Update ArcanaUI2.py

ArcanaUI2.py

@@ -6,6 +6,9 @@ import os
 import shutil
 from datetime import datetime
 import Arcana
+from nylon import *
+import pandas as pd
+import json
 
 # SSL configuration to avoid verification issues
 try:
@@ -15,6 +18,24 @@ except AttributeError:
 else:
     ssl._create_default_https_context = _create_unverified_https_context
 
+def query_database2(query):
+    db = ChatDatabase('memory.txt')
+
+    sender = 'Arcana'
+    N = 10
+    cache = {}
+    query_tag = None
+
+    relevant_messages = db.get_relevant_messages(sender, query, N, cache, query_tag)
+
+    print("Relevant messages:")
+    for message in relevant_messages:
+        print(f"Sender: {message[0]}, Time: {message[1]}, Tag: {message[3]}")
+        print(f"Message: {message[2][:100]}...")
+        print()
+
+    df_data = [str(message) for message in relevant_messages]
+    return ';'.join(df_data)
 
 # OpenAI client setup
 client = OpenAI(
@@ -22,20 +43,71 @@ client = OpenAI(
     api_key='sk-Nxf8HmLpfIMhCd83n3TOr00TR57uBZ0jMbAgGCOzppXvlsx1',
 )
 
+# Function list for OpenAI API
+function_list = [
+    {
+        "name": "query_database",
+        "description": "Query the database and return a list of results as strings",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "The query to execute against the database"
+                },
+            },
+            "required": ["query"]
+        }
+    }
+]
+
+# Mapping of function names to actual function objects
+function_map = {
+    "query_database": query_database2
+}
+
+def execute_function(function_name, function_args):
+    if function_name in function_map:
+        return function_map[function_name](**function_args)
+    else:
+        return f"Error: Function {function_name} not found"
+
 # Retry logic for OpenAI API call
 def openai_api_call(messages, retries=3, delay=5):
     for attempt in range(retries):
         try:
             completion = client.chat.completions.create(
-                model="gpt-4o",
+                model="gpt-3.5-turbo",  # Changed from "gpt-4o" to "gpt-4"
                 messages=messages,
+                functions=function_list,
+                function_call='auto',
                 timeout=10
             )
-            return completion.choices[0].message.content
+            response_message = completion.choices[0].message
+
+            # Check if the model wants to call a function
+            if response_message.function_call:
+                function_name = response_message.function_call.name
+                function_args = json.loads(response_message.function_call.arguments)
+                function_response = execute_function(function_name, function_args)
+                # Add the function response to the conversation
+                messages.append(response_message.model_dump())  # The model's request to call the function
+                messages.append({
+                    "role": "function",
+                    "name": function_name,
+                    "content": json.dumps(function_response)
+                })
+                # Make a follow-up call to the model with the function response
+                return openai_api_call(messages)
+            else:
+                return response_message.content
+
         except Exception as e:
             print(f"Attempt {attempt + 1} failed: {e}")
-            time.sleep(delay)
-    return "Sorry, I am having trouble connecting to the server. Please try again later."
+            if attempt < retries - 1:
+                time.sleep(delay)
+            else:
+                return "Sorry, I am having trouble connecting to the server. Please try again later."
 
 # Chatbot response function
 def chatbot_response(message, history):
@@ -53,13 +125,33 @@ Your primary goal is to provide students with the most helpful and accurate stud
 
 selected = None
 
-def upload_file(file):
-    foldername = 'cache'
-    if not os.path.exists(foldername):
-        os.mkdir(foldername)
-    file_path = os.path.join(foldername, os.path.basename(file.name))
-    shutil.copy(file.name, file_path)
-    return list_uploaded_files()
+from concurrent.futures import ThreadPoolExecutor
+
+# Function to handle the file upload
+def handle_file_upload(file):
+    # Ensure the cache2 directory exists
+    cache_dir = 'cache'
+    os.makedirs(cache_dir, exist_ok=True)
+    
+    # Get the uploaded file path
+    file_path = file.name
+    
+    # Define the new path for the uploaded file
+    new_file_path = os.path.join(cache_dir, os.path.basename(file_path))
+    
+    # Move the file to the cache2 directory
+    shutil.move(file_path, new_file_path)
+    
+    # Get the file size
+    file_size = os.path.getsize(new_file_path)
+    
+    return f"File saved to {new_file_path} with size: {file_size} bytes"
+
+# Wrapper function to run the file upload in a thread
+def handle_file_upload_threaded(file):
+    with ThreadPoolExecutor() as executor:
+        future = executor.submit(handle_file_upload, file)
+        return future.result()
 
 def list_uploaded_files():
     foldername = 'cache'
@@ -123,7 +215,9 @@ def display_file(evt: gr.SelectData, df):
 def render_to_database():
     # This function is undefined as per your request
     Arcana.main()
-    
+
+def change_theme(theme):
+    gr.Interface.theme = theme
 
 def rename_file(new_name):
     global selected
@@ -138,71 +232,95 @@ def rename_file(new_name):
             return list_uploaded_files(), f"File {selected} not found", None, "", ""
     return list_uploaded_files(), "No file selected or new name not provided", None, "", ""
 
+def query_database(query):
+    # Usage example
+    db = ChatDatabase('memory.txt')
+
+    # Example 1: Get relevant messages
+    sender = 'Arcana'
+    N = 10
+    cache = {}
+    query_tag = None
+
+    relevant_messages = db.get_relevant_messages(sender, query, N, cache, query_tag)
+
+    print("Relevant messages:")
+    for message in relevant_messages:
+        print(f"Sender: {message[0]}, Time: {message[1]}, Tag: {message[3]}")
+        print(f"Message: {message[2][:100]}...")
+        print()
+
+    df_data = [{"Nylon Returned Query": str(message)} for message in relevant_messages]
+    
+    # Create a pandas DataFrame
+    df = pd.DataFrame(df_data)
+    
+    return df
+
+    
+example_database = [
+    "What is Hydrogen Bonding?",
+    "Tell me the difference between impulse and force.",
+    "Tell me a joke that Calculus students will understand.",
+    "How should I review for the AP Biology Exam?",
+    "What kind of resources are available in PA and Indexademics?",
+    "What is the StandardCAS™ group?",
+    "Explain the concept of quantum entanglement.",
+    "What are the main differences between mitosis and meiosis?",
+    "How does the Doppler effect work?",
+    "Explain the process of photosynthesis.",
+    "What is the significance of the Pythagorean theorem?",
+    "How does natural selection contribute to evolution?",
+    "What is the most important chapter in AP Statistics?",
+    "How should I prepare on the IB Chinese Exam?"
+]
+
+import random
+
+def get_random_examples(num_examples=5):
+    return random.sample(example_database, min(num_examples, len(example_database)))
+
 # Create the Gradio interface for the chatbot
 chatbot_interface = gr.ChatInterface(
     chatbot_response,
     chatbot=gr.Chatbot(height=400),
-    textbox=gr.Textbox(placeholder="Type your message here...", container=True, scale=100),
+    textbox=gr.Textbox(placeholder="Type your message here...", container=True, scale=10),
     title="Review With Arcana",
-    description="ArcanaUI v0.7 - Chatbot",
+    description="ArcanaUI v0.8 - Chatbot",
     theme="soft",
-    examples=[
-        "What is Hydrogen Bonding?",
-        "Tell me the difference between impulse and force.",
-        "Tell me a joke that Calculus students will know.",
-        "How should I review for the AP Biology Exam?",
-        "What kind of resources are available in PA and Indexademics?",
-        "What is the StandardCAS™ group?"
-    ],
+    examples=get_random_examples(),
     cache_examples=False,
     retry_btn=None,
     undo_btn="Delete Previous",
     clear_btn="Clear"
 )
 
+
+def relaunch():
+    global demo
+    demo.close()
+    demo.launch(share=True)
+    
 # Combine the interfaces using Tabs
-with gr.Blocks() as demo:
-    gr.Markdown("# ArcanaUI v0.7")
+with gr.Blocks(js="""
+async () => {
+    const originalFetch = window.fetch;
+    window.fetch = (url, options) => {
+        if (options && options.signal) {
+            const controller = new AbortController();
+            options.signal = controller.signal;
+            setTimeout(() => controller.abort(), 3600000);  // 300000 ms = 5 minutes
+        }
+        return originalFetch(url, options);
+    };
+}
+""") as demo:
+    gr.Markdown("# ArcanaUI v0.8")
     with gr.Tabs():
         with gr.TabItem("Welcome Page"):
-            gr.Markdown("""
-            # Welcome to ArcanaUI v0.7 by the Indexademics Team
-            Program Base Powered by StandardCAS™
-            ## Introduction
-            Welcome to Arcana, your dynamic study resource database! Our goal is to help students like you excel in your exams by providing quick and accurate answers to your study questions.
-
-            ## How to Use
-            - Navigate to the 'Chatbot' tab to ask your study-related questions.
-            - Type your question into the textbox and press Enter.
-            - The chatbot will respond with helpful information.
-            - Use the 'Delete Previous' button to remove the last interaction or 'Clear' to reset the chat.
-
-            ## Works Cited
-            Below is a sample citation in BibTeX format:
-            ```
-            @article{Fan2023CELSIA,
-              title={CELSIA-Nylon},
-              author={Chengjui Fan},
-              journal={Conf-MLA 2023},
-              year={2023},
-              volume={NAN},
-              number={NAN},
-              pages={NAN},
-              publisher={Conf-MLA}
-            }
-
-            @misc{Indexademics,
-              title={indexademics Chatbot},
-              author={NAN},
-              journal={SHSID},
-              year={2024},
-              volume={NAN},
-              number={NAN},
-              pages={NAN},
-              publisher={Peer Advisor(PA) SHSID}
-            }
-            ```
-            """)
+            with open('introduction.txt',mode='r') as file:
+                intro_content = file.read()
+            gr.Markdown(intro_content)
 
         with gr.TabItem("Chatbot"):
             chatbot_interface.render()
@@ -212,17 +330,22 @@ with gr.Blocks() as demo:
             gr.Markdown('# Upload and View Files')
             
             with gr.Row():
+
                 # Left column: File list and buttons
                 with gr.Column(scale=1):
+                    gr.Markdown("## Upload File")
+                    file_input = gr.File(label="Upload your file here", file_types=["pdf", "jpeg", "jpg", "gif", "docx"])
+                    file_input.change(handle_file_upload_threaded, inputs=file_input)
+                    
                     uploaded_files_list = gr.DataFrame(headers=["Uploaded Files"], datatype="str", interactive=False)
                     
                     with gr.Row():
-                        upload_button = gr.UploadButton('Upload File')
                         refresh_button = gr.Button('Refresh')
                         delete_button = gr.Button('Delete Selected File')
 
+
                 # Right column: File viewer and Image viewer
-                with gr.Column(scale=1):
+                with gr.Column(scale=1):                    
                     with gr.Tab("File  Viewer"):
                         file_viewer = gr.File(label="File Restore")
                         file_status = gr.Textbox(label="File Status", interactive=False)
@@ -236,10 +359,11 @@ with gr.Blocks() as demo:
                     
                     with gr.Tab("Image Viewer"):
                         image_viewer = gr.Image(label="Image Viewer", type="filepath")
+
+                        
                         
             # Event handlers
             refresh_button.click(fn=refresh_files, outputs=uploaded_files_list)
-            upload_button.upload(upload_file, inputs=upload_button, outputs=uploaded_files_list)
             delete_button.click(fn=delete_file, outputs=[uploaded_files_list, file_viewer, file_status, file_size, file_creation_time])
             uploaded_files_list.select(fn=display_file, inputs=uploaded_files_list, outputs=[file_viewer, image_viewer, file_status])
             uploaded_files_list.select(fn=on_select, outputs=[file_viewer, file_status, file_size, file_creation_time])
@@ -250,5 +374,29 @@ with gr.Blocks() as demo:
             render_button = gr.Button("Render all PDFs to Database")
             render_button.click(fn=render_to_database)
 
+        with gr.TabItem('Settings'):
+            with gr.TabItem('Database'):
+                gr.Markdown('Settings')
+
+                test_nylon = gr.Textbox(label='Test Nylon', placeholder='Query')
+                uploaded_files_list2 = gr.DataFrame(headers=["Nylon Returned Query"], datatype="str", interactive=False)
+                
+                query_button = gr.Button('Query')
+                
+                query_button.click(fn=query_database, inputs=test_nylon, outputs=uploaded_files_list2)
+            with gr.TabItem('Theme'):
+                gr.Markdown('Change Theme')
+
+                theme_dropdown = gr.Dropdown(choices=['default', 'compact', 'huggingface', 'soft', 'dark'], label='Choose Theme')
+                theme_button = gr.Button('Apply Theme')
+
+                theme_button.click(fn=change_theme, inputs=theme_dropdown)
+                relaunch_button = gr.Button('Relaunch')
+                relaunch_button.click(fn=relaunch)
+
+
 # Launch the interface
 demo.launch(share=True)
+
+
+