app.py

import torch
import transformers
import gradio as gr

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

tokenizer = transformers.AutoTokenizer.from_pretrained(
    "distilbert/distilbert-base-uncased",
    use_fast=True
)

model = transformers.AutoModelForSequenceClassification.from_pretrained(
    "sirunchained/imdb-text-classifier"
)


for name, param in model.named_parameters():
    print(name, param.mean().item())
    break

print(model.config)



model.to(device)
model.eval()

def predict_sentiment(text):
    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        padding=True,
        max_length=512
    )
    inputs = {k: v.to(device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model(**inputs)

    probs = torch.softmax(outputs.logits, dim=-1)[0]
    negative = float(probs[0])
    positive = float(probs[1])

    if positive > negative:
        sentiment = "POSITIVE 😄"
        confidence = positive
        color = "orange"
    else:
        sentiment = "NEGATIVE 😡"
        confidence = negative
        color = "red"

    return (
        {
            "Negative": negative,
            "Positive": positive,
        },
        sentiment,
        confidence,
        gr.update(visible=True, value=confidence),
        gr.update(elem_classes=color)
    )


with gr.Blocks(css="""
.orange .bar {background-color: orange !important;}
.red .bar {background-color: red !important;}
""") as demo:

    gr.Markdown("## 🎬 IMDb Sentiment Analyzer")

    text_input = gr.Textbox(
        label="Enter movie review (press Shift+Enter to predict)",
        lines=4,
        placeholder="This movie was absolutely amazing..."
    )

    with gr.Row():
        label_output = gr.Label(num_top_classes=2)
        sentiment_text = gr.Markdown()

    confidence_bar = gr.Slider(
        minimum=0,
        maximum=1,
        step=0.01,
        label="Model Confidence",
        interactive=False,
        visible=False
    )

    text_input.submit(
        predict_sentiment,
        inputs=text_input,
        outputs=[
            label_output,
            sentiment_text,
            confidence_bar,
            confidence_bar,
            label_output
        ]
    )

demo.launch()