from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr

# --- Load model ---
model_name = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# --- Create pipeline ---
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device=-1  # CPU (Hugging Face free tier may not support GPU)
)

# --- Function to get AI response ---
def ask_oscar(question):
    if not question.strip():
        return "Please ask a question about medicinal plants."
    response = generator(question, max_length=200, do_sample=True, temperature=0.7)
    return response[0]['generated_text']

# --- Gradio interface ---
with gr.Blocks() as demo:
    gr.HTML(
        """
        <h1>🌿 AskOscar Herbal AI 🌿</h1>
        <p>Ask me anything about medicinal plants, healing herbs, or natural remedies worldwide.</p>
        """
    )
    user_input = gr.Textbox(label="Your Question")
    output = gr.Textbox(label="AI Answer")
    btn = gr.Button("Ask")

    btn.click(fn=ask_oscar, inputs=user_input, outputs=output)

# --- Launch ---
demo.launch()