app.py

# app.py

import gradio as gr
import pandas as pd
import torch
from sentence_transformers import SentenceTransformer, util

# 1) Load your committed Excel database
df = pd.read_excel("BuyWellDataBase.xlsx")

# 2) Prepare the "combined" field and compute embeddings for each row
df["combined"] = df["Company Name"] + " — " + df["Product or Services"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
row_embeddings = model.encode(df["combined"].tolist(), convert_to_tensor=True)

# 3) Build a centroid embedding for each sector
sectors = df["Sector"].unique().tolist()
sector_embeddings = {}
for sector in sectors:
    # indices of rows in this sector
    idxs = df.index[df["Sector"] == sector].tolist()
    # mean of their embeddings
    sector_embeddings[sector] = row_embeddings[idxs].mean(dim=0, keepdim=True)

def recommend_store(query: str) -> str:
    q = query.strip()
    if not q:
        return "<p style='color:red;'>❗ Please enter what you’d like to buy.</p>"

    # 1) Embed the query
    q_emb = model.encode(q, convert_to_tensor=True, normalize_embeddings=True)

    # 2) Find the best-matching sector via cosine similarity
    sector_sims = {
        sector: util.cos_sim(q_emb, emb).item()
        for sector, emb in sector_embeddings.items()
    }
    best_sector = max(sector_sims, key=sector_sims.get)

    # 3) Within that sector, score all rows and pick top 5
    mask = df["Sector"] == best_sector
    sector_idxs = df.index[mask].tolist()
    sims = util.cos_sim(q_emb, row_embeddings[sector_idxs])[0]
    top5 = torch.topk(sims, k=min(5, len(sector_idxs))).indices.tolist()

    # 4) Build an HTML table of the top 5
    rows = []
    for rank_idx in top5:
        df_idx = sector_idxs[rank_idx]
        row = df.loc[df_idx]
        rows.append(f"""
<tr>
  <td><strong>{row['Company Name']}</strong></td>
  <td>{row['Product or Services']}</td>
  <td style="text-align:center;">{row['Rating']}</td>
  <td>{row['Google Map Location']}</td>
  <td><a href="{row['Website']}" target="_blank">Visit site</a></td>
</tr>
""")
    table_html = f"""
<h3>Top 5 alternatives in <em>{best_sector}</em></h3>
<table border="1" cellpadding="4" cellspacing="0">
  <thead style="background:#f0f0f0;">
    <tr>
      <th>Company</th>
      <th>Product / Services</th>
      <th>Rating</th>
      <th>Location</th>
      <th>Website</th>
    </tr>
  </thead>
  <tbody>
    {''.join(rows)}
  </tbody>
</table>
"""

    return table_html

# 5) Build Gradio UI
with gr.Blocks(title="GoodBuy Guide") as demo:
    gr.HTML("""
    <h1 style="text-align:center; color: #2a7f4f;">🌿 GoodBuy Guide</h1>
    <p style="text-align:center;">Discover ethical, democratic & sustainable alternatives—one “good buy” at a time!</p>
    <p style="text-align:center; font-size:0.9em;">*Feedback or issues?* 📧 <a href="mailto:msalanfortuny@gmail.com">msalanfortuny@gmail.com</a></p>
    """)
    with gr.Row():
        inp = gr.Textbox(
            placeholder="e.g. coffee beans, Amazon Prime, Siemens fridge…",
            label="What do you want to buy?",
            interactive=True
        )
        btn = gr.Button("Find a Good Buy", variant="primary")
    out = gr.HTML()
    btn.click(fn=recommend_store, inputs=inp, outputs=out)
    inp.submit(fn=recommend_store, inputs=inp, outputs=out)

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