test persistent mount

app.py

@@ -1,32 +1,27 @@
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
 from Prediction import *
 import os
 from datetime import datetime
-
-
-# examples = []
-# if os.path.exists("assets/examples.txt"):
-#     with open("assets/examples.txt", "r", encoding="utf8") as file:
-#         for sentence in file:
-#             sentence = sentence.strip()
-#             examples.append(sentence)
-# else:
-examples = [
-    "Ends tonight! Shop select certifiably comfortable shoes!",
-    "Just Do it!",
-    "Don't miss our products!",
-    "What are some of your favorite jokes? Let us know!",
-    "Is anyone being creative with their snow day?",
-    "Did you see our latest movie?",
-    "In fact, we discovered that Woollip works better than what we imagined.",
-    "It is made of Titanium Grade 5, a material famous for being very strong yet very light.",
-    "Each game already comes with six characters.",
-    "We thank you personally for the trust you are putting in us and our company.",
-    "I wear it everyday and am very happy with it!",
-    "We are so grateful for our everyday heroes who never cease to amaze us!"
-]
+import re
+import json
+
+persistent_path = "/data"
+os.environ['HF_HOME'] = os.path.join(persistent_path, ".huggingface")
+user_input_path = os.path.join(persistent_path, 'user.jsonl')
+
+examples = []
+if os.path.exists("assets/examples.txt"):
+    with open("assets/examples.txt", "r", encoding="utf8") as file:
+        for sentence in file:
+            sentence = sentence.strip()
+            examples.append(sentence)
+else:
+    examples = [
+        "Games of the imagination teach us actions have consequences in a realm that can be reset.",
+        "But New Jersey farmers are retiring and all over the state, development continues to push out dwindling farmland.",
+        "He also is the Head Designer of The Design Trust so-to-speak, besides his regular job ..."
+        ]
 
 device = torch.device('cpu')
 tokenizer = BertTokenizer.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
@@ -51,6 +46,65 @@ def csv_process(csv_file, attr="content"):
     outputs.append(output_path)
     return outputs
 
+def check_save(fname, lname, cnum, email, oname, position):
+    errors = []
+    valid_vars = {}
+
+    if not fname.strip() or not lname.strip():
+        errors.append("Name cannot be empty")
+    elif fname.isdigit() or lname.isdigit():
+        errors.append("Name cannot be purely numerical")
+    else:
+        valid_vars["fname"] = fname
+        valid_vars["lname"] = lname
+
+    valid_vars["cnum"] = ''
+    if cnum:
+        if not cnum.isdigit():
+            errors.append("The phone number must be a pure number")
+        else:
+            valid_vars["cnum"] = cnum
+
+    if not email.strip():
+        errors.append("Email cannot be empty")
+    elif not re.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$', email):
+        errors.append("Incorrect email format")
+    else:
+        valid_vars["email"] = email
+
+    if not oname.strip():
+        errors.append("Organization name cannot be empty")
+    elif oname.isdigit():
+        errors.append("Organization cannot be purely numerical")
+    else:
+        valid_vars["oname"] = oname
+
+    valid_vars["position"] = ''
+    if position:
+        if position.isdigit():
+            errors.append("Position in your company cannot be purely numerical")
+        else:
+            valid_vars["position"] = position
+
+    if errors:
+        return errors
+    
+    current_time = datetime.now()
+    formatted_time = current_time.strftime("%Y_%m_%d_%H_%M_%S")
+    valid_vars['time'] = formatted_time
+
+    with open(user_input_path, 'a+', encoding="utf8") as file:
+        file.write(json.dumps(valid_vars)+"\n")
+
+    records = {}
+    with open(user_input_path, 'r', encoding="utf8") as file:
+        for line in file:
+            line = line.strip()
+            dct = json.loads(line)
+            records[dct['time']] = dct
+
+    return records
+
 
 my_theme = gr.Theme.from_hub("JohnSmith9982/small_and_pretty")
 with gr.Blocks(theme=my_theme, title='Brand_Tone_of_Voice_demo') as demo:
@@ -64,70 +118,114 @@ with gr.Blocks(theme=my_theme, title='Brand_Tone_of_Voice_demo') as demo:
             <h5 style="margin: 0;">If you like our project, please give us a star ✨ on Github for the latest update.</h5>
             <div style="display: flex; justify-content: center; align-items: center; text-align: center;>
                 <a href="https://arxiv.org/abs/xx.xx"><img src="https://img.shields.io/badge/Arxiv-xx.xx-red"></a>
-                <a href='https://huggingface.co/spaces/Oliver12315/Brand_Tone_of_Voice_Online_Demo'><img src='https://img.shields.io/badge/Project_Page-Oliver12315/Brand_Tone_of_Voice_Online_Demo' alt='Project Page'></a>
+                <a href='https://huggingface.co/spaces/Oliver12315/Brand_Tone_of_Voice_demo'><img src='https://img.shields.io/badge/Project_Page-Oliver12315/Brand_Tone_of_Voice_demo' alt='Project Page'></a>
                 <a href='https://github.com'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
             </div>
         </div>
         </div>
         """)
 
