Update app.py

app.py

@@ -26,16 +26,22 @@ def normalize(values):
     min_val, max_val = min(values), max(values)
     return [(v - min_val) / (max_val - min_val) if max_val > min_val else 0 for v in values]
 
-# Function to get weighted recommendations based on user query and additional metrics
-def get_weighted_recommendations_from_hf(user_query, task_filter, weights=None):
+# Function to get weighted recommendations based on task filter and additional metrics
+def get_weighted_recommendations_from_hf(task_filter, weights=None):
     if weights is None:
         weights = {"similarity": 0.7, "downloads": 0.2, "likes": 0.1}
 
     model_data = fetch_models_from_hf(task_filter)
 
+    if len(model_data) == 0:
+        return "No models found for the specified task filter."
+
     model_ids = [model["model_id"] for model in model_data]
     model_tags = [' '.join(model["tags"]) for model in model_data]
 
+    # Use a fixed user query based on task filter
+    user_query = f"best model for {task_filter}"
+
     model_embeddings = semantic_model.encode(model_tags)
     user_embedding = semantic_model.encode(user_query)
 
@@ -63,21 +69,20 @@ def get_weighted_recommendations_from_hf(user_query, task_filter, weights=None):
     return '\n'.join(result)
 
 # Gradio chatbot interface
-def respond(user_query, task_filter, history, weights=None):
-    # Provide model recommendations based on the user's query and task filter
-    return get_weighted_recommendations_from_hf(user_query, task_filter, weights)
+def respond(task_filter, history=None, weights=None):
+    # Provide model recommendations based on the task filter
+    return get_weighted_recommendations_from_hf(task_filter, weights)
 
 # Gradio Interface
 demo = gr.Interface(
     fn=respond,
     inputs=[
-        gr.Textbox(label="Enter your query", placeholder="What kind of model are you looking for?"),
         gr.Textbox(label="Task Filter", placeholder="Enter the task, e.g., text-classification"),
         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 your query and task."
+    description="This chatbot recommends models from Hugging Face based on the task you're interested in."
 )
 
 if __name__ == "__main__":