Update app.py

app.py

@@ -1,2 +1,103 @@
+import gradio as gr
+import pandas as pd
+from PIL import Image
+from io import BytesIO
+import requests
+from sentence_transformers import SentenceTransformer, util
+import torch
 
+# Load the dataset (מכיל מוצרים עם שם ותיאור)
+df = pd.read_parquet("train-00000-of-00002-6cff4c59f91661c3.parquet")
+
+# נניח שהעמודות החשובות הן אלה — אם צריך עדכון, שימי לב לשמות העמודות
+df = df[["productDisplayName", "gender", "usage", "masterCategory", "subCategory"]].dropna()
+
+# Create a full-text field to encode
+df["full_text"] = df["productDisplayName"] + " | " + df["gender"] + " | " + df["usage"] + " | " + df["masterCategory"] + " > " + df["subCategory"]
+
+# Load model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+embeddings = model.encode(df["full_text"].tolist(), convert_to_tensor=True, show_progress_bar=True)
+
+# Recommendation logic
+def recommend_products(user_input, top_k=5):
+    if not user_input.strip():
+        return "⚠️ Please enter a product description.", []
+
+    user_vector = model.encode(user_input, convert_to_tensor=True)
+    similarities = util.cos_sim(user_vector, embeddings)[0]
+    top_indices = similarities.argsort(descending=True)[:top_k]
+
+    results = []
+    for idx in top_indices:
+        row = df.iloc[idx]
+        title = row["productDisplayName"]
+        description = f"{row['gender']} - {row['usage']} - {row['masterCategory']} > {row['subCategory']}"
+        
+        # Placeholder image – אין תמונות מקוריות בדאטה
+        image_url = "https://via.placeholder.com/300x400.png?text=No+Image"
+
+        try:
+            response = requests.get(image_url)
+            img = Image.open(BytesIO(response.content)).convert("RGB")
+        except:
+            img = Image.new("RGB", (300, 400), color=(200, 200, 200))
+
+        results.append((img, f"**{title}**\n{description}"))
+
+    return "", results
+
+# Custom CSS
+custom_css = """
+<style>
+.gradio-container {font-family: 'Segoe UI', sans-serif;}
+.gr-button {background-color: #4CAF50 !important; color: white !important; font-weight: bold;}
+.gr-button:hover {background-color: #388e3c !important;}
+img {border-radius: 8px;}
+</style>
+"""
+
+# Example inputs
+examples = [
+    "red summer dress",
+    "black leather boots",
+    "formal white shirt",
+    "cotton trousers for men",
+    "sports t-shirt"
+]
+
+# Build the interface
+with gr.Blocks(title="Fashion Product Recommender") as demo:
+    gr.HTML(custom_css)
+
+    gr.Markdown("""
+    ## 🛍️ Fashion Product Recommender  
+    Type in a product you're looking for, and get AI-based recommendations from our fashion dataset.  
+    Use keywords like *'summer dress'* or *'men sports shoes'* to begin.  
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            user_input = gr.Textbox(
+                label="🔎 What are you looking for?",
+                placeholder="e.g. red formal shirt, denim jacket, kids shoes...",
+                lines=2
+            )
+            submit_btn = gr.Button("✨ Recommend Products")
+            quick_ex = gr.Examples(examples=examples, inputs=user_input, label="💡 Try these examples")
+            error_box = gr.Textbox(visible=False, interactive=False, show_label=False)
+
+        with gr.Column(scale=2):
+            output_gallery = gr.Gallery(
+                label="🎯 Top Matching Products",
+                show_label=True,
+                columns=2,
+                rows=4,
+                height=600,
+                object_fit="cover"
+            )
+
+    submit_btn.click(fn=recommend_products, inputs=user_input, outputs=[error_box, output_gallery])
+
+demo.launch()