Upload app.py

app.py

@@ -0,0 +1,162 @@
+# -*- coding: utf-8 -*-
+# file: app.py
+# time: 18:37 23/09/2023
+# author: Amir Khan
+# github: https://github.com/Amir22010
+
+import numpy
+import ast
+import gradio as gr
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+try:
+    tokenizer_english = AutoTokenizer.from_pretrained("amir22010/PyABSA_Hospital_English_allenai_tk-instruct-base-def-pos_FinedTuned_Model")
+    double_english_generator = AutoModelForSeq2SeqLM.from_pretrained("amir22010/PyABSA_Hospital_English_allenai_tk-instruct-base-def-pos_FinedTuned_Model")
+except:
+    print("english model load error")
+'''
+try:
+    tokenizer_multilingual = AutoTokenizer.from_pretrained("amir22010/layoutxlm-xfund-ja")
+    double_multilingual_generator = AutoModelForSeq2SeqLM.from_pretrained("amir22010/layoutxlm-xfund-ja")
+except:
+    print("multilingual model load error")
+
+try:
+    tokenizer_keybert = AutoTokenizer.from_pretrained("amir22010/layoutxlm-xfund-ja")
+    double_keybert_generator = AutoModelForSeq2SeqLM.from_pretrained("amir22010/layoutxlm-xfund-ja")
+except:
+    print("keybert model load error")
+
+'''
+def perform_asde_inference(text, dataset, model_id):
+    if not text:
+        df = pd.read_csv('pyabsa_english.csv')
+        random_i = np.random.randint(low=0, high=df.shape[0], size=(1,)).flat[0]
+        selected_df = df.iloc[random_i]
+        text = selected_df['clean_text'].iloc[0]
+        true_aspect = selected_df['actual_aspects'].iloc[0]
+        true_sentiment = selected_df['actual_sentiments'].iloc[0]
+        
+
+    bos_instruction = """Definition: The output will be the aspects (both implicit and explicit) and the aspects sentiment polarity. In cases where there are no aspects the output should be noaspectterm:none.
+        Positive example 1-
+        input: this hospital has a good team of doctors who will take care of all your needs brilliantly.
+        output: doctors:positive
+        Positive example 2- 
+        input: Arthur as Irv at ham hospital ran an Nagar , Madurai has a doctor who engages you in a conversation and tries to take your mind off the pain and he has trained the staff to do so as well.
+        output: doctor:positive, staff:positive
+        Now complete the following example-
+        input: """
+    delim_instruct = ''
+    eos_instruct = ' \noutput:'
+
+    if model_id == "PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
+        tokenized_text = tokenizer_english(bos_instruction + text + delim_instruct + eos_instruct, return_tensors="pt")
+        output = double_english_generator.generate(tokenized_text.input_ids,max_length=512)
+        model_generated = tokenizer_english.decode(output[0], skip_special_tokens=True)
+    '''
+    elif model_id == "PyABSA_Hospital_Multilingual_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
+        tokenized_text = tokenizer_multilingual(bos_instruction + text + delim_instruct + eos_instruct, return_tensors="pt")
+        output = double_multilingual_generator.generate(tokenized_text.input_ids,max_length=512)
+        result = tokenizer_multilingual.decode(output[0], skip_special_tokens=True)
+    elif model_id == "PyABSA_Hospital_KeyBert_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
+        tokenized_text = tokenizer_keybert(bos_instruction + text + delim_instruct + eos_instruct, return_tensors="pt")
+        output = double_keybert_generator.generate(tokenized_text.input_ids,max_length=512)
+        result = tokenizer_keybert.decode(output[0], skip_special_tokens=True)
+    '''
+    pred_asp = [i.split(':')[0] for i in model_generated.split(',')]
+    pred_sent = [i.split(':')[1] for i in model_generated.split(',')]
+
+    pred_doubles = pd.DataFrame(list(map(list, zip(pred_asp, pred_sent))),columns=['Aspect','Sentiment'])
+    true_doubles = pd.DataFrame(list(map(list, zip(ast.literal_eval(true_aspect), ast.literal_eval(true_sentiment)))),columns=['Aspect','Sentiment'])
+
+    return pred_doubles, true_doubles, text, model_generated
+
+def run_demo(text, dataset, model_id):
+    try:
+        data = {
+            "text": text,
+            "dataset": dataset
+        }
+        return inference(text, dataset, model_id)
+    except Exception as e:
+        print(e)
+
+
+def inference(text, dataset, model_id):
+    return perform_asde_inference(text, dataset, model_id)
+
+if __name__ == "__main__":
+    demo = gr.Blocks()
+    with demo:
+        with gr.Row():
+            if triplet_extractor:
+                with gr.Column():
+                    gr.Markdown(
+                        "# <p align='center'>Hospital Review Aspect Sentiment Generation</p>"
+                    )
+                    with gr.Row():
+                        with gr.Column():
+                            asde_input_sentence = gr.Textbox(
+                                placeholder="Leave this box blank and choose a dataset will give you a random example...",
+                                label="Example:",
+                            )
+                            gr.Markdown(
+                                "You can find code and dataset at [MTech Thesis Project](https://github.com/Amir22010)"
+                            )
+                            asde_dataset_ids = gr.Radio(
+                                choices=[
+                                    "HospitalReviews"
+                                ],
+                                value="HospitalReviews",
+                                label="Datasets",
+                            )
+                            
+                            asde_model_ids = gr.Radio(
+                                choices=[
+                                    "PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model", 
+                                   # "PyABSA_Hospital_Multilingual_allenai/tk-instruct-base-def-pos_FinedTuned_Model", 
+                                   # "PyABSA_Hospital_KeyBert_allenai/tk-instruct-base-def-pos_FinedTuned_Model"
+                                ],
+                                value="PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model",
+                                label="Fine-tuned Models on Hospital Review custom data",
+                            )
+
+                            asde_inference_button = gr.Button("Let's go!")
+
+                            asde_output_text = gr.TextArea(label="Example:")
+
+                            asde_model_output_generated_sentence = gr.Textbox(
+                                placeholder="Text Generated...",
+                                label="Model Prediction Text Generated:",
+                            )
+                            asde_output_pred_df = gr.DataFrame(
+                                label="Predicted Aspect & Sentiment:"
+                            )
+                            asde_output_true_df = gr.DataFrame(
+                                label="Original Aspect & Sentiment:"
+                            )
+
+                            asde_inference_button.click(
+                                fn=run_demo,
+                                inputs=[
+                                    asde_input_sentence,
+                                    asde_dataset_ids,
+                                    asde_model_ids
+                                    gr.Text("ASDE", visible=False),
+                                ],
+                                outputs=[
+                                    asde_output_pred_df,
+                                    asde_output_true_df,
+                                    asde_output_text,
+                                    asde_model_output_generated_sentence
+                                ],
+                            )
+        gr.Markdown(
+            """### GitHub Repo: [MTech Thesis Project](https://github.com/Amir22010)
+            ### Author: [Amir Khan](https://github.com/Amir22010)
+            """
+        )
+
+    demo.launch()