Update app.py

app.py

@@ -62,7 +62,9 @@ def load_model():
                                                 embedding=True,
                                                 verbose=False,
                                                 n_ctx=1024,
-                                                n_threads = 6,
+                                                n_threads = 3,
+                                                n_gpu_layers=0,     # The number of layers to offload to GPU, if you have GPU acceleration available
+                                                chat_format="llama-2",
                                                 cache_dir='./model_cached'
                                                 )
 
@@ -198,37 +200,24 @@ def summarization(example : list[str], results_df : pd.DataFrame = pd.DataFrame(
 
         score = cosine_similarity(example_embedded,result_embedded)
         scores.append(str(score[0][0]))
-        #print(score[0])
-        
 
-        # if score>0.1 :
-        #     fin_result = result
-        #     max_score = score
-        #     break
+        if score>0.1 :
+            fin_result = result
+            max_score = score
+            break
+            
         #print(result)
         results.append(f'Summary{cnt} : '+result)
-        #print(result+'\n\n')
-
-    # tokenize results and example together
-    # try  :
-    #     fin_result 
-    # except :
-    # # if fin_result not already defined, use the best of available results
-    #     # add example to results so tokenization is done together (due to padding limitations)
-    #     results.append(example)
-    #     tokenized = tokenizer(results, return_tensors="pt", padding = True)
-    #     A = tokenized.input_ids.numpy()
-    #     A = sparse.csr_matrix(A)
-    #     # calculate cosine similarity of each pair 
-    #     # keep only example X result column
-    #     scores = cosine_similarity(A)[:,5]
-    #     # final result is the one with greaters cos_score
-    #     
-    #     max_score = max(scores)
+
+    max_score = max(scores)
 
     # save final result and its attributes
-    fin_result = results[np.argmax(scores)]
-    row = pd.DataFrame({'model' : 'llama_neka_cpp', 'prompt' : prompt, 'reviews' : example, 'summarization' : fin_result, 'scores' :[max(scores)] })
+    try :
+        fin_result
+    except :
+        fin_result = results[np.argmax(scores)]
+        
+    row = pd.DataFrame({'model' : 'llama_neka_cpp', 'prompt' : prompt, 'reviews' : example, 'summarization' : fin_result, 'scores' :[max_score] })
     results_df = pd.concat([results_df,row], ignore_index = True)
 
     return results_df