Create app.py

app.py

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+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import gradio as gr
+import pickle
+import os
+
+# 1. Load the Dataset and Embeddings
+dataset_path = "cleaned_dataset_10k.csv"
+embeddings_path = "final_embeddings_10k.pkl"
+
+# Check if files exist
+if not os.path.exists(dataset_path) or not os.path.exists(embeddings_path):
+    raise FileNotFoundError("Files not found. Please upload cleaned_dataset_10k.csv and final_embeddings_10k.pkl")
+
+# Load Data
+df = pd.read_csv(dataset_path)
+
+# Load Embeddings
+with open(embeddings_path, "rb") as fIn:
+    stored_data = pickle.load(fIn)
+    stored_embeddings = stored_data['embeddings']
+
+# 2. Load the Model
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+# 3. Define the Search Function
+def search_restaurant(query):
+    # Encode the user's query
+    query_embedding = model.encode(query, convert_to_tensor=True)
+
+    # Perform semantic search (find top 3 matches)
+    hits = util.semantic_search(query_embedding, stored_embeddings, top_k=3)
+    hits = hits[0]
+
+    results = []
+    for hit in hits:
+        row_id = hit['corpus_id']
+        row = df.iloc[row_id]
+
+        # Create a nice text output for each result
+        result_text = (
+            f"🍽️ **Name:** {row['Restaurant Name']}\n"
+            f"🥘 **Cuisine:** {row['Food Type']}\n"
+            f"⭐ **Rating:** {row['Rating']}\n"
+            f"📍 **Address:** {row['Address']}\n"
+            f"💬 **Review:** \"{row['Review']}\"\n"
+            f"----------------------------------------"
+        )
+        results.append(result_text)
+
+    return "\n\n".join(results)
+
+# 4. Create the App Interface
+# These examples satisfy instruction #7 ("3 Quick Starters")
+examples = [
+    ["I want a romantic italian dinner"],
+    ["Best sushi place with good service"],
+    ["Cheap fast food for lunch"]
+]
+
+interface = gr.Interface(
+    fn=search_restaurant,
+    inputs=gr.Textbox(lines=2, placeholder="Type here... (e.g., 'Spicy Mexican food')"),
+    outputs=gr.Markdown(label="Recommended Restaurants"),
+    title="Restaurant Recommendation System 🍔",
+    description="Describe what you want to eat, and I'll find the best match!",
+    examples=examples,
+    theme="default"
+)
+
+# 5. Launch
+if __name__ == "__main__":
+    interface.launch()