app.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

import torch
import transformers

tokenizer = AutoTokenizer.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")
model = AutoModelForSequenceClassification.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")


# Load the model and tokenizer
# model = transformers.AutoModelForSequenceClassification.from_pretrained("facebook/bart-large-mnli")

# tokenizer = transformers.AutoTokenizer.from_pretrained("facebook/bart-large-mnli")

# Define a function to split a text into segments of 512 tokens
def split_text(text):
    #this prints progress
    print("going to split the 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

# Define a function to extract predictions from model output (adjust as needed)
def extract_predictions(outputs):
    # Assuming outputs contain logits and labels (adapt based on your model's output format)
    logits = outputs.logits
    probs = logits.softmax(dim=1)
    preds = torch.argmax(probs, dim=1)
    return probs, preds  # Return all probabilities and predicted labels
    
# a function that classifies text

def classify_text(text):

    # Split text into segments using split_text
    segments = split_text(text)

    # Initialize empty list for predictions
    predictions = []

    # Move device to GPU if available
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = model.to(device)

    # Loop through segments, process, and store predictions
    for segment in segments:
        inputs = tokenizer([segment], padding=True, return_tensors="pt")
        input_ids = inputs["input_ids"].to(device)
        attention_mask = inputs["attention_mask"].to(device)

        with torch.no_grad():
            outputs = model(input_ids, attention_mask=attention_mask)

        # Extract predictions for each segment
        probs, preds = extract_predictions(outputs)  # Define this function based on your model's output

        # Append predictions for this segment
        predictions.append({
            "segment_text": segment,
            "label": preds[0],  # Assuming single label prediction
            "probability": probs[preds[0]]  # Access probability for the predicted label
        })


interface = gr.Interface(
    fn=classify_text,
    inputs="text",
    outputs="text",
    title="Text Classification Demo",
    description="Enter some text, and the model will classify it.",
    #choices=["depression", "anxiety", "bipolar disorder", "schizophrenia", "PTSD", "OCD", "ADHD", "autism", "eating disorder", "personality disorder", "phobia"]  # Adjust class names
)

interface.launch()