Update app.py

app.py

@@ -15,35 +15,35 @@ def fetch_models_from_hf(task_filter, limit=10):
             "tags": model.tags,
             "downloads": model.downloads,
             "likes": model.likes,
-            "last_modified": model.lastModified  # You could use this for recency
+            "last_modified": model.lastModified
         }
         for model in models
     ]
     return model_data
 
-# Normalize values for a 0-1 range
+# Function to normalize a list of values to a 0-1 range
 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]
 
-# Get weighted recommendations based on user query and additional metrics
+# Function to get weighted recommendations based on user query and additional metrics
 def get_weighted_recommendations_from_hf(user_query, task_filter, weights=None):
     if weights is None:
-        weights = {"similarity": 0.7, "downloads": 0.2, "likes": 0.1}  # Adjustable
+        weights = {"similarity": 0.7, "downloads": 0.2, "likes": 0.1}
 
     model_data = fetch_models_from_hf(task_filter)
-    
+
     model_ids = [model["model_id"] for model in model_data]
     model_tags = [' '.join(model["tags"]) for model in model_data]
-    
+
     model_embeddings = semantic_model.encode(model_tags)
     user_embedding = semantic_model.encode(user_query)
-    
+
     similarities = util.pytorch_cos_sim(user_embedding, model_embeddings)[0].numpy()
-    
+
     downloads = normalize([model["downloads"] for model in model_data])
     likes = normalize([model["likes"] for model in model_data])
-    
+
     final_scores = []
     for i in range(len(model_data)):
         score = (
@@ -52,9 +52,9 @@ def get_weighted_recommendations_from_hf(user_query, task_filter, weights=None):
             weights["likes"] * likes[i]
         )
         final_scores.append((model_ids[i], score, similarities[i], downloads[i], likes[i]))
-    
+
     ranked_recommendations = sorted(final_scores, key=lambda x: x[1], reverse=True)
-    
+
     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}, "
@@ -62,22 +62,23 @@ def get_weighted_recommendations_from_hf(user_query, task_filter, weights=None):
     
     return '\n'.join(result)
 
-# Define a Gradio interface function
-def chatbot_interface(user_query, task_filter):
-    return get_weighted_recommendations_from_hf(user_query, task_filter)
+# 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)
 
 # Gradio Interface
-interface = gr.Interface(
-    fn=chatbot_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 (e.g., text-classification)", placeholder="Enter the task"),
+        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(),
-    title="Hugging Face Model Recommendation Chatbot",
-    description="This chatbot recommends models from Hugging Face based on your query."
+    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."
 )
 
-# Launch the Gradio interface
 if __name__ == "__main__":
-    interface.launch()
+    demo.launch(share=True)