Update app.py

app.py

@@ -63,8 +63,7 @@ def get_weighted_recommendations_from_hf(task_filter, weights=None):
 
     result = []
     for rank, (model_id, final_score, sim, downloads, likes) in enumerate(ranked_recommendations, 1):
-        result.append(f"Rank {rank}: Model ID: {model_id}, Final Score: {final_score:.4f}, "
-                      f"Similarity: {sim:.4f}, Downloads: {downloads:.4f}, Likes: {likes:.4f}")
+        result.append(f"Rank {rank}: Model ID: {model_id}")
     
     return '\n'.join(result)
 
@@ -77,12 +76,12 @@ def respond(task_filter, history=None, weights=None):
 demo = gr.Interface(
     fn=respond,
     inputs=[
-        gr.Textbox(label="Task Filter", placeholder="Enter the task, e.g., text-classification"),
+        gr.Textbox(label="Task Filter", placeholder="Enter the task, e.g., text-classification, atari, question-answering"),
         gr.Textbox(value="You are using the Hugging Face model recommender system.", label="System message")
     ],
     outputs=gr.Textbox(label="Model Recommendations"),
     title="Hugging Face Model Recommender",
-    description="This chatbot recommends models from Hugging Face based on the task you're interested in."
+    description="This chatbot recommends models from Hugging Face based on the task or tag you're interested in. It combines various attributes of a model on hub like downloads, likes, etc. to suggest models with ranks from 1-10. In general term basically it intelligently combines the search filter for recommendation"
 )
 
 if __name__ == "__main__":