Updated

app.py

@@ -26,13 +26,13 @@ analyze_and_highlight_quillbot = partial(
 )
 
 
-def ai_generated_test(option, bias_buster_selected, input, models):
+def ai_generated_test(option, bias_buster_selected, input):
     if bias_buster_selected:
         input = update(input)
     if option == "Human vs AI":
         return predict_bc_scores(input), None
     elif option == "Human vs AI Source Models":
-        return predict_bc_scores(input), predict_mc_scores(input, models)
+        return predict_bc_scores(input), predict_mc_scores(input)
     return None, None
 
 
@@ -41,7 +41,6 @@ def main(
     ai_option,
     plag_option,
     input,
-    models,
     year_from,
     month_from,
     day_from,
@@ -52,17 +51,6 @@ def main(
     source_block_size,
 ):
 
-    # formatted_tokens = plagiarism_check(
-    #     plag_option,
-    #     input,
-    #     year_from,
-    #     month_from,
-    #     day_from,
-    #     year_to,
-    #     month_to,
-    #     day_to,
-    #     domains_to_skip,
-    # )
     formatted_tokens = html_highlight(
         plag_option,
         input,
@@ -75,9 +63,9 @@ def main(
         domains_to_skip,
         source_block_size,
     )
-    depth_analysis_plot = depth_analysis(input)
+    depth_analysis_plot = depth_analysis(bias_buster_selected, input)
     bc_score = predict_bc_scores(input)
-    mc_score = predict_mc_scores(input, models)
+    mc_score = predict_mc_scores(input)
     quilscore = predict_quillbot(input)
 
     return (
@@ -143,13 +131,13 @@ with gr.Blocks() as demo:
             multiselect=True,
             label="Models to test against",
         )
+        
     with gr.Row():
         with gr.Column():
             ai_option = gr.Radio(
                 [
                     "Human vs AI",
                     "Human vs AI Source Models",
-                    # "Human vs AI Source Models (1 on 1)",
                 ],
                 label="Choose an option please.",
             )
@@ -205,9 +193,6 @@ with gr.Blocks() as demo:
         with gr.Column():
             bc_highlighter_output = gr.HTML(label="Human vs. AI Highlighter")
 
-        # with gr.Column():
-        #     mc1on1Label = gr.Label(label="Creator(1 on 1 Approach)")
-
     with gr.Row():
         with gr.Column():
             QLabel = gr.Label(label="Humanized")
@@ -314,7 +299,6 @@ with gr.Blocks() as demo:
             ai_option,
             plag_option,
             input_text,
-            models,
             year_from,
             month_from,
             day_from,
@@ -327,7 +311,6 @@ with gr.Blocks() as demo:
         outputs=[
             bcLabel,
             mcLabel,
-            # mc1on1Label,
             sentenceBreakdown,
             writing_analysis_plot,
             QLabel,
@@ -337,8 +320,7 @@ with gr.Blocks() as demo:
 
     only_ai_btn.click(
         fn=ai_generated_test,
-        inputs=[ai_option, bias_buster_selected, input_text, models],
-        # outputs=[bcLabel, mcLabel, mc1on1Label],
+        inputs=[ai_option, bias_buster_selected, input_text],
         outputs=[bcLabel, mcLabel],
         api_name="ai_check",
     )

