Create app.py

app.py

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+import gradio as gr
+from bs4 import BeautifulSoup
+import requests
+from acogsphere import acf
+from bcogsphere import bcf
+import math
+import glob
+#from python_actr import *
+#from cogscidighum import *
+
+
+#class myCelSci(Model):
+#    pass
+    
+#def main(link):
+#    response=getviews(link)+getresult("hello world")[0]["label"] + str(math.trunc(getresult("hello world")[0]["score"])*100/100)
+#    return  response #result #soup.prettify()
+
+
+
+import sqlite3
+import huggingface_hub
+import pandas as pd
+import shutil
+import os
+import datetime
+from apscheduler.schedulers.background import BackgroundScheduler
+
+import random
+import time
+
+DB_FILE = "./reviews.db"
+
+TOKEN = os.environ.get('HF_KEY')
+
+repo = huggingface_hub.Repository(
+    local_dir="data",
+    repo_type="dataset",
+    clone_from="CognitiveScience/csdhdata",
+    use_auth_token=TOKEN
+)
+repo.git_pull()
+
+# Set db to latest
+shutil.copyfile("./data/reviews.db", DB_FILE)
+
+# Create table if it doesn't already exist
+
+db = sqlite3.connect(DB_FILE)
+try:
+    db.execute("SELECT * FROM reviews").fetchall()
+    db.close()
+except sqlite3.OperationalError:
+    db.execute(
+        '''
+        CREATE TABLE reviews (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
+                              created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,
+                              name TEXT, review INTEGER, comments TEXT)
+        ''')
+    db.commit()
+    db.close()
+
+
+def get_latest_reviews(db: sqlite3.Connection):
+    reviews = db.execute("SELECT * FROM reviews ORDER BY id DESC limit 10").fetchall()
+    total_reviews = db.execute("Select COUNT(id) from reviews").fetchone()[0]
+    reviews = pd.DataFrame(reviews, columns=["id", "date_created", "name", "review", "comments"])
+    return reviews, total_reviews
+
+
+def add_review(name: str, review: int, comments: str):
+    db = sqlite3.connect(DB_FILE)
+    cursor = db.cursor()
+    cursor.execute("INSERT INTO reviews(name, review, comments) VALUES(?,?,?)", [name, review, comments])
+    db.commit()
+    reviews, total_reviews = get_latest_reviews(db)
+    db.close()
+    return reviews, total_reviews
+
+def load_data():
+    db = sqlite3.connect(DB_FILE)
+    reviews, total_reviews = get_latest_reviews(db)
+    db.close()
+    return reviews, total_reviews
+    
+def delete_review(id: int):
+    db = sqlite3.connect(DB_FILE)
+    cursor = db.cursor()
+    cursor.execute("DELETE FROM reviews WHERE id = ?", [id])
+    db.commit()
+    reviews, total_reviews = get_latest_reviews(db)
+    db.close()
+    return reviews, total_reviews
+    
+def delete_all_reviews():
+    db = sqlite3.connect(DB_FILE)
+    cursor = db.cursor()
+    cursor.execute("DELETE FROM reviews")
+    db.commit()
+    reviews, total_reviews = get_latest_reviews(db)
+    db.close()
+    return reviews, total_reviews
+#def cs(link):
+#    response="Hi " + "bcf" #(link) #acf("hello world")[0]["label"] + str(math.trunc(acf("hello world")[0]["score"])*100/100)+bcf(link)
+#    return  response #result #soup.prettify()
+
+def respond3(message, chat_history):
+    bot_message = random.choice(["How are you3?", "I love you3", "I'm very hungry3"])
+    chat_history.append((message, bot_message))
+    time.sleep(2)
+    
+    return "", chat_history
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            with gr.Box():
+                gr.Markdown("Based on dataset [here](https://huggingface.co/datasets/freddyaboulton/gradio-reviews)")
+                #data = gr.Dataframe()
+                count = gr.Number(label="Total number of reviews")
+                name = gr.Textbox(label="Name", placeholder="ur name?")
