# pip install unsloth transformers gradio pillow torch bitsandbytes
import os


# 1) Read & validate your token
hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
if not hf_token:
    raise ValueError("Please set HF_TOKEN (or HUGGINGFACE_HUB_TOKEN) in your environment")

# 2) Make it available to transformers
from huggingface_hub import login
login(token=hf_token)
os.environ["HUGGINGFACE_HUB_TOKEN"] = hf_token

# 3) Workaround for libgomp thread issue
os.environ["OMP_NUM_THREADS"] = "1"

import gradio as gr
from transformers import pipeline
import torch
from PIL import Image

# 4) Initialize pipeline with auth
pipe = pipeline(
    task="image-text-to-text",
    model="EpistemeAI/PD_gemma-3n-E4B-v2",
    use_auth_token=True,
)

def convert_image_to_text(image: Image.Image) -> str:
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text",  "text": "Analyze the plant disease and provide solution"}
            ]
        }
    ]
    response = pipe(
        text=messages,
        return_full_text=False,
        max_new_tokens=700
    )
    return response[0]["generated_text"]

# 5) Gradio app
demo = gr.Interface(
    fn=convert_image_to_text,
    inputs=gr.Image(type="pil", label="Upload an image"),
    outputs=gr.Textbox(label="Analysis"),
    title="Plant Disease Scientific Analyzer",
    description="Upload a plant image and receive a diagnostic report."
)

if __name__ == "__main__":
    demo.launch()