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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Can you tell me what the weather forecast is for the coordinates 17.14 longitude and 32.81 latitude? I am planning my week and need to know if it's best to carry an umbrella or don a sunhat."
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:
Before writing any code, I need to search for a function within the `mnm` codebase that can retrieve weather information for a given set of geographical coordinates. I will start by searching for a function that might be related to weather retrieval.
ACTION TYPE:
search
ACTION CONTENT:
text: weather AND (type: FUNCTION)

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

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

Prototypes for the next 7 out of 31 classes/functions found in unexpanded results (search again with the same query to see details):
def get_full_path_data(filename): ... # mnm/tool_api.py
def text_processing(file_path): ... # 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 save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_weather` from the `mnm` codebase is exactly what is needed to retrieve the weather forecast data based on specific geographical coordinates. I will write the code to call this function with the provided longitude and latitude values.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_weather

# Longitude and latitude values provided by the user
longitude = "17.14"
latitude = "32.81"

# Retrieve the weather forecast for the given coordinates
weather_forecast = get_weather(lon=longitude, lat=latitude)
print(weather_forecast)

-----Response-----
RESPONSE (Execution Result):
EXECUTION ERROR:
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/requests/models.py", line 971, in json
    return complexjson.loads(self.text, **kwargs)
  File "/opt/conda/lib/python3.10/json/__init__.py", line 346, in loads
    return _default_decoder.decode(s)
  File "/opt/conda/lib/python3.10/json/decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/opt/conda/lib/python3.10/json/decoder.py", line 355, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

During handling of the above exception, another exception occurred:

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 8, in <module>
  File "/root/mnm/mnm/tool_api.py", line 980, in get_weather
    result_formatted = {"objects": response.json()["dataseries"]}
  File "/opt/conda/lib/python3.10/site-packages/requests/models.py", line 975, in json
    raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
requests.exceptions.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_weather": <function get_weather at 0x7f31151040d0>, "longitude": 17.14, "latitude": 32.81}

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

------Action------
THOUGHT:
The execution error indicates that the response from the weather API could not be decoded as JSON. This could be due to a variety of reasons, such as the API endpoint being incorrect, the API service being down, or the API response not being in JSON format. Since I cannot fix external API issues, I will check if the function `get_weather` is handling the response correctly and if there are any alternative ways to handle the response within the `mnm` codebase.
ACTION TYPE:
search
ACTION CONTENT:
text: get_weather AND (type: FUNCTION)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[17, 22]
type=FUNCTION
content={
def get_full_path_data(
    filename,
):  # get the path of data which isn't generated during the process
    base_path = MNM_BASE_PATH_DATA
    return os.path.join(base_path, filename)
}
---
file_path=mnm/tool_api.py
lines=[100, 106]
type=FUNCTION
content={
def audio_processing(audio_file):
    if os.path.exists(audio_file):
        return audio_file
    else:
        full_path = get_full_path_data(audio_file)
        return full_path
}
---
file_path=mnm/tool_api.py
lines=[463, 489]
type=FUNCTION
content={
def optical_character_recognition(
    image, ckpt=MODEL_SELECTION["optical_character_recognition"]
):
    """
    It takes an image and outputs recognized texts in the image.
    """
    import easyocr

    if isinstance(image, str):
        image_path = image if os.path.exists(image) else get_full_path_data(image)
    else:
        image = image_processing(image)
        image_path = os.path.join(MNM_BASE_PATH_DATA, "ocr_temp.jpg")
        image.save(image_path)

    assert ckpt == "easyOCR"  # only support easyocr for now
    reader = easyocr.Reader(
        ["en"]
    )  # this needs to run only once to load the model into memory
    result = reader.readtext(image_path)
    result_text = [
        text for _, text, _ in result
    ]  # Extract only the text from the result
    result_formatted = {"text": ", ".join(result_text)}

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

Prototypes for the next 7 out of 33 classes/functions found in unexpanded results (search again with the same query to see details):
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def get_location(city: 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

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

------Action------
THOUGHT:
The search results did not return any additional information about handling API responses or alternative methods for the `get_weather` function. It seems that the function is straightforward and expects a JSON response from the API. Since the error is related to the API response and not the code itself, and there are no alternative weather-related functions in the codebase, I will have to report that the task cannot be completed due to external API issues.
ACTION TYPE:
done
ACTION CONTENT:
False

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

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

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

-----Response-----
RESPONSE (User Message):
Please immediately take a code_summary action to generate a summary of your code produced so far. If you have not produced any code yet, make an educated guess based on what you have learned so far.

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

------Action------
THOUGHT:
The user has requested a code summary of the actions taken so far. I will summarize the code that was written to call the `get_weather` function, which is intended to retrieve weather forecast data based on geographical coordinates.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_weather
weather_forecast = get_weather(lon="17.14", lat="32.81")

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