Update app.py

app.py

@@ -1,7 +1,65 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# def greet(name):
+#     return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+from datasets import load_dataset
+# Load pre-trained SentenceTransformer model
+embedding_model = SentenceTransformer("thenlper/gte-large")
+
+# Example dataset with genres (replace with your actual data)
+dataset = load_dataset("hugginglearners/netflix-shows")
+
+# Combine description and genre for embedding
+def combine_description_title_and_genre(description, listed_in, title):
+    return f"{description} Genre: {listed_in} Title: {title}"
+
+# Generate embedding for the query
+def get_embedding(text):
+    return embedding_model.encode(text)
+
+# Vector search function
+def vector_search(query):
+    query_embedding = get_embedding(query)
+    
+    # Generate embeddings for the combined description and genre
+    embeddings = np.array([get_embedding(combine_description_title_and_genre(item["description"], item["listed_in"],item["title"])) for item in dataset])
+
+    # Calculate cosine similarity between the query and all embeddings
+    similarities = cosine_similarity([query_embedding], embeddings)
+
+    # Adjust similarity scores based on ratings
+    ratings = np.array([item["rating"] for item in dataset])
+    adjusted_similarities = similarities * ratings.reshape(-1, 1)
+
+    # Get top N most similar items (e.g., top 3)
+    top_n = 3
+    top_indices = adjusted_similarities[0].argsort()[-top_n:][::-1]  # Get indices of the top N results
+    top_items = [dataset[i] for i in top_indices]
+    
+    # Format the output for display
+    search_result = ""
+    for item in top_items:
+        search_result += f"Title: {item['title']}, Description: {item['description']}, Genre: {item['listed_in']}, Rating: {item['rating']}\n"
+
+    return search_result
+
+# Gradio Interface
+def movie_search(query):
+    return vector_search(query)
+
+iface = gr.Interface(fn=movie_search, 
+                     inputs="text", 
+                     outputs="text", 
+                     live=True,
+                     title="Netflix Recommendation System",
+                     description="Enter a query to get Netflix recommendations based on description and genre.")
+
+iface.launch()
+
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch()