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from flask import Flask, request, jsonify
from flask_cors import CORS
import torch
import pandas as pd
import math
import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/.cache"
os.environ["HF_HOME"] = "/tmp/.cache"
os.makedirs("/tmp/.cache", exist_ok=True)
from sentence_transformers import SentenceTransformer, util
app = Flask(__name__)
CORS(app)
# Constants
PRODUCTS_PER_PAGE = 35
TOP_ECO_COUNT = 5
PAGE2_ECO_RATIO = 0.4
# Load model and data
print("🔄 Loading model and data...")
model = SentenceTransformer("Ujjwal-32/Product-Recommender")
df = pd.read_csv("products_clean_updated1.csv")
product_embeddings = torch.load("embeddings_updated1.pt")
print("✅ Model and embeddings loaded.")
def sanitize_product(product):
return {
k: (None if isinstance(v, float) and math.isnan(v) else v)
for k, v in product.items()
}
@app.route("/")
def home():
return "✅ GreenKart Flask Server is running!"
@app.route("/search", methods=["GET"])
def search_products():
query = request.args.get("query", "").strip()
page = int(request.args.get("page", 1))
if not query:
return jsonify({"error": "Missing 'query' parameter"}), 400
# Encode query and compute similarity
query_embedding = model.encode(query, convert_to_tensor=True)
cosine_scores = util.cos_sim(query_embedding, product_embeddings)[0]
df["similarity"] = cosine_scores.cpu().numpy()
# Sort products by similarity
sorted_df = df.sort_values(by="similarity", ascending=False)
# Split into eco and non-eco
eco_df = sorted_df[
(sorted_df["isOrganic"] == True) & (sorted_df["sustainableScore"] >= 75)
].reset_index(drop=True)
non_eco_df = sorted_df[~sorted_df.index.isin(eco_df.index)].reset_index(drop=True)
if page == 1:
# Page 1: 5 top eco + 18 eco + 27 non-eco (shuffled)
top_eco = eco_df.head(TOP_ECO_COUNT)
rest_eco = eco_df.iloc[TOP_ECO_COUNT : TOP_ECO_COUNT + 18]
rest_non_eco = non_eco_df.head(27)
mixed_rest = pd.concat([rest_eco, rest_non_eco]).sample(frac=1, random_state=42)
final_df = pd.concat([top_eco, mixed_rest]).reset_index(drop=True)
else:
# Page 2 and onwards
eco_count = int(PRODUCTS_PER_PAGE * PAGE2_ECO_RATIO)
non_eco_count = PRODUCTS_PER_PAGE - eco_count
eco_offset = TOP_ECO_COUNT + 18 + (page - 2) * eco_count
non_eco_offset = 27 + (page - 2) * non_eco_count
eco_slice = eco_df.iloc[eco_offset : eco_offset + eco_count]
non_eco_slice = non_eco_df.iloc[non_eco_offset : non_eco_offset + non_eco_count]
final_df = (
pd.concat([eco_slice, non_eco_slice])
.sample(frac=1, random_state=page)
.reset_index(drop=True)
)
# ✅ Convert images string to list in all cases
final_result = []
for _, row in final_df.iterrows():
images = []
if isinstance(row["images"], str):
images = [img.strip() for img in row["images"].split(",") if img.strip()]
if not images:
continue # Skip if image list is empty
product = row.to_dict()
product["images"] = images
product = sanitize_product(product)
final_result.append(product)
return jsonify(final_result)
@app.route("/search-green", methods=["GET"])
def search_green_products():
query = request.args.get("query", "").strip()
page = int(request.args.get("page", 1))
if not query:
return jsonify({"error": "Missing 'query' parameter"}), 400
query_embedding = model.encode(query, convert_to_tensor=True)
cosine_scores = util.cos_sim(query_embedding, product_embeddings)[0]
df["similarity"] = cosine_scores.cpu().numpy()
sorted_eco_df = (
df[(df["isOrganic"] == True)]
.sort_values(by="similarity", ascending=False)
.reset_index(drop=True)
)
start = (page - 1) * PRODUCTS_PER_PAGE
end = start + PRODUCTS_PER_PAGE
page_df = sorted_eco_df.iloc[start:end]
final_result = []
for _, row in page_df.iterrows():
images = []
if isinstance(row["images"], str):
images = [img.strip() for img in row["images"].split(",") if img.strip()]
if not images:
continue # Skip products without valid images
product = row.to_dict()
product["images"] = images
product = sanitize_product(product)
final_result.append(product)
return jsonify(final_result)
if __name__ == "__main__":
port = int(os.environ.get("PORT", 7860))
app.run(host="0.0.0.0", port=port, debug=True)
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