import streamlit as st
from transformers import pipeline
from config import IMAGE_MODEL_NAME

@st.cache_resource
def load_image_model():
    """
    Loads an image captioning model recognized by the HF pipeline.
    Example: "nlpconnect/vit-gpt2-image-captioning" or "Salesforce/blip-image-captioning-base".
    """
    return pipeline("image-to-text", model=IMAGE_MODEL_NAME)

def analyze_image(image_file, image_model):
    """
    Pass an uploaded image to the loaded pipeline for caption generation.
    """
    try:
        result = image_model(image_file)
        if isinstance(result, list) and len(result) > 0:
            return result[0].get("generated_text", "No caption.")
        return "No output from the model."
    except Exception as e:
        return f"Error analyzing image: {str(e)}"