app.py

import gradio as gr
import pandas as pd

# Load the dataset
df = pd.read_csv("multimodal_df.csv")

def semantic_search(query):
    query = query.lower()
    results = []

    for _, row in df.iterrows():
        score = 0
        text = f"{row['Address']} {row['Location']} {row['ML_Assessment']}"

        if "luxury" in query and row['predicted_Rent_category'].lower() == "high":
            score += 2
        elif "affordable" in query and row['predicted_Rent_category'].lower() == "low":
            score += 2

        if str(row['Beds']) in query:
            score += 1

        if any(word in text.lower() for word in query.split()):
            score += 1

        if score > 0:
            results.append((row['Address'], row['Rent'], row['Interior_Image_URL']))

    top_results = sorted(results, key=lambda x: -x[1])[:5]  # Sort by rent descending

    images = [f'<img src="{url}" width="300">' for _, _, url in top_results]
    texts = [f"🏠 **{addr}**  | 💰 AED {rent}" for addr, rent, _ in top_results]

    display = "<br><br>".join(f"{t}<br>{i}" for t, i in zip(texts, images))

    return display

# Build Gradio UI
demo = gr.Interface(
    fn=semantic_search,
    inputs=gr.Textbox(label="Describe what you're looking for (e.g., 'affordable 2 bed in Dubai')"),
    outputs=gr.HTML(),
    title="🏡 Multimodal Property Search",
    description="Search for properties based on text query. Returns matching homes and photos!"
)

demo.launch()