nohup.out

@@ -1,166 +1,15 @@
-Original BC scores: AI: 0.983885645866394, HUMAN: 0.01611432246863842
-Calibration BC scores: AI: 0.5142857142857142, HUMAN: 0.48571428571428577
-Input Text: sOperation Title was an unsuccessful 1942 Allied attack on the German battleship Tirpitz during World War II. The Allies considered Tirpitz to be a major threat to their shipping and after several Royal Air Force heavy bomber raids failed to inflict any damage it was decided to use Royal Navy midget submarines instead. /s
-
-correcting text..:   0%|          | 0/2 [00:00<?, ?it/s]
-correcting text..: 100%|██████████| 2/2 [00:00<00:00, 29.39it/s]
-Traceback (most recent call last):
-  File "/usr/local/lib/python3.9/dist-packages/gradio/queueing.py", line 527, in process_events
-    response = await route_utils.call_process_api(
-  File "/usr/local/lib/python3.9/dist-packages/gradio/route_utils.py", line 270, in call_process_api
-    output = await app.get_blocks().process_api(
-  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1856, in process_api
-    data = await self.postprocess_data(fn_index, result["prediction"], state)
-  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1634, in postprocess_data
-    self.validate_outputs(fn_index, predictions)  # type: ignore
-  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1610, in validate_outputs
-    raise ValueError(
-ValueError: An event handler (update) didn't receive enough output values (needed: 2, received: 1).
-Wanted outputs:
-    [<gradio.components.textbox.Textbox object at 0x7f79abf202b0>, <gradio.components.textbox.Textbox object at 0x7f79abf20a60>]
-Received outputs:
-    ["Operation Title was an unsuccessful 1942 Allied attack on the German battleship Tirpitz during World War II. The Allies considered Tirpitz to be a major threat to their shipping and after several Royal Air Force heavy bomber raids failed to inflict any damage it was decided to use Royal Navy midget submarines instead."]
-/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version!
-  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
-2024-05-15 18:41:05.953508: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-2024-05-15 18:41:11.449382: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-[nltk_data] Downloading package punkt to /root/nltk_data...
-[nltk_data]   Package punkt is already up-to-date!
-[nltk_data] Downloading package stopwords to /root/nltk_data...
-[nltk_data]   Package stopwords is already up-to-date!
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
-- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
-- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-Framework not specified. Using pt to export the model.
-Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
-- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
-- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
-Using the export variant default. Available variants are:
-    - default: The default ONNX variant.
-
-***** Exporting submodel 1/1: RobertaForSequenceClassification *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> False
-Framework not specified. Using pt to export the model.
-Using the export variant default. Available variants are:
-    - default: The default ONNX variant.
-Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
-Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
-
-***** Exporting submodel 1/3: T5Stack *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> False
-
-***** Exporting submodel 2/3: T5ForConditionalGeneration *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> True
-/usr/local/lib/python3.9/dist-packages/transformers/modeling_utils.py:1017: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-  if causal_mask.shape[1] < attention_mask.shape[1]:
-
-***** Exporting submodel 3/3: T5ForConditionalGeneration *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> True
-/usr/local/lib/python3.9/dist-packages/transformers/models/t5/modeling_t5.py:503: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-  elif past_key_value.shape[2] != key_value_states.shape[1]:
-In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
-In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
-Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
-Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
-[nltk_data] Downloading package cmudict to /root/nltk_data...
-[nltk_data]   Package cmudict is already up-to-date!
-[nltk_data] Downloading package punkt to /root/nltk_data...
-[nltk_data]   Package punkt is already up-to-date!
-[nltk_data] Downloading package stopwords to /root/nltk_data...
-[nltk_data]   Package stopwords is already up-to-date!
-[nltk_data] Downloading package wordnet to /root/nltk_data...
-[nltk_data]   Package wordnet is already up-to-date!
-/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version!
-  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
-Collecting en_core_web_sm==2.3.1
-  Using cached en_core_web_sm-2.3.1-py3-none-any.whl
-Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from en_core_web_sm==2.3.1) (2.3.9)
-Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.9)
-Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.11)
-Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.66.2)
-Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.7)
-Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.1)
-Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3)
-Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (52.0.0)
-Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.8)
-Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.10)
-Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.10.1)
-Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.26.4)
-Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.2)
-Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.6)
-✔ Download and installation successful
-You can now load the model via spacy.load('en_core_web_sm')
