init

app.py

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+import gradio as gr
+
+
+with gr.Blocks() as demo:
+    backend = Backend()
+    with gr.Row():
+        gr.Markdown(f'<center> <h1> <b> DAN_PDF_CHAT </b> </h1> </center>')
+    with gr.Row():
+        with gr.Column(scale = 0.5):
+            with gr.Group():
+                gr.Markdown(f'<center> <h3> <b> Setup for the Agent </b> </h3> </center>')
+                openai_key = gr.Textbox(
+                        label='Enter your OpenAI API key here',
+                        type='password')
+                assistant_id = gr.Textbox(
+                        label='Enter the OpenAI assistant ID here, or you can use the default one',
+                        value = 'asst_FXsUUX2RacJ5GxEs6sCXL7nY',
+                        type = 'password',
+                        ) 
+            with gr.Group():
+                gr.Markdown(f'<center> <h3> <b> Setup for the User </b> </h3> </center>')
+                file = gr.File(label='Upload your .txt or .pdf file here', file_types=['.txt', '.pdf'], file_count = 'single')
+                btn_submit_txt_online = gr.Button(value='Submit passage')
+                
+        with gr.Column(scale=1):
+            # with gr.Group():
+            chatbot = gr.Chatbot(show_copy_button = True)
+            question_box = gr.Textbox(label='Enter your question here',
+                                    placeholder = 'What is the animal mentioned in this passage?',
+                                    value = 'What is the animal mentioned in this passage?'
+            )
+            with gr.Row():
+                btn_submit_question_txt = gr.Button(value='Submit')
+                btn_reset_question_txt = gr.Button(value='Reset')
+                btn_show_html = gr.Button(value='Show reference')
+                btn_hide_html = gr.Button(value='Hide reference')
+
+    with gr.Row():
+        html = gr.HTML(visible = False, label='HTML', value='<h1> References would be shown HERE.</h1>')
+
+    btn_submit_txt_online.click(
+        fn = backend.submit_passage,
+        inputs = [openai_key, assistant_id, file],
+    )
+
+    btn_submit_question_txt.click(
+        fn = backend.submit_question,
+        inputs = [question_box],
+        # outputs = [chatbot],
+        outputs = [chatbot, html],
+    )
+    
+    btn_show_html.click(
+        fn = lambda:  gr.update(visible=True),
+        outputs=html,
+    )
+    btn_hide_html.click(
+        fn = lambda:  gr.update(visible=False),
+        outputs=html,
+    )
+
+demo.queue()
+demo.launch(show_error=True)

backend.py

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+import time
+import json
+from openai import OpenAI
+
+class Backend:
+    def __init__(self):
+        self.return_instruction = """  Please only return in the following Json format:
+        {{
+        "Answer": "",
+        "Reference Sentences": [""]
+        }}"""
+
+        self.chat_history = []
+
+    def load_agent(self, openai_api_key, assistant_id):
+        client = OpenAI(api_key=openai_api_key)
+        assistant = client.beta.assistants.retrieve(assistant_id=assistant_id)
+        return client, assistant
+
+    def update_file(self, file_path):
+        file = open(file_path, 'rb')
+        file = self.client.files.create(file=file, purpose='assistants')
+        return file
+
+    def create_thread(self):
+        thread = self.client.beta.threads.create()
+        return thread
+
+    def delate_thread(self, thread):
+        self.client.beta.threads.delete(thread.id)
+
+    def create_message(self, question, thread, file):
+        message = self.client.beta.threads.messages.create(
+            thread_id=thread.id,
+            role="user",
+            content= question + self.return_instruction,
+            file_ids=[file.id]
+        )
+        return message
+
+    def delate_message(self, message):
+        self.client.beta.threads.messages.delete(message.id)
+
+    def create_run(self, thread, assistant):
+        run = self.client.beta.threads.runs.create(
+            thread_id=thread.id,
+            assistant_id=assistant.id,
+            # instructions="""Please read PDF and answer the qusetions asked by users with professional knowledge."""
+        )
+        return run
+    
+    def delate_run(self, run):
+        self.client.beta.threads.runs.delete(run.id)
+
+    def get_massage(self, thread):
+        messages = self.client.beta.threads.messages.list(
+            thread_id=thread.id
+        )
+        return messages
+
+    def phrase_massage(self, question, messages):
+        mess = json.loads(messages.json())
+        output = mess['data'][0]['content'][0]['text']['value']
+        print(output)
+        try:
+            output = output.split("{")[1:]
+            output = "{" + "".join(output)
+            output = output.split("}")[:-1]
+            output = "".join(output) + "}"   
+            print(output)    
+            output = eval(output)
+            answer = output['Answer']
+            reference = output['Reference Sentences']
+        except:
+            self.detete_message(message)
+            answer = output
+            reference = []
+        reference = ' '.join(reference)
+        reference = self.processing_html(reference)
+        self.chat_history.append([question, answer])
+        
+        return self.chat_history, reference
+
+    def phrase_massage_1(self, question, messages):
+        mess = json.loads(messages.json())
+        output = mess['data'][0]['content'][0]['text']['value']
+        
+        self.chat_history.append([question, output])
+        
+        return self.chat_history
+
+    def processing_html(self, text):
+        return f'<center><p> {text} </p></center>'
+
+    def submit_passage(self, openai_key, assistant_id, file):
+        # Create a new conversation
+        self.client, self.assistant = self.load_agent(openai_key, assistant_id)
+
+        # Update file
+        self.file = self.update_file(file.name)
+        
+        # Create a new conversation
+        self.thread = self.create_thread()
+
+        gr.Info("Upload successful. Please can now chat with the assistant. Enjoy!")
+
+    def submit_question(self, question):
+        # print(question)
+        # print(self.thread.id)
+        # print(self.file.id)
+        # Create a new message
+        self.message = self.create_message(question, self.thread, self.file)
+
+        # Create a new run
+        run = self.create_run(self.thread, self.assistant)
+
+        # Wait for the run to complete
+        while True:
+            run = self.client.beta.threads.runs.retrieve(thread_id=self.thread.id, run_id=run.id)
+            if run.status not in ["queued", "in_progress"]:
+                break
+            time.sleep(1)
+        
+        # Get the answer
+        messages = self.get_massage(self.thread)
+        answer, reference = self.phrase_massage(question, messages)
+
+        return answer, reference
+
+    def submit_question_another(self, question):
+        # Create a new message
+        self.message = self.create_message(question, self.thread, self.file)
+
+        # Create a new run
+        run = self.create_run(self.thread, self.assistant)
+
+        # Wait for the run to complete
+        while True:
+            run = self.client.beta.threads.runs.retrieve(thread_id=self.thread.id, run_id=run.id)
+            if run.status not in ["queued", "in_progress"]:
+                break
+            time.sleep(1)
+        
+        # Get the answer
+        messages = self.get_massage(self.thread)
+        answer = self.phrase_massage_1(question, messages)
+
+        return answer

requirements.txt

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+openai