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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from huggingface_hub import login
import os
# Load the original pre-trained model
def load_model(model_name):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
return tokenizer, model
# Models to compare
original_model_name = "Vishwas1/hummingbird-base-marathi" # Replace with your original model
fine_tuned_model_name = "Vishwas1/hummingbird-finetuned-marathi" # Replace with your fine-tuned model's repo ID
# Load models
hf_token = os.getenv('HUGGINGFACE_TOKEN')
if not hf_token:
return "Error: Hugging Face token not found. Please set it as a secret."
# Login to Hugging Face Hub
login(hf_token)
original_tokenizer = AutoTokenizer.from_pretrained(original_model_name)
original_model = AutoModelForSequenceClassification.from_pretrained(original_model_name)
fine_tuned_tokenizer = AutoTokenizer.from_pretrained(fine_tuned_model_name)
fine_tuned_model = AutoModelForSequenceClassification.from_pretrained(fine_tuned_model_name)
# Ensure models are in evaluation mode
original_model.eval()
fine_tuned_model.eval()
def compare_models(text):
# Original model prediction
inputs_orig = original_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
with torch.no_grad():
outputs_orig = original_model(**inputs_orig)
logits_orig = outputs_orig.logits
probs_orig = torch.softmax(logits_orig, dim=1)
pred_orig = torch.argmax(probs_orig, dim=1).item()
confidence_orig = probs_orig[0][pred_orig].item()
# Fine-tuned model prediction
inputs_fine = fine_tuned_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
with torch.no_grad():
outputs_fine = fine_tuned_model(**inputs_fine)
logits_fine = outputs_fine.logits
probs_fine = torch.softmax(logits_fine, dim=1)
pred_fine = torch.argmax(probs_fine, dim=1).item()
confidence_fine = probs_fine[0][pred_fine].item()
# Map predictions to labels (adjust based on your model's labels)
labels = {0: "Negative", 1: "Positive"}
result = {
"Original Model Prediction": f"{labels[pred_orig]} ({confidence_orig:.2f})",
"Fine-Tuned Model Prediction": f"{labels[pred_fine]} ({confidence_fine:.2f})"
}
return result
# Gradio Interface
iface = gr.Interface(
fn=compare_models,
inputs=gr.Textbox(lines=5, placeholder="Enter text here...", label="Input Text"),
outputs=gr.JSON(label="Model Predictions"),
title="Compare Original and Fine-Tuned Models",
description="Enter text to see predictions from the original and fine-tuned models."
)
iface.launch()
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