-/usr/local/lib/python3.9/dist-packages/gradio/utils.py:953: UserWarning: Expected 1 arguments for function <function depth_analysis at 0x7f6df970eee0>, received 2.
+/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
   warnings.warn(
-/usr/local/lib/python3.9/dist-packages/gradio/utils.py:961: UserWarning: Expected maximum 1 arguments for function <function depth_analysis at 0x7f6df970eee0>, received 2.
+/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
   warnings.warn(
-IMPORTANT: You are using gradio version 4.28.3, however version 4.29.0 is available, please upgrade.
---------
-Running on local URL:  http://0.0.0.0:80
-Running on public URL: https://1f9431205fb743687b.gradio.live
-
-This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
-
-
-huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-To disable this warning, you can either:
-	- Avoid using `tokenizers` before the fork if possible
-	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
-  warnings.warn("Can't initialize NVML")
-/usr/local/lib/python3.9/dist-packages/optimum/bettertransformer/models/encoder_models.py:301: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
-  hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)
-Original BC scores: AI: 0.0012912281090393662, HUMAN: 0.9987087249755859
-Calibration BC scores: AI: 0.09973753280839895, HUMAN: 0.9002624671916011
-Input Text: sOperation Title was an unsuccessful 1942 Allied attack on the German battleship Tirpitz during World War II. The Allies considered Tirpitz to be a major threat to their shipping and after several Royal Air Force heavy bomber raids failed to inflict any damdage it was decided to use Royal Navy midget submarines instead. /s
-
-
-Original BC scores: AI: 1.946412595543734e-07, HUMAN: 0.9999997615814209
-Calibration BC scores: AI: 0.0013484877672895396, HUMAN: 0.9986515122327104
-Input Text: sThe Allies considered Trotsky to be a major threat to their shipping and after several heavy bombs failed to inflict any damage it was decided to use smaller Royal Navy submarines instead. /s
-Original BC scores: AI: 7.88536635809578e-06, HUMAN: 0.9999921321868896
-Calibration BC scores: AI: 0.008818342151675485, HUMAN: 0.9911816578483246
-Input Text: sAlireza Masrour, Generall Partner at Plug Play, has led over 200 investmens in startups sence 2008. Notable unicorn investmens include CloudWalk, Flyr, FiscalNote, Shippo, Owkin, and Trulioo. He has also been involvd in sucsessful exits such as FiscalNote's IPO, HealthPocket's acqusition by Health Insurans Innovations, and Kustomer's acqusition by FaceBook. Alireza has receeved recognition for his acheivements, includng beeing named a Silicon Valley 40 under 40 in 2018 and a rising-star VC by BusinessInsider. He has had 13 unicorn portfollio companys and manages a B Portfollio Club with investmens in companys like N26, BigID, Shippo, and TrueBill, wich was acquried by RocketCo for 1. 3B. Other investmens include Flexiv, Owkin, VisbyMedikal, Animoca, and AutoX. /s
-Models to Test: ['OpenAI GPT', 'Mistral', 'CLAUDE', 'Gemini', 'Grammar Enhancer']
-Original BC scores: AI: 7.88536635809578e-06, HUMAN: 0.9999921321868896
-Calibration BC scores: AI: 0.008818342151675485, HUMAN: 0.9911816578483246
-Starting MC
-MC Score: {'OpenAI GPT': 1.1978447330533474e-12, 'Mistral': 2.7469434957703303e-13, 'CLAUDE': 8.578213092883691e-13, 'Gemini': 6.304846046418989e-13, 'Grammar Enhancer': 0.008818342148714584}
-
-Original BC scores: AI: 0.9980764389038086, HUMAN: 0.001923577394336462
-Calibration BC scores: AI: 0.7272727272727273, HUMAN: 0.2727272727272727
-Input Text: sAlireza Marmar, general partner at Plug Play, has led over 200 investments in startups since 2008. Notable unicorns include CloudWatch, Flyer, FiscalNote, Shippo, Owkin, and Trulio. He has also been involved in successful exits such as Microsoft's IPO, HealthPocket's acquisition by HealthInsuranceInc. , and Salesforce's acquisition of Facebook. Alireza has received praise for his achievements, including being named a Silicon Valley 40 under 40 in 2018 and a Rising Star by Business Insider. He has had 13 unicorn companies and manages a Billion Ponzi scheme with investments in companies like N26, BigID, Shippo, and TruBill, which was acquired by RocketCoop for 1. 3B. Other investments include Xerox, Owatu, Microsoft, Amazon, and AutoX. /s
-Models to Test: ['OpenAI GPT', 'Mistral', 'CLAUDE', 'Gemini', 'Grammar Enhancer']
-Original BC scores: AI: 0.9980764389038086, HUMAN: 0.001923577394336462
-Calibration BC scores: AI: 0.7272727272727273, HUMAN: 0.2727272727272727
-Starting MC
-MC Score: {'OpenAI GPT': 1.7068867157614812e-06, 'Mistral': 6.292188498138414e-10, 'CLAUDE': 8.175567903345952e-09, 'Gemini': 2.868823230740637e-08, 'Grammar Enhancer': 0.7272709828929925}
-
-
-/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version!
-  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
-2024-05-15 19:31:58.934498: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+2024-05-16 12:11:24.934695: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
 To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-2024-05-15 19:32:05.107700: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 [nltk_data] Downloading package punkt to /root/nltk_data...
 [nltk_data]   Package punkt is already up-to-date!
 [nltk_data] Downloading package stopwords to /root/nltk_data...
 [nltk_data]   Package stopwords is already up-to-date!
+/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
 The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
 The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
 The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
@@ -186,6 +35,8 @@ Using the export variant default. Available variants are:
     - default: The default ONNX variant.
 Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
 Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
+/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
 
