init

app.py

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+import os, json
+import gradio as gr
+import pandas as pd
+
+QUESTIONS = [
+    "What is the DOI of this study?",
+    "What is the Citation ID of this study?",
+    "What is the First author of this study?",
+    "What is the year of this study?",
+    "What is the animal type of this study?",
+    "What is the exposure age of this study?",
+    "Is there any behavior test done in this study?",
+    "What's the Intervention 1's name of this study?(anesthetics only)",
+    "What's the Intervention 2's name of this study?(anesthetics only)",
+    "What's the genetic chain of this study?",
+]
+
+template = '''We now have a following <document> in the medical field:
+
+"""
+{}
+"""
+We have some introduction here:
+
+1. DOI: The DOI link for the article, usually can be found in the first line of the .txt file for the article. E.g., “DOI: 10.3892/mmr.2019.10397”.
+2. Citation ID: The number in the file name. E.g., “1134”.
+3. First author: The last name in the file name. E.g., “Guan”.
+4. Year: The year in the file name. E.g., “2019”.
+5. Animal type: The rodent type used in the article, should be one of the choices: mice, rats. E.g., “rats”.
+6. Exposure age: The age when the animals were exposed to anesthetics, should be mentioned as "PND1", "PND7","postnatal day 7", "Gestational day 21", etc, which should be extract as: 'PND XX' , 'Gestational day xx'. E.g., “PND7”.
+7. Behavior test: Whether there is any behavior test in the article, should be one of the choices: "Y", "N". "Y" is chosen if there are any of the behavior tests described and done in the article, which mentioned as: "Open field test", "Morris water task", "fear conditioning test", "Dark/light avoidance"; "passive/active avoidance test"; "elevated maze", "Forced swim test", "Object recognition test", "Social interaction/preference“. E.g., “N”.
+8. Intervention 1 & Intervention 2: Intervention 1 and Intervention 2 are both anesthetic drugs, which listed as: "isoflurane", "sevoflurane", "desflurane", "ketamine", "propofol", "Midazolam", "Nitrous oxide“. If none, put “NA”. E.g., “propofol”.
+9. Genetic chain: Genetic chain is the genetic type of the animals being used in the article, here is the examples: 
+    "C57BL/6", "C57BL/6J" should be extracted as "C57BL/6"; "Sprague Dawley", "Sprague-Dawley", "SD" should be extracted as "Sprague Dawley"; "CD-1" should be extracted as "CD-1"; "Wistar/ST" should be extracted as "Wistar/ST"; "Wistar" should be extracted as "Wistar"; "FMR-1 KO" should be extracted as "FMR-1 KO“. E.g., “Sprague Dawley”.
+    
+We have some <question>s begin with "Question" here:
+"""
+{}
+"""
+
+Please finish the following task:
+
+1. Please select the <original sentences> related the each <question> from the <document>.
+2. Please use the <original sentences> to answer the <question>.
+3. Please provide <original sentences> coming from the <document>.
+4. Output the <answer> in the following json format:
+
+{{
+    "Question 1": {{
+        "question": {{}},
+        "answer": {{}},
+        "original sentences": []
+    }},
+    "Question 2": {{
+        "question": {{}},
+        "answer": {{}},
+        "original sentences": []
+    }},
+    ...
+}}
+'''
+
+
+import requests
+
+class OpenAI:
+    def __init__(self, init_prompt = None):
+        self.history = []
+        if init_prompt is not None:
+            self.history.append({'role': 'system', 'content': init_prompt})
+
+    def clear_history(self):
+        self.history = []
+
+    def show_history(self):
+        for message in self.history:
+            print(f"{message['role']}: {message['content']}")
+
+    def get_raw_history(self):
+        return self.history
+
+    def __call__(self, prompt, with_history = False, model = 'gpt-3.5-turbo', temperature = 0, api_key = None):
+        URL = 'https://api.openai.com/v1/chat/completions'
+        new_message = {'role': 'user', 'content': prompt}
+        if with_history:
+            self.history.append(new_message)
+            messages = self.history
+        else:
+            messages = [new_message]
+
+        resp = requests.post(URL, json={
+            'model': model,
+            'messages': messages,
+            'temperature': temperature,
+        }, headers={
+            'Authorization': f"Bearer {api_key}"
+        })
+        # print(resp.json())
+        self.history.append(resp.json()['choices'][0]['message'])
+
+        return resp.json()['choices'][0]['message']['content']
+
+
+class Backend:
+    def __init__(self):
+        self.agent = OpenAI()
+
+    def read_file(self, file):
+        # read the file
+        with open(file.name, 'r') as f:
+            text = f.read()
+        return text
+
+    def highlight_text(self, text, highlight_list):
+        # hightlight the reference
+        for hl in highlight_list:
+            text = text.replace(hl, f'<mark style="background: #8B4513">{hl}</mark>')
+
+        # add line break
+        text = text.replace('\n', f" <br /> ")
+
+        # add scroll bar
+        text = f'<div style="height: 500px; overflow: auto;">{text}</div>'
+
+        return text
+
+    def process_file(self, file, question, openai_key):
+        # get the question
+        question = [ f'Question {id_ +1 }: {q}' for id_, q in enumerate(question) if 'Input question' not in q]
+        question = '\n'.join(question)
+
+        # get the text
+        self.text = self.read_file(file)
+
+        # make the prompt
+        prompt = template.format(self.text, question)
+
+        # interact with openai
