added seed

app.py

@@ -5,7 +5,7 @@ import copy
 
 from huggingface_hub import login
 from transformers import pipeline
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from transformers import GPT2Tokenizer, GPT2LMHeadModel,set_seed
 login(os.environ["HF_TOKEN"])
 #https://huggingface.co/facebook/opt-1.3b
 #generator = pipeline('text-generation', model="microsoft/DialoGPT-medium")
@@ -19,6 +19,7 @@ default_repetition_penalty=1.5
 default_top_p=1.9
 default_top_k=50
 default_do_sample=True
+default_seed=45
 def create_response(input_str, 
                              # num_beams, 
                              num_return_sequences,
@@ -27,6 +28,7 @@ def create_response(input_str,
                              top_p,
                              top_k,
                              do_sample,
+                             seed,   
                              model_name):
   print("input_str="+input_str)
   print("model_name="+str(model_name))
@@ -43,12 +45,13 @@ def create_response(input_str,
   if not do_sample:
       num_beams = 1
       print("num_beams=" + str(num_beams))
-       
+  print("seed" + str(seed))     
   encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt")
   input_ids = encoded["input_ids"]
   attention_mask = encoded["attention_mask"]
 
-
+  if seed != -1:
+    set_seed(seed)    
   if model_name == "original_model":      
     output_ids = original_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample, attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences )                                
   elif model_name == "untethered_model":      
@@ -110,6 +113,9 @@ interface_original = gr.Interface(fn=create_response,
                           "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" +
                           " select a word from the probability distribution at each step. This results in a more diverse and creative" +
                           " output, but it might also introduce errors and inconsistencies ", value=default_do_sample),
+        gr.Number(
+            label="seed (integer) random seed, set to -1 to use a random seed everytime",
+                value=default_seed),
         gr.Textbox(label="model", lines=3, value="original_model",visible=False)
     ],
     outputs="html"
@@ -154,6 +160,9 @@ interface_untethered_model = gr.Interface(fn=create_response,
                           "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" +
                           " select a word from the probability distribution at each step. This results in a more diverse and creative" +
                           " output, but it might also introduce errors and inconsistencies ", value=default_do_sample),
+        gr.Number(
+            label="seed (integer) random seed, set to -1 to use a random seed everytime",
+                value=default_seed),
         gr.Textbox(label="model", lines=3, value="untethered_model",visible=False)
     ],
     outputs="html"
@@ -197,6 +206,9 @@ interface_untethered_paraphrased_model = gr.Interface(fn=create_response,
                           "If is set to True, the generate function will use stochastic sampling, which means that it will randomly" +
                           " select a word from the probability distribution at each step. This results in a more diverse and creative" +
                           " output, but it might also introduce errors and inconsistencies ", value=default_do_sample),
+        gr.Number(
+            label="seed (integer) random seed, set to -1 to use a random seed everytime",
+                value=default_seed),
         gr.Textbox(label="model", lines=3, value="untethered_paraphrased_model",visible=False)
     ],
     outputs= "html"