app.py

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

# ── Model & tokenizer ───────────────────────────────────────────────────────────
model_name = "microsoft/Phi-3-mini-4k-instruct"  # 3.8 B‑param open instruct model :contentReference[oaicite:0]{index=0}

# seed for reproducibility
torch.random.manual_seed(0)

# load tokenizer (no gating, open access) :contentReference[oaicite:1]{index=1}
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# load as causal‑LM, quantize to int8 to halve memory & speed up inference :contentReference[oaicite:2]{index=2}
model = AutoModelForCausalLM.from_pretrained(model_name)
# compile once for ~30% further speed‑up :contentReference[oaicite:3]{index=3}
model = torch.compile(model)

# ── Prompt template ──────────────────────────────────────────────────────────────
prompt_prefix = """
You are an energy‑saving expert tasked to help households reduce their monthly electricity bills.
Given the user's appliance usage information (device name, wattage, hours used per day, days used per week):
1. Flag the highest energy consumers.
2. Recommend practical, empathetic, achievable actions.
3. Suggest appliance swaps (e.g. LED, inverter AC) and habit changes.
Format with bullet points.
Here is the summary:
"""

# ── Generation function ──────────────────────────────────────────────────────────
def generate_recommendation(appliance_info: str) -> str:
    prompt = prompt_prefix + appliance_info + "\n\nRecommendations:"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=150,
            use_cache=True,
            do_sample=False,
            temperature=0.0
        )
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    # return only the recommendations section
    return text.split("Recommendations:")[-1].strip()

# ── Gradio interface ────────────────────────────────────────────────────────────
iface = gr.Interface(
    fn=generate_recommendation,
    inputs=gr.Textbox(lines=8, placeholder="e.g. Refrigerator: 150 W, 8 h/day, 7 days/week\n..."),
    outputs="text",
    title="Energy‑Saving Tips (Phi‑3‑Mini‑4K‑Instruct)",
    description="Paste your per‑appliance summary to get targeted energy‑saving recommendations."
)

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