 ***** Exporting submodel 1/3: T5Stack *****
 Using framework PyTorch: 2.3.0+cu121
@@ -209,104 +60,8 @@ In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.
 In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
 Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
 Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
-[nltk_data] Downloading package cmudict to /root/nltk_data...
-[nltk_data]   Package cmudict is already up-to-date!
-[nltk_data] Downloading package punkt to /root/nltk_data...
-[nltk_data]   Package punkt is already up-to-date!
-[nltk_data] Downloading package stopwords to /root/nltk_data...
-[nltk_data]   Package stopwords is already up-to-date!
-[nltk_data] Downloading package wordnet to /root/nltk_data...
-[nltk_data]   Package wordnet is already up-to-date!
-/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version!
-  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
-Collecting en_core_web_sm==2.3.1
-  Using cached en_core_web_sm-2.3.1-py3-none-any.whl
-Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from en_core_web_sm==2.3.1) (2.3.9)
-Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.26.4)
-Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.9)
-Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.6)
-Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.2)
-Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3)
-Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.1)
-Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.10.1)
-Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.7)
-Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.66.2)
-Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.11)
-Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (52.0.0)
-Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.10)
-Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.8)
-✔ Download and installation successful
-You can now load the model via spacy.load('en_core_web_sm')
-/usr/local/lib/python3.9/dist-packages/gradio/utils.py:953: UserWarning: Expected 1 arguments for function <function depth_analysis at 0x7f137170dee0>, received 2.
-  warnings.warn(
-/usr/local/lib/python3.9/dist-packages/gradio/utils.py:961: UserWarning: Expected maximum 1 arguments for function <function depth_analysis at 0x7f137170dee0>, received 2.
-  warnings.warn(
-WARNING:  Invalid HTTP request received.
-huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-To disable this warning, you can either:
-	- Avoid using `tokenizers` before the fork if possible
-	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
 /usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
   warnings.warn("Can't initialize NVML")
-/usr/local/lib/python3.9/dist-packages/optimum/bettertransformer/models/encoder_models.py:301: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
-  hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)
-/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version!
-  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
-2024-05-15 22:08:54.473739: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-2024-05-15 22:09:00.121158: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-[nltk_data] Downloading package punkt to /root/nltk_data...
-[nltk_data]   Package punkt is already up-to-date!
-[nltk_data] Downloading package stopwords to /root/nltk_data...
-[nltk_data]   Package stopwords is already up-to-date!
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
-- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
-- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
-The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.
-Framework not specified. Using pt to export the model.
-Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
-- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
-- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
-Using the export variant default. Available variants are:
-    - default: The default ONNX variant.
-
-***** Exporting submodel 1/1: RobertaForSequenceClassification *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> False
-Framework not specified. Using pt to export the model.
-Using the export variant default. Available variants are:
-    - default: The default ONNX variant.
-Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
-Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
-
-***** Exporting submodel 1/3: T5Stack *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> False
-
-***** Exporting submodel 2/3: T5ForConditionalGeneration *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> True
-/usr/local/lib/python3.9/dist-packages/transformers/modeling_utils.py:1017: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-  if causal_mask.shape[1] < attention_mask.shape[1]:
-
-***** Exporting submodel 3/3: T5ForConditionalGeneration *****
-Using framework PyTorch: 2.3.0+cu121
-Overriding 1 configuration item(s)
-	- use_cache -> True
-/usr/local/lib/python3.9/dist-packages/transformers/models/t5/modeling_t5.py:503: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-  elif past_key_value.shape[2] != key_value_states.shape[1]:
-In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
-In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode
-Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
-Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_beams': 2, 'no_repeat_ngram_size': 4}
 [nltk_data] Downloading package cmudict to /root/nltk_data...
 [nltk_data]   Package cmudict is already up-to-date!
 [nltk_data] Downloading package punkt to /root/nltk_data...
@@ -315,47 +70,99 @@ Non-default generation parameters: {'max_length': 512, 'min_length': 8, 'num_bea
 [nltk_data]   Package stopwords is already up-to-date!
 [nltk_data] Downloading package wordnet to /root/nltk_data...
 [nltk_data]   Package wordnet is already up-to-date!
-/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0) doesn't match a supported version!
-  warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
-Collecting en_core_web_sm==2.3.1
-  Using cached en_core_web_sm-2.3.1-py3-none-any.whl
-Requirement already satisfied: spacy<2.4.0,>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from en_core_web_sm==2.3.1) (2.3.9)
-Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3)
-Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.10)
-Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.2)
-Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.11)
-Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (52.0.0)
-Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.26.4)
-Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.25.1)
-Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (4.66.2)
-Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.10.1)
-Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.9)
-Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.8)
-Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (7.4.6)
-Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.7)
+/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
