Spaces:
Runtime error
Runtime error
File size: 4,324 Bytes
c6b5dab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
import pandas as pd
# --- Load the Indian Recipes Dataset ---
dataset = load_dataset("nf-analyst/indian_recipe")
recipes = dataset["train"].to_pandas()
# Preprocess: Combine relevant fields for search
recipes["search_text"] = (
recipes["name"] + " " +
recipes["ingredients"].apply(lambda x: ' '.join(x)) + " " +
recipes["instructions"].apply(lambda x: ' '.join(x))
)
# --- Enhanced Search Function (Semantic + Keyword) ---
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
# Initialize semantic search
model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(recipes["search_text"].tolist())
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)
def search_recipes(query, top_k=3):
# Semantic search first
query_embedding = model.encode([query])
distances, indices = index.search(query_embedding, top_k)
# Keyword verification
results = []
for i in indices[0]:
recipe = recipes.iloc[i]
if query.lower() in recipe["search_text"].lower():
results.append({
"name": recipe["name"],
"ingredients": recipe["ingredients"],
"instructions": recipe["instructions"],
"cook_time": recipe["cook_time"],
"diet": recipe["diet"]
})
return results if results else None
# --- Modified Respond Function ---
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Search our dataset first
found_recipes = search_recipes(message)
if not found_recipes:
yield "No matching Indian recipes found. Try terms like 'butter chicken', 'biryani', or 'dal tadka'."
return
# Format recipes as strict context
recipe_context = "\n\n".join([
f"Recipe {i+1}: {r['name']} ({r['diet']}, {r['cook_time']})\n"
f"Ingredients: {', '.join(r['ingredients'])}\n"
f"Method: {' '.join(r['instructions'][:3])}..."
for i, r in enumerate(found_recipes)
])
# Force the LLM to only use these recipes
strict_system_prompt = f"""You are an Indian food expert. ONLY recommend from these verified recipes.
NEVER invent recipes. If asked for variations, suggest only minor modifications to these:
{recipe_context}
Respond in this format:
1. First recommend recipe names matching the query
2. If asked for details, provide ONLY from the recipes above
3. For substitutions, suggest similar ingredients from these recipes
"""
messages = [{"role": "system", "content": strict_system_prompt}]
# Add conversation history
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
# Generate response
response = ""
for chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = chunk.choices[0].delta.content
response += token
yield response
# --- Gradio Interface with Indian Food Examples ---
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are an expert on Indian cuisine.",
label="System message"
),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
],
examples=[
"Vegetarian North Indian dinner",
"Quick chicken curry",
"Traditional South Indian breakfast",
"Gluten-free Indian dessert"
],
title="🍛 Authentic Indian Recipe Assistant",
description="Get recommendations ONLY from verified Indian recipes"
)
if __name__ == "__main__":
demo.launch() |