import streamlit as st
import requests

# Open-source LLM endpoint (replace if needed)
API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-small"

# If you have a Hugging Face token, include it here. If not, this model works without one.
headers = {"Authorization": ""}

def query_llm(prompt):
    payload = {"inputs": prompt}
    response = requests.post(API_URL, headers=headers, json=payload)

    try:
        output = response.json()
        if isinstance(output, list) and 'generated_text' in output[0]:
            return output[0]['generated_text']
        elif isinstance(output, dict) and "error" in output:
            return f"❌ API Error: {output['error']}"
        else:
            return "❌ Unexpected API response format."
    except Exception as e:
        return f"❌ Failed to parse response: {str(e)}"

# Streamlit UI
st.set_page_config(page_title="Science Lab Assistant", page_icon="🧪")
st.title("🧪 Science Lab Assistant")
st.subheader("Ask me about school science experiments!")

with st.form("lab_form"):
    experiment = st.text_input("🔍 Enter your experiment name or question:")
    hypothesis = st.text_area("💡 What is your hypothesis?")
    submitted = st.form_submit_button("Explain Experiment")

if submitted and experiment:
    prompt = f"Explain the experiment '{experiment}' for a school science class. The student hypothesizes: '{hypothesis}'. Provide an explanation and possible result."
    result = query_llm(prompt)
    st.markdown("### 🧠 AI Explanation")
    st.write(result)

st.markdown("---")
st.markdown("📚 Example: `Vinegar and baking soda`")
st.markdown("💡 Hypothesis: `Combining them will create bubbles.`")