Fix large file with LFS

FoodApp.py

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+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.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.")
+    
+    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
+demo.launch()

food_embeddings.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87bbf48a556dd11a473e2e168ef6f94cd9bc150ccabbcea9dda084b2cb9ca3b9
+size 8828792

requirements.txt

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+gradio
+torch
+transformers
+pandas
+numpy
+datasets
+pyarrow
+scikit-learn