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

app.py

@@ -59,7 +59,23 @@ def create_response(input_str,
     outputs=outputs+output+"<br/>"
   return outputs
 
-common_input_component_list = [
+
+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)   
+interface_original = gr.Interface(fn=create_response, 
+    title="original",
+    description="original language model, no fine tuning",
+    examples=examples,
+    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),
@@ -87,46 +103,53 @@ common_input_component_list = [
                           "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
+                          " output, but it might also introduce errors and inconsistencies ", value=True),
+        gr.Textbox(label="model", lines=3, value="original_model",visible=False)
+    ],
+    outputs=[gr.Textbox(label="output response", lines=30)]
     )
 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
+    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",visible=False)
+    ],
+    outputs=[gr.Textbox(label="output response", lines=30)]
     )
 
 examples = copy.deepcopy(common_examples)
@@ -134,15 +157,42 @@ 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
+    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_paraphrased_model",visible=False)
+    ],
+    outputs= [gr.Textbox(label="output response", lines=30)]
     )