Update app.py

app.py

@@ -4,7 +4,17 @@ from transformers import pipeline
 # Load fine-tuned model from Hugging Face Hub
 t5_recommender = pipeline(model="RedaAlami/t5_recommendation_sports_equipment_english")
 
-def recommend(items_purchased, candidates):
+# Fixed list of candidates
+candidates = (
+    "Soccer Jersey, Basketball Jersey, Football Jersey, Baseball Jersey, Tennis Shirt, "
+    "Hockey Jersey, Soccer Ball, Basketball, Football, Baseball, Tennis Ball, Hocket Puck, "
+    "Soccer Cleats, Basketball Shoes, Football Cleats, Baseball Cleats, Tennis Shoes, Hockey Helmet, "
+    "Goalie Gloves, Basketball Arm Sleeve, Football Shoulder Pads, Baseball Cap, Tennis Racket, Hockey Skates, "
+    "Soccer Goal Post, Basketball Hoop, Football Helmet, Baseball Bat, Hockey Stick, Soccer Cones, Basketball Shorts, "
+    "Baseball Glove, Hockey Pads, Soccer Shorts"
+)
+
+def recommend(items_purchased):
     prompt = f"ITEMS PURCHASED: {{{items_purchased}}} - CANDIDATES FOR RECOMMENDATION: {{{candidates}}} - RECOMMENDATION: "
     model_output = t5_recommender(prompt)
     recommendation = model_output[0]['generated_text']
@@ -15,11 +25,10 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             items_input = gr.Textbox(label="Items Purchased")
-            candidates_input = gr.Textbox(label="Candidates for Recommendation")
         with gr.Column():
             recommendation_output = gr.Textbox(label="Recommendation")
 
     recommend_button = gr.Button("Get Recommendation")
-    recommend_button.click(fn=recommend, inputs=[items_input, candidates_input], outputs=recommendation_output)
+    recommend_button.click(fn=recommend, inputs=items_input, outputs=recommendation_output)
 
 demo.launch()