-    with gr.Tab("Single Sentence"):
-        with gr.Row():
-            tbox_input = gr.Textbox(label="Input",
-                                    info="Please input a sentence here:")
-            gr.Markdown("""
-                # Detailed information about our model:
+
+    debug_tb = gr.Textbox(label="Persistent: ", type='text')
+    with gr.Column(visible=True) as regis:
+        gr.Markdown("# Welcome to BTV!  Please fill out the form below to continue.\nI’m assuming that you mention somewhere that this project/research is conducted by the University of Manchester/AMBS. By ticking this box, I consent to be approached by the research team of the University of Manchester.")
+        with gr.Column(variant='panel'):   
+            fname_tb = gr.Textbox(label="First Name: ", type='text')
+            lname_tb = gr.Textbox(label="Last Name: ", type='text')
+            email_tb = gr.Textbox(label="Email: ", type='email')
+            cnum_tb = gr.Textbox(label="Contact: (Optional)", type='text')
+            oname_tb = gr.Textbox(label="Organization name: ", type='text')
+            position_tb = gr.Textbox(label="Positions in your company: (Optional)", type='text')
+        error_box = gr.HTML(value="", visible=False)
+        submit_btn = gr.Button("Click here to start if you have fullfill all the item!")
+
+    with gr.Row(visible=False) as mainrow:
+
+        with gr.Tab("Single Sentence"):
+            with gr.Row():
+                tbox_input = gr.Textbox(label="Input",
+                                        info="Please input a sentence here:")
+                gr.Markdown("""
+                    # Detailed information about our model:
+                    ...
+                    """)
+            tab_output = gr.DataFrame(label='Predictions:', 
+                                    headers=["Label", "Probability"],
+                                    datatype=["str", "number"],
+                                    interactive=False)
+            with gr.Row():
+                button_ss = gr.Button("Submit", variant="primary", visible=False)
+                button_ss.click(fn=single_sentence, inputs=[tbox_input], outputs=[tab_output])
+                gr.ClearButton([tbox_input, tab_output])
+
+            gr.Examples(
+                examples=examples,
+                inputs=tbox_input,
+                examples_per_page=len(examples)
+            )
+
+        with gr.Tab("Csv File"):
+            with gr.Row():
+                csv_input = gr.File(label="CSV File:",
+                                    file_types=['.csv'],
+                                    file_count="single"
+                                    )
+                csv_output = gr.File(label="Predictions:")
+
+            with gr.Row():
+                button_cf = gr.Button("Submit", variant="primary", visible=False)
+                button_cf.click(fn=csv_process, inputs=[csv_input], outputs=[csv_output])
+                gr.ClearButton([csv_input, csv_output])
+
+            gr.Markdown("## Examples \n The incoming CSV must include the ``content`` field, which represents the text that needs to be predicted!")
+            gr.DataFrame(label='Csv input format:',
+                        value=[[i, examples[i]] for i in range(len(examples))],
+                        headers=["index", "content"],
+                        datatype=["number","str"],
+                        interactive=False
+                        )
+
+        with gr.Tab("Readme"):
+            gr.Markdown(
+                """
+                # Paper Name
+
+                # Authors
+
+                + First author
+                + Corresponding author
+                
+                # Detailed Information
+
                 ...
-                """)
-        tab_output = gr.DataFrame(label='Predictions:', 
-                                  headers=["Label", "Probability"],
-                                  datatype=["str", "number"],
-                                  interactive=False)
-        with gr.Row():
-            button_ss = gr.Button("Submit", variant="primary")
-            button_ss.click(fn=single_sentence, inputs=[tbox_input], outputs=[tab_output])
-            gr.ClearButton([tbox_input, tab_output])
-
-        gr.Examples(
-            examples=examples,
-            inputs=tbox_input,
-            examples_per_page=len(examples)
-        )
-
-    with gr.Tab("CSV File"):
-        with gr.Row():
-            csv_input = gr.File(label="CSV File:",
-                                file_types=['.csv'],
-                                file_count="single"
-                                )
-            csv_output = gr.File(label="Predictions:")
-
-        with gr.Row():
-            button = gr.Button("Submit", variant="primary")
-            button.click(fn=csv_process, inputs=[csv_input], outputs=[csv_output])
-            gr.ClearButton([csv_input, csv_output])
-
-        gr.Markdown("## Examples \n The incoming CSV must include the ``content`` field, which represents the text that needs to be predicted!")
-        gr.DataFrame(label='Csv input format:',
-                    value=[[i, examples[i]] for i in range(len(examples))],
-                    headers=["index", "content"],
-                    datatype=["number","str"],
-                    interactive=False
-                    )
-
-    with gr.Tab("Readme"):
-        gr.Markdown(
-            """
-            # Paper Name
-
-            # Authors
-
-            + First author
-            + Corresponding author
-            
-            # Detailed Information
-
-            ...
-            """
-        )
+                """
+            )
+
+    def submit(*user_input):
+        res = check_save(*user_input)
+        if isinstance(res, list):
+            return {
+                error_box: gr.HTML(
+                    value=f"""
+                    <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+                    <div>
+                        <p style="color:red;">{"; ".join(res)}</p>
+                    </div>
+                    </div>
+                    """, 
+                    visible=True)
+            }
+        else:
+            return {
+                mainrow: gr.Row(visible=True),
+                regis: gr.Row(visible=False),
+                error_box: gr.HTML(visible=False),
+                debug_tb: gr.Textbox(value=json.dumps(res), type='text', visible=True)
+            }
+
+    submit_btn.click(
+        submit,
+        [fname_tb, lname_tb, cnum_tb, email_tb, oname_tb, position_tb],
+        [mainrow, regis, error_box, debug_tb],
+    )
 demo.launch()