Update

app-ngrok.py

@@ -5,6 +5,7 @@ import requests
 from time import sleep
 import re
 import platform
+import pyperclip
 # Additional Firebase imports
 import firebase_admin
 from firebase_admin import credentials, firestore
@@ -23,6 +24,7 @@ initial_model = "WizardLM/WizardCoder-15B-V1.0"
 lora_model = "richardr1126/spider-skeleton-wizard-coder-qlora"
 dataset = "richardr1126/spider-skeleton-context-instruct"
 
+# Firebase code
 # Initialize Firebase
 base64_string = os.getenv('FIREBASE')
 base64_bytes = base64_string.encode('utf-8')
@@ -36,18 +38,53 @@ cred = credentials.Certificate(firebase_auth)
 firebase_admin.initialize_app(cred)
 db = firestore.client()
 
-def log_to_firestore(input_message, db_info, temperature, response_text):
+def log_message_to_firestore(input_message, db_info, temperature, response_text):
     doc_ref = db.collection('logs').document()
     log_data = {
         'timestamp': firestore.SERVER_TIMESTAMP,
         'temperature': temperature,
         'db_info': db_info,
         'input': input_message,
-        'output': response_text
+        'output': response_text,
     }
     doc_ref.set(log_data)
+
+rated_outputs = set()  # set to store already rated outputs
+
+def log_rating_to_firestore(input_message, db_info, temperature, response_text, rating):
+    global rated_outputs
+    output_id = f"{input_message} {db_info} {response_text} {temperature}"
+
+    if output_id in rated_outputs:
+        gr.Warning("You've already rated this output!")
+        return
+    if not input_message or not db_info or not response_text or not rating:
+        gr.Info("You haven't asked a question yet! Or the output box is empty.")
+        return
+    
+    rated_outputs.add(output_id)
+
+    doc_ref = db.collection('ratings').document()
+    log_data = {
+        'timestamp': firestore.SERVER_TIMESTAMP,
+        'temperature': temperature,
+        'db_info': db_info,
+        'input': input_message,
+        'output': response_text,
+        'rating': rating,
+    }
+    doc_ref.set(log_data)
+    gr.Info("Thanks for your feedback!")
 # End Firebase code
 
+def copy_to_clipboard(text):
+    # Copy to clipboard
+    try:
+        pyperclip.copy(text)
+        gr.Info("Copied to clipboard!")
+    except Exception:
+        gr.Warning("Couldn't copy to clipboard :(")
+
 def format(text):
     # Split the text by "|", and get the last element in the list which should be the final query
     try:
@@ -71,7 +108,7 @@ def generate(input_message: str, db_info="", temperature=0.2, top_p=0.9, top_k=0
     # Format the user's input message
     messages = f"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n\nConvert text to sql: {input_message} {db_info}\n\n### Response:\n\n"
 
-    url = "https://e9f4be879d38-8269039109365193683.ngrok-free.app/api/v1/generate"
+    url = os.getenv("KOBOLDCPP_API_URL")
     stop_sequence = stop_sequence.split(",")
     stop = ["###"] + stop_sequence
     payload = {
@@ -104,7 +141,7 @@ def generate(input_message: str, db_info="", temperature=0.2, top_p=0.9, top_k=0
 
             if log:
                 # Log the request to Firestore
-                log_to_firestore(input_message, db_info, temperature, output if format_sql else response_text)
+                log_message_to_firestore(input_message, db_info, temperature, output if format_sql else response_text)
 
             return output
 
@@ -120,16 +157,24 @@ with gr.Blocks(theme='gradio/soft') as demo:
     header = gr.HTML("""
         <h1 style="text-align: center">SQL Skeleton WizardCoder Demo</h1>
         <h3 style="text-align: center">🕷️☠️🧙‍♂️ Generate SQL queries from Natural Language 🕷️☠️🧙‍♂️</h3>
+        <br>
+        <p style="font-size: 12px; text-align: center">⚠️ Should take 30-60s to generate. Please rate the response, it helps a lot.</p>
     """)
 
