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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import re
import pandas as pd
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")
model = AutoModelForSequenceClassification.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Define a function to split a text into segments of 512 tokens
def split_text(text):
text=re.sub(r'[^a-zA-Z\s]','',text)
text=str(text)
# Tokenize the text
tokens = tokenizer.tokenize(text)
# Initialize an empty list for segments
segments = []
# Initialize an empty list for current segment
current_segment = []
# Initialize a counter for tokens
token_count = 0
# Loop through the tokens
for token in tokens:
# Add the token to the current segment
current_segment.append(token)
# Increment the token count
token_count += 1
# If the token count reaches 512 or the end of the text, add the current segment to the segments list
if token_count == 512 or token == tokens[-1]:
# Convert the current segment to a string and add it to the segments list
segments.append(tokenizer.convert_tokens_to_string(current_segment))
# Reset the current segment and the token count
current_segment = []
token_count = 0
# Return the segments list
return segments
def classify(text):
# Define the labels
labels = ["depression", "anxiety", "bipolar disorder", "schizophrenia", "PTSD", "OCD", "ADHD", "autism", "eating disorder", "personality disorder", "phobia"]
#labels=list(model.config.id2label)
# Encode the labels
label_encodings = tokenizer(labels, padding=True, return_tensors="pt")
# Split the text into segments
segments = split_text(text)
# Initialize an empty list for logits
logits_list = []
# Loop through the segments
for segment in segments:
# Encode the segment and the labels
inputs = tokenizer([segment] + labels, padding=True, return_tensors="pt")
# Get the input ids and attention mask
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
# Move the input ids and attention mask to the device
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
# Get the model outputs for each segment
with torch.no_grad():
outputs = model(
input_ids,
attention_mask=attention_mask,
)
# Get the logits for each segment and append them to the logits list
logits = outputs.logits
logits_list.append(logits)
# Average the logits across the segments
avg_logits = torch.mean(torch.stack(logits_list), dim=0)
# Apply softmax to convert logits to probabilities
probabilities = torch.softmax(avg_logits, dim=1)
# Get the probabilities for each label
label_probabilities = probabilities[:, :len(labels)].tolist()
# Get the top 3 most likely labels and their probabilities
# Get the top 3 most likely labels and their probabilities
top_labels = []
top_probabilities = []
label_probabilities = label_probabilities[0] # Extract the list of probabilities for the first (and only) example
for _ in range(3):
max_prob_index = label_probabilities.index(max(label_probabilities))
top_labels.append(labels[max_prob_index])
top_probabilities.append(max(label_probabilities))
label_probabilities[max_prob_index] = 0 # Set the max probability to 0 to get the next highest probability
# Create a dictionary to store the results
results = {
"sequence": text,
"top_labels": top_labels,
"top_probabilities": top_probabilities
}
return results
# Streamlit app
st.title("Text Classification.")
st.write("Enter some text, and the model will classify it.")
text_input = st.text_input("Text Input")
#if st.button("Classify"):
predictions = classify(text_input)
labels_str=",".join(predictions["top_labels"])
probs_ints=",".join(map(str,predictions["top_probabilities"]))
#df=pd.DataFrame({'probabilities: ',probs_ints})
#formated_df=df.styled.format("{:.2f}").to_dict('list')
#for prediction in predictions:
# st.write(f"Segment Text: {prediction['segment_text']}")
st.write(f"Label: {labels_str}")
st.write(f"Probability: {probs_ints}") |