+        res = self.agent(prompt, with_history = False, temperature = 0.1, model = 'gpt-3.5-turbo-16k', api_key = openai_key)
+        res = json.loads(res)
+
+        # for multiple questions
+        self.gpt_result = res
+        self.curret_question = 0
+        self.totel_question = len(res.keys())
+
+        # make a dataframe to record everything
+        self.ori_answer_df = pd.DataFrame(res).T
+        self.answer_df = pd.DataFrame(res).T
+
+        # default fist question
+        res = res['Question 1']
+        question = res['question']
+        self.answer = res['answer']
+        self.highlighted_out = res['original sentences']
+        highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
+        self.highlighted_out = '\n'.join(self.highlighted_out)
+
+        return question, self.answer, highlighted_out_html, self.answer, self.highlighted_out
+
+    def process_results(self, answer_correct, correct_answer, reference_correct, correct_reference):
+        if not hasattr(self, 'clicked_correct_answer'):
+            raise gr.Error("You need to judge whether the generated answer is correct first")
+
+        if not hasattr(self, 'clicked_correct_reference'):
+            raise gr.Error("You need to judge whether the highlighted reference is correct first")
+
+        if not hasattr(self, 'answer_df'):
+            raise gr.Error("You need to submit the document first")
+
+        if self.curret_question >= self.totel_question or self.curret_question < 0:
+            raise gr.Error("No more questions, please return back")
+
+        # record the answer
+        self.answer_df.loc[f'Question {self.curret_question + 1}', 'answer_correct'] = answer_correct
+        self.answer_df.loc[f'Question {self.curret_question + 1}', 'reference_correct'] = reference_correct
+
+        if self.clicked_correct_answer == True:
+            if hasattr(self, 'answer'):
+                self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_answer'] = self.answer
+            else:
+                raise gr.Error("You need to submit the document first")
+        else:
+            self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_answer'] = correct_answer
+
+        if self.clicked_correct_reference == True:
+            if hasattr(self, 'highlighted_out'):
+                self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_reference'] = self.highlighted_out
+            else:
+                raise gr.Error("You need to submit the document first")
+        else:
+            self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_reference'] = correct_reference
+
+        gr.Info('Results saved!')
+        return "Results saved!"
+
+    def process_next(self):
+        self.curret_question += 1
+        if hasattr(self, 'clicked_correct_answer'):
+            del self.clicked_correct_answer
+        if hasattr(self, 'clicked_correct_reference'):
+            del self.clicked_correct_reference
+
+        if self.curret_question >= self.totel_question:
+            # self.curret_question -= 1
+            return "No more questions!", "No more questions!", "No more questions!", 'No more questions!', 'No more questions!', 'Still need to click the button above to save the results', None, None
+        else:
+            res = self.gpt_result[f'Question {self.curret_question + 1}']
+            question = res['question']
+            self.answer = res['answer']
+            self.highlighted_out = res['original sentences']
+            highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
+            self.highlighted_out = '\n'.join(self.highlighted_out)
+            return question, self.answer, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None
+
+    def process_last(self):
+        self.curret_question -= 1
+
+        if hasattr(self, 'clicked_correct_answer'):
+            del self.clicked_correct_answer
+        if hasattr(self, 'clicked_correct_reference'):
+            del self.clicked_correct_reference
+
+        if self.curret_question < 0:
+            # self.curret_question += 1
+            return "No more questions!", "No more questions!", "No more questions!", 'No more questions!', 'No more questions!', 'Still need to click the button above to save the results', None, None
+        else:
+            res = self.gpt_result[f'Question {self.curret_question + 1}']
+            question = res['question']
+            self.answer = res['answer']
+            self.highlighted_out = res['original sentences']
+            highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
+            self.highlighted_out = '\n'.join(self.highlighted_out)
+            return question, self.answer, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None
+
+    def download_answer(self, path = './tmp', name = 'answer.xlsx'):
+        os.makedirs(path, exist_ok = True)
+        path = os.path.join(path, name)
+        self.ori_answer_df.to_excel(path, index = False)
+
+        return path
+
+    def download_corrected(self, path = './tmp', name = 'corrected_answer.xlsx'):
+        os.makedirs(path, exist_ok = True)
+        path = os.path.join(path, name)
+        self.answer_df.to_excel(path, index = False)
+
+        return path
+
+    def change_correct_answer(self, correctness):
+        if correctness == "Correct":
+            self.clicked_correct_answer = True
+            return "No need to change"
+        else:
+            if hasattr(self, 'answer'):
+                self.clicked_correct_answer = False
+                return self.answer
+            else:
