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from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Define the model and tokenizer
tokenizer_emotion = AutoTokenizer.from_pretrained("SamLowe/roberta-base-go_emotions")
model_emotion = AutoModelForSequenceClassification.from_pretrained("SamLowe/roberta-base-go_emotions")
from transformers import AutoModelForTokenClassification
tokenizer_t_class = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model_t_class = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
# FastAPI app
app = FastAPI()
# Define the request body
class Input(BaseModel):
text: str
@app.post("/emotion")
async def predict_emotion(input: Input):
# Tokenize the input text
inputs = tokenizer_emotion(input.text, return_tensors="pt")
# Get the model's predictions
outputs = model_emotion(**inputs)
# Get the predicted class
predicted_class_idx = torch.argmax(outputs.logits).item()
# Decode the predicted class
predicted_class = model_emotion.config.id2label[predicted_class_idx]
# Return the prediction
return {"predicted_emotion": predicted_class}
# from transformers import pipeline
@app.post("/ner")
async def perform_ner(item: Input):
# nlp = pipeline("ner", model=model_t_class, tokenizer=tokenizer_t_class)
# ner_results = nlp(item.text)
# print(ner_results)
# Tokenize the input text
inputs = tokenizer_t_class.encode(item.text, return_tensors="pt")
# Get the model's predictions
outputs = model_t_class(inputs).logits
# Get the predicted classes for each token
predictions = torch.argmax(outputs, dim=2)
# Decode the tokens and their predicted classes
tokens = tokenizer_t_class.convert_ids_to_tokens(inputs[0])
predicted_labels = [model_t_class.config.id2label[prediction] for prediction in predictions[0].tolist()]
# Pair tokens with their predicted labels
token_labels = list(zip(tokens, predicted_labels))
# Filter out subwords
filtered_token_labels = [(token, label) for token, label in token_labels if not token.startswith("##")]
# Return the tokens and their predicted labels
return {"token_labels": filtered_token_labels}
# if __name__ == "__main__":
# import uvicorn
# uvicorn.run(app, host="0.0.0.0", port=8000)
import streamlit as st
st.title("Emotion Detection App")
input_text = st.text_input("Enter your text here")
if st.button("Predict"):
inputs = tokenizer_emotion(input_text, return_tensors="pt")
# Get the model's predictions
outputs = model_emotion(**inputs)
# Get the predicted class
predicted_class_idx = torch.argmax(outputs.logits).item()
# Decode the predicted class
predicted_class = model_emotion.config.id2label[predicted_class_idx]
# Return the prediction
st.write({"predicted_emotion": predicted_class})
# Tokenize the input text
inputs = tokenizer_t_class.encode(input_text, return_tensors="pt")
# Get the model's predictions
outputs = model_t_class(inputs).logits
# Get the predicted classes for each token
predictions = torch.argmax(outputs, dim=2)
# Decode the tokens and their predicted classes
tokens = tokenizer_t_class.convert_ids_to_tokens(inputs[0])
predicted_labels = [model_t_class.config.id2label[prediction] for prediction in predictions[0].tolist()]
# Pair tokens with their predicted labels
token_labels = list(zip(tokens, predicted_labels))
# Filter out subwords
filtered_token_labels = [(token, label) for token, label in token_labels if not token.startswith("##")]
# Return the tokens and their predicted labels
st.write({"token_labels": filtered_token_labels}) |