+  warnings.warn("Can't initialize NVML")
+Collecting en-core-web-sm==3.7.1
+  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)
+Requirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.9/dist-packages (from en-core-web-sm==3.7.1) (3.7.2)
+Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.4.0)
+Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.9.4)
+Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (24.0)
+Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (6.4.0)
+Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.2)
+Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10)
+Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5)
+Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/lib/python3/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.25.1)
+Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.4.8)
+Requirement already satisfied: thinc<8.3.0,>=8.1.8 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.3)
+Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (52.0.0)
+Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.26.4)
+Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8)
+Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.4)
+Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.66.4)
+Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10)
+Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.3.4)
+Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12)
+Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.7.1)
+Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9)
+Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.9/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)
+Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.9/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.1)
+Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.18.2)
+Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.11.0)
+Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.6.0)
+Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11)
+Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4)
+Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)
+Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.9/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0)
+Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5)
 ✔ Download and installation successful
-You can now load the model via spacy.load('en_core_web_sm')
-/usr/local/lib/python3.9/dist-packages/gradio/utils.py:953: UserWarning: Expected 1 arguments for function <function depth_analysis at 0x7f149d70dee0>, received 2.
+You can now load the package via spacy.load('en_core_web_sm')
+/usr/local/lib/python3.9/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
   warnings.warn(
-/usr/local/lib/python3.9/dist-packages/gradio/utils.py:961: UserWarning: Expected maximum 1 arguments for function <function depth_analysis at 0x7f149d70dee0>, received 2.
+/usr/local/lib/python3.9/dist-packages/gradio/utils.py:924: UserWarning: Expected 1 arguments for function <function depth_analysis at 0x7f402afeff70>, received 2.
   warnings.warn(
-huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-To disable this warning, you can either:
-	- Avoid using `tokenizers` before the fork if possible
-	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-/usr/local/lib/python3.9/dist-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
-  warnings.warn("Can't initialize NVML")
+/usr/local/lib/python3.9/dist-packages/gradio/utils.py:932: UserWarning: Expected maximum 1 arguments for function <function depth_analysis at 0x7f402afeff70>, received 2.
+  warnings.warn(
+IMPORTANT: You are using gradio version 4.26.0, however version 4.29.0 is available, please upgrade.
+--------
+Running on local URL:  http://0.0.0.0:80
+Running on public URL: https://3122a891c774a52363.gradio.live
+
+This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
+
 /usr/local/lib/python3.9/dist-packages/optimum/bettertransformer/models/encoder_models.py:301: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)
   hidden_states = torch._nested_tensor_from_mask(hidden_states, ~attention_mask)
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
-WARNING:  Invalid HTTP request received.
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.9/dist-packages/gradio/queueing.py", line 527, in process_events
+    response = await route_utils.call_process_api(
+  File "/usr/local/lib/python3.9/dist-packages/gradio/route_utils.py", line 261, in call_process_api
+    output = await app.get_blocks().process_api(
+  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1786, in process_api
+    result = await self.call_function(
+  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1338, in call_function
+    prediction = await anyio.to_thread.run_sync(
+  File "/usr/local/lib/python3.9/dist-packages/anyio/to_thread.py", line 56, in run_sync
+    return await get_async_backend().run_sync_in_worker_thread(
+  File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 2144, in run_sync_in_worker_thread
+    return await future
+  File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 851, in run
+    result = context.run(func, *args)
+  File "/usr/local/lib/python3.9/dist-packages/gradio/utils.py", line 759, in wrapper
+    response = f(*args, **kwargs)
+  File "/home/aliasgarov/copyright_checker/app.py", line 35, in ai_generated_test
+    return predict_bc_scores(input), predict_mc_scores(input)
+  File "/home/aliasgarov/copyright_checker/predictors.py", line 390, in predict_mc_scores
+    for key in models
+NameError: name 'models' is not defined
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.9/dist-packages/gradio/queueing.py", line 527, in process_events
+    response = await route_utils.call_process_api(
+  File "/usr/local/lib/python3.9/dist-packages/gradio/route_utils.py", line 261, in call_process_api
+    output = await app.get_blocks().process_api(
+  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1786, in process_api
+    result = await self.call_function(
+  File "/usr/local/lib/python3.9/dist-packages/gradio/blocks.py", line 1338, in call_function
+    prediction = await anyio.to_thread.run_sync(
+  File "/usr/local/lib/python3.9/dist-packages/anyio/to_thread.py", line 56, in run_sync
+    return await get_async_backend().run_sync_in_worker_thread(
+  File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 2144, in run_sync_in_worker_thread
+    return await future
+  File "/usr/local/lib/python3.9/dist-packages/anyio/_backends/_asyncio.py", line 851, in run
+    result = context.run(func, *args)
+  File "/usr/local/lib/python3.9/dist-packages/gradio/utils.py", line 759, in wrapper
+    response = f(*args, **kwargs)
+  File "/home/aliasgarov/copyright_checker/app.py", line 35, in ai_generated_test
+    return predict_bc_scores(input), predict_mc_scores(input)
+  File "/home/aliasgarov/copyright_checker/predictors.py", line 390, in predict_mc_scores
+    for key in models
+NameError: name 'models' is not defined

