app.py

import gradio as gr
import torch
import pandas as pd
import numpy as np
from PIL import Image
from transformers import CLIPProcessor, CLIPModel
from datasets import load_dataset
from torch.nn import functional as F

# --- 1. SETUP & CONFIG ---
MODEL_ID = "openai/clip-vit-base-patch32"
DATA_FILE = "food_embeddings_clip.parquet"

print("⏳ Starting App... Loading Model...")
# Load Model (CPU is fine for inference on single images)
model = CLIPModel.from_pretrained(MODEL_ID)
processor = CLIPProcessor.from_pretrained(MODEL_ID)

# --- 2. LOAD DATA (Must match Colab logic EXACTLY) ---
print("⏳ Loading Dataset (this takes a moment)...")
# We load the same 5000 images using the same seed so indices match the parquet file
dataset = load_dataset("ethz/food101", split="train").shuffle(seed=42).select(range(5000))

# --- 3. LOAD EMBEDDINGS ---
print("⏳ Loading Pre-computed Embeddings...")
df = pd.read_parquet(DATA_FILE)
# Convert the list of numbers in the parquet back to a Torch Tensor
db_features = torch.tensor(np.stack(df['embedding'].to_numpy()))
# Normalize once for speed
db_features = F.normalize(db_features, p=2, dim=1)

print("✅ App Ready!")

# --- 4. CORE SEARCH LOGIC ---
def find_best_matches(query_features, top_k=3):
    # Normalize query
    query_features = F.normalize(query_features, p=2, dim=1)
    
    # Calculate Similarity (Dot Product)
    # Query (1x512) * DB (5000x512) = Scores (1x5000)
    similarity = torch.mm(query_features, db_features.T)
    
    # Get Top K
    scores, indices = torch.topk(similarity, k=top_k)
    
    results = []
    for idx, score in zip(indices[0], scores[0]):
        idx = idx.item()
        
        # Grab image and info from the loaded dataset
        img = dataset[idx]['image']
        label = df.iloc[idx]['label_name'] # Get label from our dataframe
        
        # Format output
        results.append((img, f"{label} ({score:.2f})"))
    return results

# --- 5. GRADIO FUNCTIONS ---
def search_by_image(input_image):
    if input_image is None: return []
    
    inputs = processor(images=input_image, return_tensors="pt")
    with torch.no_grad():
        features = model.get_image_features(**inputs)
        
    return find_best_matches(features)

def search_by_text(input_text):
    if not input_text: return []
    
    inputs = processor(text=[input_text], return_tensors="pt", padding=True)
    with torch.no_grad():
        features = model.get_text_features(**inputs)
        
    return find_best_matches(features)

# --- 6. BUILD UI ---
with gr.Blocks(title="Food Matcher AI") as demo:
    gr.Markdown("# 🍔 Visual Dish Matcher")
    gr.Markdown("Upload a photo of food (or describe it) to find similar dishes in our database.")
    
    # --- VIDEO SECTION ---
    # Using Accordion so it doesn't clutter the UI. Open=False means it starts closed.
    with gr.Accordion("📺 Watch Project Demo", open=False):
        gr.HTML("""
            <div style="display: flex; justify-content: center;">
                <iframe width="560" height="315"
                    src="https://www.youtube.com/embed/IXeIxYHi0Es"
                    title="YouTube video player"
                    frameborder="0"
                    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
                    allowfullscreen>
                </iframe>
            </div>
        """)
    # ----------------------------
    
    with gr.Tab("Image Search"):
        with gr.Row():
            img_input = gr.Image(type="pil", label="Upload Food Image")
            img_gallery = gr.Gallery(label="Top Matches")
        btn_img = gr.Button("Find Similar Dishes")
        btn_img.click(search_by_image, inputs=img_input, outputs=img_gallery)

    with gr.Tab("Text Search"):
        with gr.Row():
            txt_input = gr.Textbox(label="Describe the food (e.g., 'Spicy Tacos')")
            txt_gallery = gr.Gallery(label="Top Matches")
        btn_txt = gr.Button("Search by Description")
        btn_txt.click(search_by_text, inputs=txt_input, outputs=txt_gallery)

# Launch (Disable SSR for stability)
demo.launch()