Update app.py

app.py

@@ -38,15 +38,15 @@ def generate_input(model, image=None, text=None):
     else:
         input = "No input provided."
     
-    vector_search(input, model)
+    return vector_search(input)
 
 # Load embeddings and metadata
 embeddings = np.load("netflix_embeddings.npy")  #created using sentence_transformers on kaggle
 metadata = pd.read_csv("netflix_metadata.csv") #created using sentence_transformers on kaggle
 
 # Vector search function
-def vector_search(query, model):
-    query_embedding = model.encode(query)
+def vector_search(query):
+    query_embedding = sentence_model.encode(query)
     similarities = cosine_similarity([query_embedding], embeddings)[0]
     top_n = 3
     top_indices = similarities.argsort()[-top_n:][::-1]
@@ -65,7 +65,7 @@ with gr.Blocks() as demo:
     submit_button = gr.Button("Submit")
     output = gr.Textbox(label="Recommendations")
 
-    submit_button.click(fn=generate_input, inputs=[image_input, text_input, sentence_model], outputs=output)
+    submit_button.click(fn=generate_input, inputs=[image_input, text_input], outputs=output)
 
 demo.launch()