app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 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 = "bert-base-uncased"  # Replace with your original model
fine_tuned_model_name = "Vishwas1/bert-base-imdb"  # Replace with your fine-tuned model's repo ID

# Load models
original_tokenizer, original_model = load_model(original_model_name)
fine_tuned_tokenizer, fine_tuned_model = load_model(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()