     output_box = gr.Code(label="Generated SQL", lines=2, interactive=True)
-    note = gr.HTML("""<p style="font-size: 12px; text-align: center">⚠️ Should take 30-60s to generate</p>""")
+
+    with gr.Row():
+        copy_button = gr.Button("📋 Copy SQL", variant="secondary")
+        rate_up = gr.Button("👍", variant="secondary")
+        rate_down = gr.Button("👎", variant="secondary")
+
     input_text = gr.Textbox(lines=3, placeholder='Write your question here...', label='NL Input')
     db_info = gr.Textbox(lines=4, placeholder='Example: | table_01 : column_01 , column_02 | table_02 : column_01 , column_02 | ...', label='Database Info')
     format_sql = gr.Checkbox(label="Format SQL + Remove Skeleton", value=True, interactive=True)
     
-    # Generate button UI element
-    run_button = gr.Button("Generate SQL", variant="primary")
+    with gr.Row():
+        run_button = gr.Button("Generate SQL", variant="primary")
+        clear_button = gr.ClearButton(variant="secondary")
 
     with gr.Accordion("Options", open=False):
         temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.2, step=0.1)
@@ -138,12 +183,11 @@ with gr.Blocks(theme='gradio/soft') as demo:
         repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.08, step=0.01)
         stop_sequence = gr.Textbox(lines=1, value="Explanation,Note", label='Extra Stop Sequence')
     
-    ## Add statement saying that inputs/outpus are sent to firebase
     info = gr.HTML(f"""
         <p>🌐 Leveraging the <a href='https://huggingface.co/{quantized_model}'><strong>4-bit GGML version</strong></a> of <a href='https://huggingface.co/{merged_model}'><strong>{merged_model}</strong></a> model.</p>
         <p>🔗 How it's made: <a href='https://huggingface.co/{initial_model}'><strong>{initial_model}</strong></a> was finetuned to create <a href='https://huggingface.co/{lora_model}'><strong>{lora_model}</strong></a>, then merged together to create <a href='https://huggingface.co/{merged_model}'><strong>{merged_model}</strong></a>.</p>
         <p>📉 Fine-tuning was performed using QLoRA techniques on the <a href='https://huggingface.co/datasets/{dataset}'><strong>{dataset}</strong></a> dataset. You can view training metrics on the <a href='https://huggingface.co/{lora_model}'><strong>QLoRa adapter HF Repo</strong></a>.</p>
-        <p>📊 All inputs/outputs are logged to Firebase to see how the model is doing.</a></p>
+        <p>📊 All inputs/outputs are logged to Firebase to see how the model is doing. You can also leave a rating for each generated SQL the model produces, which gets sent to the database as well.</a></p>
     """)
 
     examples = gr.Examples([
@@ -172,11 +216,16 @@ with gr.Blocks(theme='gradio/soft') as demo:
             readme_content,
         )
     
-    with gr.Accordion("More Options:", open=False):
+    with gr.Accordion("Disabled Options:", open=False):
         log = gr.Checkbox(label="Log to Firebase", value=True, interactive=False)
     
     # When the button is clicked, call the generate function, inputs are taken from the UI elements, outputs are sent to outputs elements
     run_button.click(fn=generate, inputs=[input_text, db_info, temperature, top_p, top_k, repetition_penalty, format_sql, stop_sequence, log], outputs=output_box, api_name="txt2sql")
-    
+    copy_button.click(fn=copy_to_clipboard, inputs=[output_box])
+    clear_button.add([input_text, db_info, output_box])
+
+    # Firebase code - for rating the generated SQL (remove if you don't want to use Firebase)
+    rate_up.click(fn=log_rating_to_firestore, inputs=[input_text, db_info, temperature, output_box, rate_up])
+    rate_down.click(fn=log_rating_to_firestore, inputs=[input_text, db_info, temperature, output_box, rate_down])
 
 demo.queue(concurrency_count=1, max_size=20).launch(debug=True)

requirements.txt

@@ -8,4 +8,5 @@ scipy
 transformers
 accelerate
 sqlparse
-firebase_admin
+firebase_admin
+pyperclip