Application says "No task available"

I'm using prodigy for text classification,, we faced issue while annotation, so it stops before completing the task, the actual message says "No Tasks Available". We're using custom recipe.. Here's the code

import prodigy
from prodigy.components.preprocess import add_tokens
from prodigy.components.loaders import JSONL
import spacy

@prodigy.recipe(
"classification-context",
dataset=("The dataset to save to", "positional", None, str),
file_path=("Path to texts", "positional", None, str),
)
def cat_facts_ner(dataset, file_path, lang="en"):

blocks = [
    {"view_id": "choice", "text": None},
    {"view_id": "text_input", "field_rows": 5, "field_label": "Feedback"}
]

# nlp = spacy.blank(lang)           # blank spaCy pipeline for tokenization
stream = JSONL(file_path)     # load in the JSONL file

stream = add_options(stream)  # add label options to each task
# stream = add_tokens(nlp, stream)  # tokenize the stream for ner_manual

return {
    "dataset": dataset,          # the dataset to save annotations to
    "view_id": "blocks",         # set the view_id to "blocks"
    "stream": list(stream),      # the stream of incoming examples
    "config": {
        "blocks": blocks       # add the blocks to the config
    }
}

def add_options(stream):
# Helper function to add options to every task in a stream
options = [
{"id": 3, "text": "Positive"},
{"id": 2, "text": "Negative"},
{"id": 1, "text": "Neutral"},
{"id": 0, "text": "Ambivalent"}
]

for task in stream:
    task["options"] = options
    yield task

Hi! Did you also post this thread by any chance? The code looks almost identical. If so, I already left a reply with more questions here:

No this is first time i posted here..
Many thanks.. "force_stream_order": true solved the problem.. Could you please add this to documentation i noticed that it's not mentioned there https://prodi.gy/docs/install

Glad it's working now! And the setting has become obsolete in newer versions of Prodigy, it's only relevant for v1.10 and below, which is why we don't focus on it in the docs anymore :slightly_smiling_face: