import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from sentence_transformers import SentenceTransformer
import re

# Load model & SBERT
model = load_model("depression_sbert_optuna_model.keras")
sbert = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

# Clean text
def clean_text(text):
    text = re.sub(r"http\S+", "", text)
    text = re.sub(r"[^a-zA-Z\s]", "", text)
    return text.strip().lower()

# Prediction logic
def predict_depression(text):
    cleaned = clean_text(text)
    embedding = sbert.encode([cleaned])
    prob = model.predict(np.array(embedding))[0][0]
    label = "Depressed" if prob > 0.5 else "Not Depressed"
    confidence = round(prob * 100 if label == "Depressed" else (1 - prob) * 100, 2)
    return f"{label} (Confidence: {confidence}%)"

# Gradio UI
interface = gr.Interface(
    fn=predict_depression,
    inputs=gr.Textbox(lines=4, placeholder="Enter a post..."),
    outputs="text",
    title="DeepPressNet - Depression Detection",
    description="Enter a post to detect if it's likely written by someone showing signs of depression."
)

interface.launch()