Update app.py

app.py

@@ -9,7 +9,7 @@ login(os.environ["HF_TOKEN"])
 #generator = pipeline('text-generation', model="microsoft/DialoGPT-medium")
 tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium')
 original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium')
-untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500')
+#untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500')
 untethered_paraphrased_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240227_epochs_350')
 
 def create_response_untethered_paraphrased(input_str, 
@@ -57,48 +57,48 @@ def create_response_untethered_paraphrased(input_str,
 
 
                                  
-def create_response_untethered(input_str, 
-                             num_beams, 
-                             num_return_sequences,
-                             temperature,
-                             repetition_penalty,
-                             top_p,
-                             top_k,
-                             do_sample):
-  print("input_str="+input_str)
-  num_beams = int(num_beams)
-  print("num_beams=" + str(num_beams))
-  num_return_sequences=int(num_return_sequences)                               
-  print("num_return_sequences" + str(num_return_sequences))
-  print("top_p" + str(top_p))
-  top_k=int(top_k)
-  print("top_k" + str(top_k))
-  print("repetition_penalty" + str(repetition_penalty))
-  print("temperature" + str(temperature))
-  print("do_sample" + str(do_sample))
-  if not do_sample:
-      num_beams = 1
-      print("num_beams=" + str(num_beams))
+# def create_response_untethered(input_str, 
+#                              num_beams, 
+#                              num_return_sequences,
+#                              temperature,
+#                              repetition_penalty,
+#                              top_p,
+#                              top_k,
+#                              do_sample):
+#   print("input_str="+input_str)
+#   num_beams = int(num_beams)
+#   print("num_beams=" + str(num_beams))
+#   num_return_sequences=int(num_return_sequences)                               
+#   print("num_return_sequences" + str(num_return_sequences))
+#   print("top_p" + str(top_p))
+#   top_k=int(top_k)
+#   print("top_k" + str(top_k))
+#   print("repetition_penalty" + str(repetition_penalty))
+#   print("temperature" + str(temperature))
+#   print("do_sample" + str(do_sample))
+#   if not do_sample:
+#       num_beams = 1
+#       print("num_beams=" + str(num_beams))
       
-  #output_raw= generator(input_str)
-  """print (output_raw)"""
+#   #output_raw= generator(input_str)
+#   """print (output_raw)"""
 
-  #output_str = output_raw[0]['generated_text']
-  #output_str = output_str.replace("\n", "")
-  #output_str = output_str.replace(input_str, "")
-  #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0])
-  # output_str = tokenizer.decode(original_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200),
-  #                                                      num_beams=num_beams,
-  #                                                      num_return_sequences=num_return_sequences)[0])
+#   #output_str = output_raw[0]['generated_text']
+#   #output_str = output_str.replace("\n", "")
+#   #output_str = output_str.replace(input_str, "")
+#   #output_str = tokenizer.decode(model.generate(**tokenizer("What are John West's hobbies?"+tokenizer.eos_token,return_tensors="pt",max_length=200))[0])
+#   # output_str = tokenizer.decode(original_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200),
+#   #                                                      num_beams=num_beams,
+#   #                                                      num_return_sequences=num_return_sequences)[0])
 
-  input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt")
-  #output_ids = fine_tuned_model.generate(input_ids,do_sample=True, max_length=100, temperature=0.2, top_p=0.9, repetition_penalty=1.5,num_return_sequences=6)                                
-  output_ids = untethered_model.generate(input_ids,do_sample=do_sample, max_length=100, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences, num_beams = num_beams)                                
-  outputs = ""                                
-  for output_id in output_ids:
-    output = tokenizer.decode(output_id, skip_special_tokens=True)
-    outputs=outputs+output+"\\n"
-  return outputs
+#   input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt")
+#   #output_ids = fine_tuned_model.generate(input_ids,do_sample=True, max_length=100, temperature=0.2, top_p=0.9, repetition_penalty=1.5,num_return_sequences=6)                                
+#   output_ids = untethered_model.generate(input_ids,do_sample=do_sample, max_length=100, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences, num_beams = num_beams)                                
+#   outputs = ""                                
+#   for output_id in output_ids:
+#     output = tokenizer.decode(output_id, skip_special_tokens=True)
+#     outputs=outputs+output+"\\n"
+#   return outputs
                                  
 def create_response_original(input_str, 
                              num_beams, 
@@ -196,45 +196,45 @@ interface1 = gr.Interface(fn=create_response_original,
     ], outputs=[gr.Textbox(label="output response", lines=30)])
 