predictors.py

@@ -28,10 +28,8 @@ device = 'cpu'
 text_bc_model_path = params["TEXT_BC_MODEL_PATH"]
 text_mc_model_path = params["TEXT_MC_MODEL_PATH"]
 text_quillbot_model_path = params["TEXT_QUILLBOT_MODEL_PATH"]
-text_1on1_models = params["TEXT_1ON1_MODEL"]
 quillbot_labels = params["QUILLBOT_LABELS"]
 mc_label_map = params["MC_OUTPUT_LABELS"]
-text_1on1_label_map = params["1ON1_OUTPUT_LABELS"]
 mc_token_size = int(params["MC_TOKEN_SIZE"])
 bc_token_size = int(params["BC_TOKEN_SIZE"])
 bias_checker_model_name = params['BIAS_CHECKER_MODEL_PATH']
@@ -48,13 +46,6 @@ quillbot_tokenizer = AutoTokenizer.from_pretrained(text_quillbot_model_path)
 quillbot_model = AutoModelForSequenceClassification.from_pretrained(
     text_quillbot_model_path
 ).to(device)
-tokenizers_1on1 = {}
-models_1on1 = {}
-for model_name, model in zip(mc_label_map, text_1on1_models):
-    tokenizers_1on1[model_name] = AutoTokenizer.from_pretrained(model)
-    models_1on1[model_name] = (
-        AutoModelForSequenceClassification.from_pretrained(model).to(device)
-    )
 
 
 
