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# app.py
"""
Gradio app for the workshop.
Two modes:
- Single Review -> instant sentiment
- Upload CSV (with 'review' column) -> run batch predictions and download CSV
"""
import gradio as gr
import pandas as pd
from model import analyze_text, analyze_batch
from data_loader import load_data
from utils import add_predictions_to_df
from io import StringIO, BytesIO
# Single prediction function
def predict_single(review: str):
pred = analyze_text(review)
label = pred.get('label')
score = pred.get('score')
return label, float(score)
# Batch prediction function for uploaded CSV
def predict_file(file_obj):
"""
Accepts an uploaded CSV file (file-like). Returns a downloadable CSV with predictions.
"""
try:
df = pd.read_csv(file_obj.name) if hasattr(file_obj, "name") else pd.read_csv(file_obj)
except Exception as e:
return "Error reading CSV: " + str(e), None
# find review-like column
cols = df.columns
text_cols = [c for c in cols if 'review' in c.lower() or 'text' in c.lower()]
if not text_cols:
return "Uploaded CSV must contain a text column named like 'review' or 'text'.", None
text_col = text_cols[0]
texts = df[text_col].fillna("").astype(str).tolist()
preds = analyze_batch(texts, batch_size=32)
out_df = add_predictions_to_df(df, preds)
# prepare downloadable CSV in memory
buffer = StringIO()
out_df.to_csv(buffer, index=False)
buffer.seek(0)
return "Success: Predictions added", ("predictions.csv", buffer.getvalue(), "text/csv")
# Optional demo: load a few rows from local imdb.csv (if present)
def demo_sample():
try:
df = load_data()
sample = df.head(5).to_dict(orient='records')
# show text samples in the UI
texts = [r['review'] for r in sample]
preds = analyze_batch(texts, batch_size=8)
return {f"Review {i+1}": (texts[i], preds[i]['label'], preds[i]['score']) for i in range(len(texts))}
except Exception as e:
return {"error": str(e)}
with gr.Blocks() as demo:
gr.Markdown("# Movie Review Sentiment — Workshop App")
gr.Markdown("**Single prediction** — Type a review and get sentiment.")
with gr.Row():
txt = gr.Textbox(lines=4, label="Enter movie review here")
out_label = gr.Textbox(label="Predicted label")
out_score = gr.Number(label="Confidence score")
btn = gr.Button("Analyze")
btn.click(fn=lambda t: predict_single(t), inputs=[txt], outputs=[out_label, out_score])
gr.Markdown("----")
gr.Markdown("**Batch prediction** — Upload a CSV with a `review` (or `text`) column.")
csv_in = gr.File(label="Upload CSV")
status = gr.Textbox(label="Status")
download_button = gr.File(label="Download predictions (after running)")
run_btn = gr.Button("Run batch predictions")
def run_and_return(file):
msg, download = predict_file(file)
# gr.File requires a filename/path: return tuple (filename, content, mime)
return msg, download
run_btn.click(fn=run_and_return, inputs=[csv_in], outputs=[status, download_button])
gr.Markdown("----")
gr.Markdown("**Demo sample (if `imdb.csv` exists locally)**")
sample_btn = gr.Button("Load demo sample & predict")
demo_output = gr.JSON()
sample_btn.click(fn=demo_sample, inputs=None, outputs=[demo_output])
if __name__ == "__main__":
# When running locally for the workshop
demo.launch(server_name="0.0.0.0", server_port=7860)
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