Update app.py

app.py

@@ -1,88 +1,21 @@
-import gradio as gr
-from transformers import (
-    AutoModelForSeq2SeqLM,
-    AutoModelForTableQuestionAnswering,
-    AutoTokenizer,
-    pipeline,
-    TapexTokenizer, 
-    BartForConditionalGeneration
-)
-import pandas as pd
-import json
-
-# model_tapex = "microsoft/tapex-large-finetuned-wtq"
-# tokenizer_tapex = AutoTokenizer.from_pretrained(model_tapex)
-# model_tapex = AutoModelForSeq2SeqLM.from_pretrained(model_tapex)
-# pipe_tapex = pipeline(
-#     "table-question-answering", model=model_tapex, tokenizer=tokenizer_tapex
-# )
-
-#new
-tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
-model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
-
-
-# model_tapas = "google/tapas-large-finetuned-wtq"
-# tokenizer_tapas = AutoTokenizer.from_pretrained(model_tapas)
-# model_tapas = AutoModelForTableQuestionAnswering.from_pretrained(model_tapas)
-# pipe_tapas = pipeline(
-#     "table-question-answering", model=model_tapas, tokenizer=tokenizer_tapas
-# )
-
-#new
-pipe_tapas = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq")
-pipe_tapas2 = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wikisql-supervised")
-
-
-
-
-def process2(query, csv_dataStr):
-    # csv_data={"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
-    csv_data = json.loads(csv_dataStr)
-    table = pd.DataFrame.from_dict(csv_data)
-    #microsoft
-    encoding = tokenizer(table=table, query=query, return_tensors="pt")
-    outputs = model.generate(**encoding)
-    result_tapex=tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
-    #google
-    result_tapas = pipe_tapas(table=table, query=query)['cells'][0]
-    #google2
-    result_tapas2 = pipe_tapas2(table=table, query=query)['cells'][0]
-    return result_tapex, result_tapas, result_tapas2
-
-
-# Inputs
-query_text = gr.Text(label="")
-# input_file = gr.File(label="Upload a CSV file", type="file")
-input_data = gr.Text(label="")
-# rows_slider = gr.Slider(label="Number of rows")
-
-# Output
-answer_text_tapex = gr.Text(label="")
-answer_text_tapas = gr.Text(label="")
-answer_text_tapas2 = gr.Text(label="")
-
-description = "This Space lets you ask questions on CSV documents with Microsoft [TAPEX-Large](https://huggingface.co/microsoft/tapex-large-finetuned-wtq) and Google [TAPAS-Large](https://huggingface.co/google/tapas-large-finetuned-wtq). \
-Both have been fine-tuned on the [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset. \n\n\
-A sample file with football statistics is available in the repository: \n\n\
-* Which team has the most wins? Answer: Manchester City FC\n\
-* Which team has the most wins: Chelsea, Liverpool or Everton? Answer: Liverpool\n\
-* Which teams have scored less than 40 goals? Answer: Cardiff City FC, Fulham FC, Brighton & Hove Albion FC, Huddersfield Town FC\n\
-* What is the average number of wins? Answer: 16 (rounded)\n\n\
-You can also upload your own CSV file. Please note that maximum sequence length for both models is 1024 tokens, \
-so you may need to limit the number of rows in your CSV file. Chunking is not implemented yet."
-
-iface = gr.Interface(
-    theme="huggingface",
-    description=description,
-    layout="vertical",
-    fn=process2,
-    inputs=[query_text, input_data],
-    outputs=[answer_text_tapex, answer_text_tapas, answer_text_tapas2],
-    examples=[
-        
-    ],
-    allow_flagging="never",
-)
-
-iface.launch()
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-anything-promptgen-v2')
+
+prompt = r'1girl, genshin'
+
+# generate text using fine-tuned model
+nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+# generate 10 samples using contrastive search
+outs = nlp(prompt, max_length=76, num_return_sequences=10, do_sample=True, repetition_penalty=1.2, temperature=0.7, top_k=4, early_stopping=True)
+
+print('\nInput:\n' + 100 * '-')
+print('\033[96m' + prompt + '\033[0m')
+print('\nOutput:\n' + 100 * '-')
+for i in range(len(outs)):
+    # remove trailing commas and double spaces
+    outs[i] = str(outs[i]['generated_text']).replace('  ', '').rstrip(',')
+print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n')