@@ -352,43 +343,7 @@ def predict_bc_scores(input):
     return bc_score
 
 
-def predict_1on1(model, tokenizer, text):
-    with torch.no_grad():
-        model.eval()
-        tokens = tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            return_tensors="pt",
-            max_length=mc_token_size,
-        ).to(device)
-        output = model(**tokens)
-        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
-        return output_norm
-
-
-def predict_1on1_combined(input):
-    predictions = []
-    for i, model in enumerate(text_1on1_models):
-        predictions.append(
-            predict_1on1(models_1on1[model], tokenizers_1on1[model], input)[1]
-        )
-    return predictions
-
-
-def predict_1on1_single(input, model):
-    predictions = predict_1on1(
-        models_1on1[model], tokenizers_1on1[model], input
-    )[1]
-    return predictions
-
-
-def predict_mc_scores(input, models):
-
-    if len(models) == 0:
-        return {}
-
-    print(f"Models to Test: {models}")
+def predict_mc_scores(input):
     # BC SCORE
     bc_scores = []
     samples_len_bc = len(
@@ -410,70 +365,34 @@ def predict_mc_scores(input, models):
     human_score = 1 - ai_score
     bc_score = {"AI": ai_score, "HUMAN": human_score}
     print(f"Calibration BC scores: AI: {ai_score}, HUMAN: {human_score}")
-
-    # MC SCORE
-    if len(models) > 1:
-        print("Starting MC")
-        mc_scores = []
-        segments_mc = split_text_allow_complete_sentences_nltk(
-            input, type_det="mc"
-        )
-        samples_len_mc = len(
-            split_text_allow_complete_sentences_nltk(input, type_det="mc")
+    mc_scores = []
+    segments_mc = split_text_allow_complete_sentences_nltk(
+        input, type_det="mc"
+    )
+    samples_len_mc = len(
+        split_text_allow_complete_sentences_nltk(input, type_det="mc")
+    )
+    for i in range(samples_len_mc):
+        cleaned_text_mc = remove_special_characters(segments_mc[i])
+        mc_score = predict_mc(
+            text_mc_model, text_mc_tokenizer, cleaned_text_mc
         )
-        for i in range(samples_len_mc):
-            cleaned_text_mc = remove_special_characters(segments_mc[i])
-            mc_score = predict_mc(
-                text_mc_model, text_mc_tokenizer, cleaned_text_mc
-            )
-            mc_scores.append(mc_score)
-        mc_scores_array = np.array(mc_scores)
-        average_mc_scores = np.mean(mc_scores_array, axis=0)
-        mc_score_list = average_mc_scores.tolist()
-        mc_score = {}
-        for score, label in zip(mc_score_list, mc_label_map):
-            mc_score[label.upper()] = score
+        mc_scores.append(mc_score)
+    mc_scores_array = np.array(mc_scores)
+    average_mc_scores = np.mean(mc_scores_array, axis=0)
+    mc_score_list = average_mc_scores.tolist()
+    mc_score = {}
+    for score, label in zip(mc_score_list, mc_label_map):
+        mc_score[label.upper()] = score
 
-        mc_score = {
-            key: mc_score[key.upper()]
-            for key in models
-            if key.upper() in mc_score
-        }
-        total = sum(mc_score.values())
-        # Normalize each value by dividing it by the total
-        mc_score = {key: value / total for key, value in mc_score.items()}
-        sum_prob = 1 - bc_score["HUMAN"]
-        for key, value in mc_score.items():
-            mc_score[key] = value * sum_prob
-        print("MC Score:", mc_score)
-        if sum_prob < 0.01:
-            mc_score = {}
-
-    elif len(models) == 1:
-        print("Starting 1on1")
-        mc_scores = []
-        segments_mc = split_text_allow_complete_sentences_nltk(
-            input, type_det="mc"
-        )
-        samples_len_mc = len(
-            split_text_allow_complete_sentences_nltk(input, type_det="mc")
-        )
-        for i in range(samples_len_mc):
-            cleaned_text_mc = remove_special_characters(segments_mc[i])
-            mc_score = predict_1on1_single(cleaned_text_mc, models[0])
-            mc_scores.append(mc_score)
-        mc_scores_array = np.array(mc_scores)
-        average_mc_scores = np.mean(mc_scores_array, axis=0)
-        print(average_mc_scores)
-        mc_score_list = average_mc_scores.tolist()
+    total = sum(mc_score.values())
+    # Normalize each value by dividing it by the total
+    mc_score = {key: value / total for key, value in mc_score.items()}
+    sum_prob = 1 - bc_score["HUMAN"]
+    for key, value in mc_score.items():
+        mc_score[key] = value * sum_prob
+    print("MC Score:", mc_score)
+    if sum_prob < 0.01:
         mc_score = {}
-        mc_score[models[0].upper()] = mc_score_list
-        mc_score["OTHER"] = 1 - mc_score_list
-
-        sum_prob = 1 - bc_score["HUMAN"]
-        for key, value in mc_score.items():
-            mc_score[key] = value * sum_prob
-        if sum_prob < 0.01:
-            mc_score = {}
 
     return mc_score