import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"  # Stops warning crashes

import gradio as gr
from transformers import pipeline

# Lazy load models only when needed (saves memory on free tier)
def get_diversity_pipeline():
    return pipeline("text-classification", model="thksirhc/autotrain-media-analysis", device=-1)  # CPU

def get_objective_pipeline():
    return pipeline("text-classification", model="thksirhc/autotrain-HKm-objective", device=-1)

def get_genuine_pipeline():
    return pipeline("text-classification", model="thksirhc/autotrain-genuine", device=-1)

def analyze(text):
    if not text.strip():
        return "Please paste an article!"
    
    try:
        # Load and run one by one (safer on low RAM)
        d_pipe = get_diversity_pipeline()
        d_result = d_pipe(text)[0]
        diversity_score = float(d_result.get("score", 0)) if "score" in d_result else float(d_result["label"].replace("LABEL_", ""))

        o_pipe = get_objective_pipeline()
        o_result = o_pipe(text)[0]
        objective_score = float(o_result.get("score", 0)) if "score" in o_result else float(o_result["label"].replace("LABEL_", ""))

        g_pipe = get_genuine_pipeline()
        g_result = g_pipe(text)[0]
        genuine_score = float(g_result.get("score", 0)) if "score" in g_result else float(g_result["label"].replace("LABEL_", ""))

        return f"""
### EchoScore Results

**Diversity:** {diversity_score:.1f}/10  
**Objectiveness:** {objective_score:.1f}/10  
**Genuineness:** {genuine_score:.1f}/10
        """
    
    except Exception as e:
        return f"Error (check logs for details): {str(e)}\n\nFirst load can be slow (30-60s on free tier)—try again or short text."

demo = gr.Interface(
    fn=analyze,
    inputs=gr.Textbox(lines=12, placeholder="Paste HK news article here (short for test)...", label="Article"),
    outputs=gr.Markdown(),
    title="EchoScore HK Media Checker",
    description="Instant bias scores—be patient on first try (free server slow)."
)

demo.launch()