+                review = gr.Radio(label="How satisfied are you with your pick?", choices=[1, 2, 3, 4, 5, 6])
+                comments = gr.Textbox(label="Comments0", lines=10, placeholder="comm?")
+                cssubmit = gr.Button(value="Submit Choice")
+                #cschatbot = gr.Chatbot()
+                #csinp = gr.Textbox()
+                #csout=cs(csinp)
+                #csclear = gr.ClearButton([csinp, cschatbot])
+                
+                #csinp.submit(cs, [csinp, cschatbot], [csinp, cschatbot])
+                
+                def cs(link):
+                    response="Hi " + link #(link) #acf("hello world")[0]["label"] + str(math.trunc(acf("hello world")[0]["score"])*100/100)+bcf(link)
+                    return  response,1 #result #soup.prettify()
+                cssubmit.click(cs, name, [comments,count])
+            
+    with gr.Row():
+        with gr.Column():            
+                name = gr.Textbox(label="Name", placeholder="What is your name?")
+                review = gr.Radio(label="How satisfied are you with using gradio?", choices=[1, 2, 3, 4, 5])
+                comments = gr.Textbox(label="Comments", lines=10, placeholder="Do you have any feedback on gradio?")
+                submit = gr.Button(value="Submit Feedback")
+        with gr.Column():
+            gr.FileExplorer(label="Working directory")
+            gr.FileExplorer(root="./data", label="Persistent storage")
+            
+        with gr.Column():
+                chatbot = gr.Chatbot()
+                msg = gr.Textbox()
+                clear = gr.ClearButton([msg, chatbot])
+            
+                def respond(message, chat_history):
+                    bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
+                    chat_history.append((message, bot_message))
+                    time.sleep(2)
+                    
+                    return "", chat_history
+            
+                msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+        with gr.Column():
+                submitsave = gr.Button(value="Save")
+            
+                def backup_db2():
+                    shutil.copyfile(DB_FILE, "./data/reviews.db")
+                    db = sqlite3.connect(DB_FILE)
+                    reviews = db.execute("SELECT * FROM reviews").fetchall()
+                    pd.DataFrame(reviews).to_csv("./data/reviews.csv", index=False)
+                    print("updating db")
+                    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}")
+                submit.click(backup_db2)
+        with gr.Column():
+            with gr.Box():
+                gr.Code(
+                value="""def hello_world():
+            return "Hello, world!"
+            
+        print(hello_world())""",
+                language="python",
+                interactive=True,
+                show_label=False,
+            )
+                gr.Markdown("Based on dataset [here](https://huggingface.co/datasets/freddyaboulton/gradio-reviews)")
+                data = gr.Dataframe()
+            count = gr.Number(label="Total number of reviews")
+            
+    submit.click(add_review, [name, review, comments], [data, count])
+    
+    #cssubmit.click(add_review, [name, review, comments], [data, count])
+
+    record2del = gr.Textbox(label="Id: ", lines=1, placeholder="to delete?")
+
+    submit2 = gr.Button(value="Delete Review")
+    id_input = gr.Number(value=202, visible=False)
+    submit2.click(delete_review, id_input)
+
+
+
+    submit3 = gr.Button(value="Delete All Reviews")
+    submit3.click(delete_all_reviews)
+
+
+    demo.load(load_data, None, [data, count])
+
+def backup_db():
+    shutil.copyfile(DB_FILE, "./data/reviews.db")
+    db = sqlite3.connect(DB_FILE)
+    reviews = db.execute("SELECT * FROM reviews").fetchall()
+    pd.DataFrame(reviews).to_csv("./data/reviews.csv", index=False)
+    print("updating db")
+    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}")
+
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(func=backup_db, trigger="interval", seconds=60)
+scheduler.start()
+
+
+demo.launch()