# Step 1: Import the necessary toolkits
# We need 'gradio' to build the web app and 'transformers' to use the Hugging Face model.
import gradio as gr
from transformers import pipeline

# Step 2: Load our AI Model
# We create a 'pipeline' which is a simple way to use a pre-trained model.
# We tell it the task ("sentiment-analysis") and the specific model we chose.
# The first time the app runs, it will download the model. This might take a minute.
print("Loading model...")
sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
print("Model loaded!")

# Step 3: Define the function for the app
# This function will take some text as input and use the model to predict the sentiment.
def analyze_sentiment(text):
    result = sentiment_pipeline(text)
    # The model returns a label ('POSITIVE' or 'NEGATIVE') and a confidence score.
    # We'll return the whole result so we can see both.
    return result

# Step 4: Create the Gradio Web App Interface
# This is where we design the look and feel of our app.
app = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(placeholder="Enter a sentence here..."),
    outputs="json",  # We'll use a JSON output to see the label and score clearly
    title="Sentiment Analyzer",
    description="Type in a sentence to see if its sentiment is POSITIVE or NEGATIVE. This app uses a DistilBERT model from Hugging Face.",
    examples=[
        ["The new Star Wars movie was incredible!"],
        ["I am not happy with the customer service."],
        ["The weather today is just okay."]
    ]
)

# Step 5: Launch the app!
app.launch()