    Returns:
        dict: A dictionary with the key 'text' and the value as the classification label.
    
    Examples:
        >>> text_classification("I love sunny days.")
        {'text': 'POSITIVE'}
    """
    ...
}
---
(6 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 6 out of 6 classes/functions found in unexpanded results (search again with the same query to see details):
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `wikipedia_simple_search` will be used to retrieve related information from Wikipedia based on the transcribed text "Qenn". After obtaining the Wikipedia summary, I will search for a function to determine the sentiment of the text.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import wikipedia_simple_search

# Retrieve related information from Wikipedia based on the transcribed text
wikipedia_summary = wikipedia_simple_search(transcribed_text['text'])
print(wikipedia_summary)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Event Gold Silver Bronze Singles Shaun Ng Qenn \xa0Singapore Alex Liew Kien Liang \xa0Malaysia Zulmazran Zulkifli \xa0Malaysia Doubles \xa0Thailand\xa0(THA) Annop Aromsaranon'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"wikipedia_simple_search": <function wikipedia_simple_search at 0x7f2c0f963eb0>, "wikipedia_summary": {'text': 'Event Gold Silver Bronze Singles Shaun Ng Qenn \xa0Singapore Alex Liew Kien Liang \xa0Malaysia Zulmazran Zulkifli \xa0Malaysia Doubles \xa0Thailand\xa0(THA) Annop Aromsaranon'}}

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

------Action------
THOUGHT:
The Wikipedia summary has been successfully retrieved. It appears to be related to a sporting event with medalists listed. Now, I will search for a function that can determine the sentiment of the text to fulfill the final part of the user's query.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: sentiment)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: sentiment)

CODE BLOCKS:
---
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']
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[840, 861]
type=FUNCTION
content={
def get_date_fact(date: str, ckpt=None):
    """
    It provides interesting facts about dates.
    """
    from dateutil import parser

    date_str = date
    dt = parser.parse(date_str)

    formatted_date = dt.strftime("%m/%d")

    url = f"https://numbersapi.p.rapidapi.com/{formatted_date}/date"
    params = {"fragment": "true", "json": "true"}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "numbersapi.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()

    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
}
---
(11 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 11 classes/functions found in unexpanded results (search again with the same query to see details):
def image_processing(img): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # 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_location(city: str, ckpt=None): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search did not return a function specifically for sentiment analysis. However, from the previous search results, I recall seeing a function named `text_classification` which could classify text into categories. I will use this function to determine the sentiment of the Wikipedia summary text, as it may classify the text into a sentiment category such as 'POSITIVE' or 'NEGATIVE'.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_classification

# Determine the sentiment of the Wikipedia summary text
sentiment_result = text_classification(wikipedia_summary['text'])
print(sentiment_result)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'POSITIVE'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_classification": <function text_classification at 0x7f2c0f962dd0>, "sentiment_result": {'text': 'POSITIVE'}}

********************************************************************************
Step 8
********************************************************************************

------Action------
THOUGHT:
The sentiment of the Wikipedia summary text has been determined as 'POSITIVE'. All parts of the user's query have been addressed: the text in the image has been transcribed, related information from Wikipedia has been retrieved, and the overall sentiment has been determined.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import optical_character_recognition, wikipedia_simple_search, text_classification
output0 = optical_character_recognition('/root/input_data/COCO_train2014_000000256855.jpg')
output1 = wikipedia_simple_search(output0['text'])
output2 = text_classification(output1['text'])

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

********************************************************************************
Step 9
********************************************************************************

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

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