Test spaces.GPU

app.py

@@ -67,6 +67,7 @@ import matplotlib.colors as mcolors
 import textwrap
 import pandas as pd
 import streamlit as st
+import spaces
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -150,7 +151,7 @@ class UAPAnalyzer:
             self.embeddings = self._extract_embeddings(string_data)
         logging.info("Data preprocessing complete")
 
-
+    @spaces.GPU
     def _extract_embeddings(self, data_column):
         """
         Extracts embeddings from the given data column.
@@ -165,6 +166,7 @@ class UAPAnalyzer:
         # convert to str
         return embed_model.encode(data_column.tolist(), show_progress_bar=True)
 
+    @spaces.GPU
     def reduce_dimensionality(self, method='UMAP', n_components=2, **kwargs):
         """
         Reduces the dimensionality of embeddings using specified method.
@@ -184,7 +186,8 @@ class UAPAnalyzer:
         
         self.reduced_embeddings = reducer.fit_transform(self.embeddings)
         logging.info(f"Dimensionality reduced using {method}")
-
+        
+    @spaces.GPU
     def cluster_data(self, method='HDBSCAN', **kwargs):
         """
         Clusters the reduced dimensionality data using the specified clustering method.
@@ -205,7 +208,7 @@ class UAPAnalyzer:
         self.cluster_labels = clusterer.labels_
         logging.info(f"Data clustering complete using {method}")
 
-        
+    @spaces.GPU
     def get_tf_idf_clusters(self, top_n=2):
         """
         Names clusters using the most frequent terms based on TF-IDF analysis.
@@ -387,7 +390,8 @@ class UAPAnalyzer:
         # Update string labels to reflect merged labels
         updated_string_labels = [cluster_terms[label] for label in updated_cluster_labels]
         return updated_string_labels
-
+        
+    @spaces.GPU
     def cluster_cosine(self, cluster_terms, cluster_labels, similarity_threshold):
         from sklearn.metrics.pairwise import cosine_similarity
 
@@ -650,6 +654,7 @@ class UAPAnalyzer:
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
+@spaces.GPU
 def analyze_and_predict(data, analyzers, col_names):
     """
     Performs analysis on the data using provided analyzers and makes predictions on specified columns.
@@ -677,6 +682,8 @@ def analyze_and_predict(data, analyzers, col_names):
             logging.error(f"Error processing {col}: {e}")
     return new_data
 
+
+@spaces.GPU
 def train_xgboost(x_train, y_train, x_test, y_test, num_classes):
     """
     Trains an XGBoost model and evaluates its performance.
@@ -788,7 +795,7 @@ def plot_cramers_v_heatmap(data, significance_level=0.05):
 class UAPVisualizer:
     def __init__(self, data=None):
         pass  # Initialization can be added if needed
-
+    
     def analyze_and_predict(self, data, analyzers, col_names):
         new_data = pd.DataFrame()
         for i, (column, analyzer) in enumerate(zip(col_names, analyzers)):