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import streamlit as st
import gc
from collections import defaultdict
from transformers import pipeline
from lingua import Language, LanguageDetectorBuilder
__version__ = "0.1.0"
#if torch.cuda.is_available():
# device_tag = 0 # first gpu
#else:
#device_tag = -1 # cpu
default_models = {
Language.ENGLISH: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.JAPANESE: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.ARABIC: "Ammar-alhaj-ali/arabic-MARBERT-sentiment",
Language.GERMAN: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.SPANISH: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.FRENCH: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.CHINESE: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.INDONESIAN: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.HINDI: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.ITALIAN: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.MALAY: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.PORTUGUESE: "lxyuan/distilbert-base-multilingual-cased-sentiments-student",
Language.SWEDISH: "KBLab/robust-swedish-sentiment-multiclass",
Language.FINNISH: "fergusq/finbert-finnsentiment",
}
language_detector = LanguageDetectorBuilder.from_all_languages().build()
def split_message(message, max_length):
""" Split a message into a list of chunks of given maximum size. """
return [message[i: i + max_length] for i in range(0, len(message), max_length)]
def process_messages_in_batches(messages_with_languages, models=None, max_length=512):
"""
Process messages in batches, creating only one pipeline at a time, and maintain the original order.
Params:
messages_with_languages: list of tuples, each containing a message and its detected language
models: dict, model paths indexed by Language
Returns:
OrderedDict: containing the index as keys and tuple of (message, sentiment result) as values
"""
if models is None:
models = default_models
else:
models = default_models.copy().update(models)
results = {}
# Group messages by model, preserving original order.
# If language is no detected or a model for that language is not
# provided, add None to results
messages_by_model = defaultdict(list)
for index, (message, language) in enumerate(messages_with_languages):
model_name = models.get(language)
if model_name:
messages_by_model[model_name].append((index, message))
else:
results[index] = {"label": "none", "score": 0}
# Process messages and maintain original order
for model_name, batch in messages_by_model.items():
sentiment_pipeline = pipeline(model=model_name, device=device_tag)
chunks = []
message_map = {}
for idx, message in batch:
message_chunks = split_message(message, max_length)
for chunk in message_chunks:
chunks.append(chunk)
if idx in message_map:
message_map[idx].append(len(chunks) - 1)
else:
message_map[idx] = [len(chunks) - 1]
chunk_sentiments = sentiment_pipeline(chunks)
for idx, chunk_indices in message_map.items():
sum_scores = {"neutral": 0}
for chunk_idx in chunk_indices:
label = chunk_sentiments[chunk_idx]["label"]
score = chunk_sentiments[chunk_idx]["score"]
if label in sum_scores:
sum_scores[label] += score
else:
sum_scores[label] = score
best_sentiment = max(sum_scores, key=sum_scores.get)
score = sum_scores[best_sentiment] / len(chunk_indices)
results[idx] = {"label": best_sentiment, "score": score}
# Force garbage collections to remove the model from memory
del sentiment_pipeline
gc.collect()
# Unify common spellings of the labels
for i in range(len(results)):
results[i]["label"] = results[i]["label"].lower()
results = [results[i] for i in range(len(results))]
return results
def sentiment(messages, models=None):
"""
Estimate the sentiment of a list of messages (strings of text). The
sentences may be in different languages from each other.
We maintain a list of default models for some languages. In addition,
the user can provide a model for a given language in the models
dictionary. The keys for this dictionary are lingua.Language objects
and items HuggingFace model paths.
Params:
messages: list of message strings
models: dict, huggingface model paths indexed by lingua.Language
Returns:
OrderedDict: containing the index as keys and tuple of (message, sentiment result) as values
"""
messages_with_languages = [
(message, language_detector.detect_language_of(message)) for message in messages
]
results = process_messages_in_batches(messages_with_languages, models)
return results
def main():
st.title("Sentiment Analysis Pipeline")
messages_input = st.text_area("Enter your messages (one per line):", height=200)
messages = [message.strip() for message in messages_input.split('\n') if message.strip()]
if st.button("Analyze Sentiments"):
results = sentiment(messages)
st.write("## Results:")
for idx, result in enumerate(results):
message = messages[idx]
sentiment_label = result["label"]
sentiment_score = result["score"]
st.write(f"**Message:** {message}")
st.write(f"**Sentiment:** {sentiment_label.capitalize()} (Score: {sentiment_score:.2f})")
if __name__ == "__main__":
main() |