app.py

import gradio as gr
from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load the tokenizer and model
tokenizer = BertTokenizer.from_pretrained('Non-playing-Character/RAGFiller')
model = BertForSequenceClassification.from_pretrained('Non-playing-Character/RAGFiller', num_labels=30)

# Define the labels
labels = [
    "That's an interesting question... let me see.",
    "Hmm, I need to consider that for a moment.",
    "Let me think about how best to address that.",
    "Well, I think it really depends on a few factors...",
    "Good thought! I need a moment to process that.",
    "You know, I've never really thought about it that way before.",
    "Okay, let me break that down for a second.",
    "That's a tough one... give me a second to gather my thoughts.",
    "I want to make sure I give you the right answer, so let me think.",
    "Let me reflect on that... there are a few angles to consider.",
    "Alright, if I remember correctly, it goes something like this...",
    "That's a good point, and I think the answer is...",
    "Good question! Let me take a moment to unpack that.",
    "Hmm, there's a lot to consider here. Give me a second.",
    "Let me think about that... it's not a straightforward answer.",
    "Interesting... I need to gather my thoughts on this.",
    "Well, let me consider the various aspects before I answer.",
    "Alright, let's break this down a bit before I answer.",
    "Good thought! I want to make sure I address it properly.",
    "Hmm, let's delve into that a bit more; I'll need a moment.",
    "Great question! I want to provide a thoughtful response, so let me think.",
    "That's a fascinating angle... let me think it through.",
    "I'll need a moment to come up with an answer.",
    "I'll take a quick moment to weigh my options.",
    "I appreciate the question; let me think it through.",
    "Let me take a step back and think that over.",
    "Let me mull that over for just a moment.",
    "I want to consider that carefully; let me pause for a second.",
    "Let's explore that further; I need a moment to think.",
    "I'd like to think that over before answering."
]

def classify_text(text):
    # Tokenize the input text
    inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
    outputs = model(**inputs)

    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    # Convert predictions to numpy array
    predictions = predictions.cpu().detach().numpy()

    labeled_predictions = {labels[i]: predictions[0][i] for i in range(len(labels))}
    max_label = labels[predictions[0].argmax()]
    max_probability = predictions[0].max()

    result = {max_label: max_probability}
    return result

# Create a Gradio interface
gradio_app = gr.Interface(fn=classify_text, inputs="text", outputs="label")

# Launch the interface
gradio_app.launch()