Merge branch 'main' of https://huggingface.co/spaces/zmbfeng/testchatbot

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
 import random
 import os
+import copy
+
 from huggingface_hub import login
 from transformers import pipeline
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
@@ -9,33 +11,142 @@ 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')
-fine_tuned_model = GPT2LMHeadModel.from_pretrained('zmbfeng/FineTune-1')
-
-def create_response_original(input_str,input_number):
-  #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))[0])
-  return (output_str)
-
-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, inputs=["text","number",], outputs="text", title="Original")
-interface2 = gr.Interface(fn=create_response_fine_tuned, inputs="text", outputs="text", title="Fine Tuned")
-demo = gr.TabbedInterface([interface1, interface2], ["Original", "Fine Tuned"])
-# with gr.Blocks() as demo:
-#   with gr.Row():
-#
+untethered_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240225_epochs_500')
+untethered_paraphrased_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untethered_20240227_epochs_350')
+                     
+def create_response(input_str, 
+                             # num_beams, 
+                             num_return_sequences,
+                             temperature,
+                             repetition_penalty,
+                             top_p,
+                             # top_k,
+                             do_sample,
+                             model_name):
+  print("input_str="+input_str)
+  print("model_name="+str(model_name))
+  # 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))
+       
+  encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt")
+  input_ids = encoded["input_ids"]
+  attention_mask = encoded["attention_mask"]
+
+
+  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 )
+      
+  outputs = model_name+"generated <br>"                                
+  for output_id in output_ids:
+    output = tokenizer.decode(output_id, skip_special_tokens=True)
+    outputs=outputs+output+"<br/>"
+  return outputs
+
+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),
+    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)
+    ]
+input_component_list=copy.deepcopy(common_input_component_list)
+input_component_list.append(gr.Textbox(label="model", lines=3, value="original_model",visible=False))
+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]
+    ]
+examples = copy.deepcopy(common_examples)
+print(examples)
+for example in examples:
+    example.append("original_model")
+print(examples)   
+input_component_list=copy.deepcopy(common_input_component_list)
+input_component_list.append(gr.Textbox(label="model", lines=3, value="untethered_model",visible=False))
+output_component_list = copy.deepcopy(common_output_component_list)
+interface_original = gr.Interface(fn=create_response, 
+    title="original",
+    description="original language model, no fine tuning",
+    examples=examples,
+    inputs=input_component_list,
+    outputs=output_component_list
+    )
+examples = copy.deepcopy(common_examples)
+print(examples)
+for example in examples:
+    example.append("untethered_model")
+print(examples)
+input_component_list=copy.deepcopy(common_input_component_list)
+input_component_list.append(gr.Textbox(label="model", lines=3, value="untethered_paraphrased_model",visible=False))
+output_component_list = copy.deepcopy(common_output_component_list)
+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=input_component_list,
+    outputs=output_component_list
+    )
+
+examples = copy.deepcopy(common_examples)
+print(examples)
+for example in examples:
+    example.append("untethered_paraphrased_model")
+print(examples)
+input_component_list=copy.deepcopy(common_input_component_list)
+input_component_list.append(gr.Textbox(label="model", lines=3, value="untethered_model",visible=False))
+output_component_list = copy.deepcopy(common_output_component_list)
+interface_untethered_paraphrased_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=input_component_list,
+    outputs= output_component_list
+    )
+
+
+
+demo = gr.TabbedInterface([interface_original, interface_untethered_model, interface_untethered_paraphrased_model], ["Original", "Untethered", "Untethered paraphrased"])
+
 demo.launch()