+                return "No answer yet, you need to submit the document first"
+
+    def change_correct_reference(self, correctness):
+        if correctness == "Correct":
+            self.clicked_correct_reference = True
+            return "No need to change"
+        else:
+            if hasattr(self, 'highlighted_out'):
+                self.clicked_correct_reference = False
+                return self.highlighted_out
+            else:
+                return "No answer yet, you need to submit the document first"
+
+
+with gr.Blocks(theme="dark") as demo:
+    backend = Backend()
+    with gr.Row():
+        with gr.Row():
+            with gr.Group():
+                gr.Markdown(f'<center><h1>Input</h1></center>')
+                gr.Markdown(f'<center><p>Please First Upload the File</p></center>')
+
+                openai_key = gr.Textbox(
+                        label='Enter your OpenAI API key here',
+                        type='password')
+
+                file = gr.File(label='Upload your .txt file here', file_types=['.txt'])
+
+                questions = gr.CheckboxGroup(choices = QUESTIONS, value = QUESTIONS, label="Questions", info="Please select the question you want to ask")
+
+                btn_submit_txt = gr.Button(value='Submit txt')
+                btn_submit_txt.style(full_width=True)
+
+            with gr.Group():
+                gr.Markdown(f'<center><h1>Output</h1></center>')
+                gr.Markdown(f'<center><p>The answer to your question is :</p></center>')
+                question_box = gr.Textbox(label='Question')
+                answer_box = gr.Textbox(label='Answer')
+
+                highlighted_text = gr.outputs.HTML(label="Highlighted Text")
+                with gr.Row():
+                    btn_last_question = gr.Button(value='Last Question')
+                    btn_next_question = gr.Button(value='Next Question')
+
+            with gr.Group():
+                gr.Markdown(f'<center><h1>Correct the Result</h1></center>')
+                gr.Markdown(f'<center><p>Please Correct the Results</p></center>')
+
+                with gr.Row():
+                    save_results = gr.Textbox(placeholder = "Still need to click the button above to save the results", label = 'Save Results')
+                with gr.Group():
+                    gr.Markdown(f'<center><p>Please Choose: </p></center>')
+                    answer_correct = gr.Radio(choices = ["Correct", "Incorrect"], label='Is the Generated Answer Correct?', info="Pease select whether the generated text is correct")
+                    correct_answer = gr.Textbox(placeholder = "Please judge on the generated answer", label = 'Correct Answer', interactive = True)
+
+                    reference_correct = gr.Radio(choices = ["Correct", "Incorrect"], label="Is the Reference Correct?", info="Pease select whether the reference is correct")
+                    correct_reference = gr.Textbox(placeholder = "Please judge on the generated answer", label = 'Correct Reference', interactive = True)
+
+                    btn_submit_correctness = gr.Button(value='Submit Correctness')
+                    btn_submit_correctness.style(full_width=True)
+
+            with gr.Group():
+                gr.Markdown(f'<center><h1>Download</h1></center>')
+                gr.Markdown(f'<center><p>Download the processed data and corrected data</p></center>')
+                answer_file = gr.File(label='Download processed data', file_types=['.xlsx'])
+                btn_download_answer = gr.Button(value='Download processed data')
+                btn_download_answer.style(full_width=True)
+                corrected_file = gr.File(label='Download corrected data', file_types=['.xlsx'])
+                btn_download_corrected = gr.Button(value='Download corrected data')
+                btn_download_corrected.style(full_width=True)
+
+
+    with gr.Row():
+        reset = gr.Button(value='Reset')
+        reset.style(full_width=True)
+
+    # Answer change
+    answer_correct.input(
+        backend.change_correct_answer,
+        inputs = [answer_correct],
+        outputs = [correct_answer],
+    )
+
+    reference_correct.input(
+        backend.change_correct_reference,
+        inputs = [reference_correct],
+        outputs = [correct_reference],
+    )
+
+
+    # Submit button
+    btn_submit_txt.click(
+            backend.process_file,
+            inputs=[file, questions, openai_key],
+            outputs=[question_box, answer_box, highlighted_text, correct_answer, correct_reference],
+        )
+
+    btn_submit_correctness.click(   # TODO
+            backend.process_results,
+            inputs=[answer_correct, correct_answer, reference_correct, correct_reference],
+            outputs=[save_results],
+        )
+
+    # Switch question button
+    btn_last_question.click(
+            backend.process_last,
+            outputs=[question_box, answer_box, highlighted_text, correct_answer, correct_reference, save_results, answer_correct, reference_correct],
+        )
+
+    btn_next_question.click(
+            backend.process_next,
+            outputs=[question_box, answer_box, highlighted_text, correct_answer, correct_reference, save_results, answer_correct, reference_correct],
+        )
+
+    # Download button
+    btn_download_answer.click(
+            backend.download_answer,
+            outputs=[answer_file],
+        )
+
+    btn_download_corrected.click(
+            backend.download_corrected,
+            outputs=[corrected_file],
+        )
+demo.queue()
+demo.launch()