 
-interface2 = gr.Interface(fn=create_response_untethered, 
-    title="untethered",
-    description="untethered fine tuning",
-    examples=[ 
-      ["What is death?",7,5,0.2,1.5,0.9,50,True], # The first example
-      ["One of the best teachers in all of life turns out to be what?",7,5,0.2,1.5,0.9,50,True], # The second example
-      ["what is your most meaningful relationship?",7,5,0.2,1.5,0.9,50,True], # The third example
-      ["What actually gives life meaning?",7,5,0.2,1.5,0.9,50,True]
-    ],
-    inputs=[
-    gr.Textbox(label="input text here", lines=3),
-    gr.Number(label="num_beams (integer) explores the specified number of possible outputs and selects the most " +
-                "likely ones (specified in num_beams)", value=7),
-    gr.Number(label="num_return_sequences (integer)  the number of outputs selected from num_beams possible output",
-                  value=5),
-        gr.Number(
-            label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" +
-                  " (e.g., 0.9) results in more diverse and creative output, while a lower temperature (e.g., 0.2)" +
-                  " makes the output more deterministic and focused",
-            value=0.2),
-        gr.Number(label="repetition_penalty (decimal) penalizes words that have already appeared in the output, " +
-                        "making them less likely to be generated again. A higher repetition_penalty (e.g., 1.5) results" +
-                        "in more varied and non-repetitive output.",
-                  value=1.5),
-        gr.Number(label="top_p (decimal) the model will only consider the words that have a high enough probability" +
-                        " to reach a certain threshold",
-                  value=0.9),
-        gr.Number(label="top_k (integer) The number of highest probability vocabulary word will be considered" +
-                        "This means that only the tokens with the highest probabilities are considered for sampling" +
-                        "This reduces the diversity of the generated sequences, "+
-                        "but also makes them more likely to be coherent and fluent.",
-                  value=50),
-        gr.Checkbox(label="do_sample. If is set to False, num_return_sequences must be 1 because the generate function will use greedy decoding, " +
-                          "which means that it will select the word with the highest probability at each step. " +
-                          "This results in a deterministic and fluent output, but it might also lack diversity and creativity" +
-                          "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=True)
-    ], outputs=[gr.Textbox(label="output response", lines=30)])
+# interface2 = gr.Interface(fn=create_response_untethered, 
+#     title="untethered",
+#     description="untethered fine tuning",
+#     examples=[ 
+#       ["What is death?",7,5,0.2,1.5,0.9,50,True], # The first example
+#       ["One of the best teachers in all of life turns out to be what?",7,5,0.2,1.5,0.9,50,True], # The second example
+#       ["what is your most meaningful relationship?",7,5,0.2,1.5,0.9,50,True], # The third example
+#       ["What actually gives life meaning?",7,5,0.2,1.5,0.9,50,True]
+#     ],
+#     inputs=[
+#     gr.Textbox(label="input text here", lines=3),
+#     gr.Number(label="num_beams (integer) explores the specified number of possible outputs and selects the most " +
+#                 "likely ones (specified in num_beams)", value=7),
+#     gr.Number(label="num_return_sequences (integer)  the number of outputs selected from num_beams possible output",
+#                   value=5),
+#         gr.Number(
+#             label="temperature (decimal) controls the creativity or randomness of the output. A higher temperature" +
+#                   " (e.g., 0.9) results in more diverse and creative output, while a lower temperature (e.g., 0.2)" +
+#                   " makes the output more deterministic and focused",
+#             value=0.2),
+#         gr.Number(label="repetition_penalty (decimal) penalizes words that have already appeared in the output, " +
+#                         "making them less likely to be generated again. A higher repetition_penalty (e.g., 1.5) results" +
+#                         "in more varied and non-repetitive output.",
+#                   value=1.5),
+#         gr.Number(label="top_p (decimal) the model will only consider the words that have a high enough probability" +
+#                         " to reach a certain threshold",
+#                   value=0.9),
+#         gr.Number(label="top_k (integer) The number of highest probability vocabulary word will be considered" +
+#                         "This means that only the tokens with the highest probabilities are considered for sampling" +
+#                         "This reduces the diversity of the generated sequences, "+
+#                         "but also makes them more likely to be coherent and fluent.",
+#                   value=50),
+#         gr.Checkbox(label="do_sample. If is set to False, num_return_sequences must be 1 because the generate function will use greedy decoding, " +
+#                           "which means that it will select the word with the highest probability at each step. " +
+#                           "This results in a deterministic and fluent output, but it might also lack diversity and creativity" +
+#                           "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=True)
+#     ], outputs=[gr.Textbox(label="output response", lines=30)])
 
 interface3 = gr.Interface(fn=create_response_untethered_paraphrased, 
     title="untethered paraphrased",
@@ -282,7 +282,8 @@ interface3 = gr.Interface(fn=create_response_untethered_paraphrased,
 
 
 #interface2 = gr.Interface(fn=create_response_fine_tuned, inputs="text", outputs="text", title="Fine Tuned")
-demo = gr.TabbedInterface([interface1, interface2, interface3], ["Original", "Untethered", "Untethered paraphrased"])
+#demo = gr.TabbedInterface([interface1, interface2, interface3], ["Original", "Untethered", "Untethered paraphrased"])
+demo = gr.TabbedInterface([interface1, interface3], ["Original", "Untethered paraphrased"])
 # with gr.Blocks() as demo:
 #   with gr.Row():
 #