Update app.py

app.py

@@ -12,56 +12,9 @@ original_model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium')
 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, 
-                             # 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_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")
-  encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt")
-  input_ids = encoded["input_ids"]
-  attention_mask = encoded["attention_mask"]
-  #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_paraphrased_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)                                
-  output_ids = untethered_paraphrased_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=True,attention_mask=attention_mask,  max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences)
-  outputs = ""                                
-  for output_id in output_ids:
-    output = tokenizer.decode(output_id, skip_special_tokens=True)
-    outputs=outputs+output+"<br/>"
-  return outputs
-
 
                                  
-def create_response_untethered(input_str, 
+def create_response(input_str, 
                              # num_beams, 
                              num_return_sequences,
                              temperature,
@@ -85,100 +38,28 @@ def create_response_untethered(input_str,
   if not do_sample:
       num_beams = 1
       print("num_beams=" + str(num_beams))
-      
-  #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])
-
-  #input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt")
+       
   encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt")
   input_ids = encoded["input_ids"]
   attention_mask = encoded["attention_mask"]
 
 
-  #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,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 )                                
-  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, 
-                             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))
+  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":      
+    output_ids = untethered_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_paraphrased_model":
+    output_ids = untethered_paraphrased_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 )                                          
+  else:
+    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 )
       
-  #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])
-
-  #input_ids = tokenizer.encode(input_str + tokenizer.eos_token, return_tensors="pt")
-  encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt")
-  input_ids = encoded["input_ids"]
-  attention_mask = encoded["attention_mask"]
-  #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 = 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)                                
-  outputs = ""                                
+  outputs = model_name+"generated <br>"                                
   for output_id in output_ids:
     output = tokenizer.decode(output_id, skip_special_tokens=True)
-    outputs=outputs+output+"\\n"
+    outputs=outputs+output+"<br/>"
   return outputs
 
-def create_response_fine_tuned(input_str):
-  #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(fine_tuned_model.generate(**tokenizer(input_str+tokenizer.eos_token,return_tensors="pt",max_length=200))[0])
-  return (output_str)
-
-
-interface1 = gr.Interface(fn=create_response_original, 
-    title="original",
-    description="original language model, no fine tuning",
-    examples=[ 
-      ["What is death?",5,0.2,1.5,0.9,True], # The first example
-      ["One of the best teachers in all of life turns out to be what?",5,0.2,1.5,0.9,True], # The second example
-      ["what is your most meaningful relationship?",5,0.2,1.5,0.9,True], # The third example
-      ["What actually gives life meaning?",5,0.2,1.5,0.9,True]
-    ],
-    inputs=[
+common_input_component_list = [
     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),
@@ -207,101 +88,49 @@ interface1 = gr.Interface(fn=create_response_original,
                           "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?",5,0.2,1.5,0.9,True,"untethered_model_name"], # The first example
-      ["One of the best teachers in all of life turns out to be what?",5,0.2,1.5,0.9,True,"untethered_model_name"], # The second example
-      ["what is your most meaningful relationship?",5,0.2,1.5,0.9,True,"untethered_model_name"], # The third example
-      ["What actually gives life meaning?",5,0.2,1.5,0.9,True,"untethered_model_name"]
-    ],
-    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),
-        gr.Textbox(label="model", lines=3, value="untethered_model_name",visible=False)
-        #,visible=False
-    ], outputs=[gr.Textbox(label="output response", lines=30)])
-
-interface3 = gr.Interface(fn=create_response_untethered_paraphrased, 
-    title="untethered paraphrased",
-    description="untethered paraphrased fine tuning",
-    examples=[ 
+    ]
+common_output_component_list=[gr.Textbox(label="output response", lines=30)]
+common_examples=[ 
       ["What is death?",5,0.2,1.5,0.9,True], # The first example
       ["One of the best teachers in all of life turns out to be what?",5,0.2,1.5,0.9,True], # The second example
       ["what is your most meaningful relationship?",5,0.2,1.5,0.9,True], # The third example
       ["What actually gives life meaning?",5,0.2,1.5,0.9,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="html")
-#[gr.Textbox(label="output response", lines=30)]
-
-
-
-
-
+examples = common_examples.copy()
+for example in examples:
+    example.append("original_model")
+    
+interface_original = gr.Interface(fn=create_response, 
+    title="original",
+    description="original language model, no fine tuning",
+    examples=examples,
+    inputs=common_input_component_list.append(gr.Textbox(label="model", lines=3, value="original_model",visible=False)),
+    outputs=common_output_component_list
+    )
+examples = common_examples.copy()
+for example in examples:
+    example.append("untethered_model")
+interface_untethered_model = gr.Interface(fn=create_response, 
+    title="untethered model",
+    description="language model fine tuned with'The Untethered Soul' chapter 17",
+    examples=examples,
+    inputs=common_input_component_list.append(gr.Textbox(label="model", lines=3, value="untethered_model",visible=False)),
+    outputs=common_output_component_list
+    )
+
+examples = common_examples.copy()
+for example in examples:
+    example.append("untethered_paraphrased_model")
+interface_untethered_model = gr.Interface(fn=create_response, 
+    title="untethered paraphrased_model",
+    description="language model fine tuned with'The Untethered Soul' chapter 17 paraphrased",
+    examples=examples,
+    inputs=common_input_component_list.append(gr.Textbox(label="model", lines=3, value="untethered_paraphrased_model",visible=False)),
+    outputs=common_output_component_list
+    )
+
+
+
+demo = gr.TabbedInterface([interface_original, interface_untethered_model, interface_untethered_model], ["Original", "Untethered", "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, interface3], ["Original", "Untethered paraphrased"])
-# with gr.Blocks() as demo:
-#   with gr.Row():
-#
 demo.launch()