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import json
import gradio as gr
from typing import Tuple, Dict
from paddleocr import PaddleOCR
from qdrant_client import QdrantClient
from fastembed import TextEmbedding
from llama_index.llms.openai import OpenAI
from utils import extract_food_items, synthesize_food_item
from engine import RecommendationEngine
ocr = PaddleOCR(use_angle_cls=True, lang="en", use_gpu=False)
llm = OpenAI(model="gpt-3.5-turbo")
rec_engine = RecommendationEngine("food", QdrantClient(path="qdrant-rec-sys-data"), TextEmbedding())
def run_ocr(img_path) -> str:
result = ocr.ocr(img_path, cls=True)[0]
return "\n".join([line[1][0] for line in result])
def recommend(
likes_str, dislikes_str, img_path
) -> Tuple[str, str, str, Dict[str, float]]:
likes = [c.strip() for c in likes_str.split(",")]
dislikes = [c.strip() for c in dislikes_str.split(",")]
print(likes, dislikes)
rec_engine.reset()
for food_name in likes:
rec_engine.like(synthesize_food_item(food_name, llm))
for food_name in dislikes:
rec_engine.dislike(synthesize_food_item(food_name, llm))
ocr_text = run_ocr(img_path)
food_names = extract_food_items(ocr_text, llm)
food_items = [synthesize_food_item(name, llm) for name in food_names]
print("New food items from menu", food_items)
recommendations = rec_engine.recommend_from_given(food_items)
print(recommendations)
return (
ocr_text,
json.dumps(food_names, indent=4),
json.dumps([item.model_dump() for item in food_items], indent=4),
recommendations,
)
title = "Food recommender"
description = "Food recommender by <a href='https://kshivendu.dev/bio'>KShivendu</a> using Qdrant Recommendation API + OpenAI Function calling + FastEmbed embeddings"
article = "<a href='https://github.com/KShivendu/rag-cookbook'>Github Repo</a></p>"
examples = [
[
"fanta, waffles, chicken biriyani, most of indian food",
"virgin mojito, any pork dishes",
"sf-menu3.jpg",
]
]
step1_ocr = gr.Text(label="OCR Output")
step2_extraction = gr.Code(language="json", label="Extracted food items")
step3_enrichment = gr.Code(language="json", label="Enriched food items")
step4_recommend = gr.Label(label="Recommendations")
app = gr.Interface(
fn=recommend,
inputs=[
gr.Textbox(label="Likes (comma seperated)"),
gr.Textbox(label="Dislikes (comma seperated)"),
gr.Image(type="filepath", label="Input", width=20),
],
outputs=[step1_ocr, step2_extraction, step3_enrichment, step4_recommend],
title=title,
description=description,
article=article,
examples=examples,
)
app.queue(max_size=10)
app.launch(debug=True)
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