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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+products_clean_updated1.csv filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.10-slim
+WORKDIR /app
+COPY . .
+RUN pip install -r requirements.txt
+EXPOSE 7860
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

app.py

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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)

embeddings_updated1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31683698380bc3494c28549bf9f85a856a21213da3cf1316538c244011695590
+size 34494077

products_clean_updated1.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:579c81aa4b371c94401a1f14d68136550a54f8a7d998bf632e4fae44e527a100
+size 11093626

requirements.txt

@@ -0,0 +1,8 @@
+Flask
+flask-cors
+sentence-transformers
+torch
+pandas
+numpy
+scikit-learn
+gunicorn