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import gradio as gr
from inference import SentimentInference
import os
from datasets import load_dataset
import random
# --- Initialize Sentiment Model ---
CONFIG_PATH = os.path.join(os.path.dirname(__file__), "config.yaml")
if not os.path.exists(CONFIG_PATH):
CONFIG_PATH = "config.yaml"
if not os.path.exists(CONFIG_PATH):
raise FileNotFoundError(
f"Configuration file not found. Tried {os.path.join(os.path.dirname(__file__), 'config.yaml')} and {CONFIG_PATH}. "
f"Ensure 'config.yaml' exists and is accessible."
)
print(f"Loading model with config: {CONFIG_PATH}")
try:
sentiment_inferer = SentimentInference(config_path=CONFIG_PATH)
print("Sentiment model loaded successfully.")
except Exception as e:
print(f"Error loading sentiment model: {e}")
sentiment_inferer = None
# --- Load IMDB Dataset ---
print("Loading IMDB dataset for samples...")
try:
imdb_dataset = load_dataset("imdb", split="test")
print("IMDB dataset loaded successfully.")
except Exception as e:
print(f"Failed to load IMDB dataset: {e}. Sample loading will be disabled.")
imdb_dataset = None
def load_random_imdb_sample():
"""Loads a random sample text from the IMDB dataset."""
if imdb_dataset is None:
return "IMDB dataset not available. Cannot load sample.", None
random_index = random.randint(0, len(imdb_dataset) - 1)
sample = imdb_dataset[random_index]
return sample["text"], sample["label"]
def predict_sentiment(text_input, true_label_state):
"""Predicts sentiment for the given text_input."""
if sentiment_inferer is None:
return "Error: Sentiment model could not be loaded. Please check the logs.", true_label_state
if not text_input or not text_input.strip():
return "Please enter some text for analysis.", true_label_state
try:
prediction = sentiment_inferer.predict(text_input)
sentiment = prediction['sentiment']
# Convert numerical label to text if available
true_sentiment = None
if true_label_state is not None:
true_sentiment = "positive" if true_label_state == 1 else "negative"
result = f"Predicted Sentiment: {sentiment.capitalize()}"
if true_sentiment:
result += f"\nTrue IMDB Label: {true_sentiment.capitalize()}"
return result, None # Reset true label state after display
except Exception as e:
print(f"Error during prediction: {e}")
return f"Error during prediction: {str(e)}", true_label_state
# --- Gradio Interface ---
with gr.Blocks() as demo:
true_label = gr.State()
gr.Markdown("## IMDb Sentiment Analyzer")
gr.Markdown("Enter a movie review to classify its sentiment as Positive or Negative, or load a random sample from the IMDb dataset.")
with gr.Row():
input_textbox = gr.Textbox(lines=7, placeholder="Enter movie review here...", label="Movie Review", scale=3)
output_text = gr.Text(label="Analysis Result", scale=1)
with gr.Row():
submit_button = gr.Button("Analyze Sentiment")
load_sample_button = gr.Button("Load Random IMDB Sample")
gr.Examples(
examples=[
["This movie was absolutely fantastic! The acting was superb and the plot was gripping."],
["I was really disappointed with this film. It was boring and the story made no sense."],
["An average movie, had some good parts but overall quite forgettable."],
["Wow so I don't think I've ever seen a movie quite like that. The plot was... interesting, and the acting was, well, hmm."]
],
inputs=input_textbox
)
# Wire actions
submit_button.click(
fn=predict_sentiment,
inputs=[input_textbox, true_label],
outputs=[output_text, true_label]
)
load_sample_button.click(
fn=load_random_imdb_sample,
inputs=None,
outputs=[input_textbox, true_label]
)
if __name__ == '__main__':
print("Launching Gradio interface...")
demo.launch(share=False)
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