Update app.py

app.py

@@ -2,21 +2,19 @@ import gradio as gr
 import pixeltable as pxt
 from pixeltable.functions.mistralai import chat_completions
 from datetime import datetime
-
 from textblob import TextBlob
 import re
 import nltk
 from nltk.tokenize import word_tokenize
 from nltk.corpus import stopwords
+import os
+import getpass
 
 # Ensure necessary NLTK data is downloaded
 nltk.download('punkt', quiet=True)
 nltk.download('stopwords', quiet=True)
 nltk.download('punkt_tab', quiet=True)
 
-import os
-import getpass
-
 # Set up Mistral API key
 if 'MISTRAL_API_KEY' not in os.environ:
     os.environ['MISTRAL_API_KEY'] = getpass.getpass('Mistral AI API Key:')
@@ -42,7 +40,6 @@ def calculate_readability(text: str) -> float:
 
 # Function to run inference and analysis
 def run_inference_and_analysis(task, system_prompt, input_text, temperature, top_p, max_tokens, min_tokens, stop, random_seed, safe_prompt):
-    
     # Initialize Pixeltable
     pxt.drop_table('mistral_prompts', ignore_errors=True)
     t = pxt.create_table('mistral_prompts', {
@@ -57,7 +54,7 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
         'stop': pxt.StringType(nullable=True),
         'random_seed': pxt.IntType(nullable=True),
         'safe_prompt': pxt.BoolType(nullable=True)
-  })
+    })
     
     # Insert new row into Pixeltable
     t.insert([{
@@ -81,14 +78,14 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
     ]
 
     common_params = {
-      'messages': msgs,
-      'temperature': temperature,
-      'top_p': top_p,
-      'max_tokens': max_tokens if max_tokens is not None else 300,
-      'min_tokens': min_tokens,
-      'stop': stop.split(',') if stop else None,
-      'random_seed': random_seed,
-      'safe_prompt': safe_prompt
+        'messages': msgs,
+        'temperature': temperature,
+        'top_p': top_p,
+        'max_tokens': max_tokens if max_tokens is not None else 300,
+        'min_tokens': min_tokens,
+        'stop': stop.split(',') if stop else None,
+        'random_seed': random_seed,
+        'safe_prompt': safe_prompt
     }
     
     # Run inference with both models
@@ -116,9 +113,7 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
     ).tail(1)
 
     history = t.select(t.timestamp, t.task, t.system, t.input_text).order_by(t.timestamp, asc=False).collect().to_pandas()
-
     responses = t.select(t.timestamp, t.omn_response, t.ml_response).order_by(t.timestamp, asc=False).collect().to_pandas()
-    
     analysis = t.select(
         t.timestamp,
         t.open_sentiment_score,
@@ -128,7 +123,6 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
         t.open_readability_score,
         t.large_readability_score
     ).order_by(t.timestamp, asc=False).collect().to_pandas()
-    
     params = t.select(
         t.timestamp,
         t.temperature,
@@ -158,60 +152,60 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
 # Gradio interface
 def gradio_interface():
     with gr.Blocks(theme=gr.themes.Base(), title="Prompt Engineering and LLM Studio") as demo:
-         gr.Markdown(
-        """
-        <div style= margin-bottom: 20px;">
-            <img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" alt="Pixeltable" style="max-width: 150px;" />
-            <h2>Text and Image similarity search on video frames with embedding indexes</h2>
-        </div>
-        """
-    ) 
         gr.Markdown(
-                """
-                # Prompt Engineering and LLM Studio
+            """
+            <div style="margin-bottom: 20px;">
+                <img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" alt="Pixeltable" style="max-width: 150px;" />
+                <h2>Text and Image similarity search on video frames with embedding indexes</h2>
+            </div>
+            """
+        ) 
+        gr.Markdown(
+            """
+            # Prompt Engineering and LLM Studio
 
-                This application demonstrates how [Pixeltable](https://github.com/pixeltable/pixeltable) can be used for rapid and incremental prompt engineering 
-                and model comparison workflows. It showcases Pixeltable's ability to directly store, version, index, 
-                and transform data while providing an interactive interface to experiment with different prompts and models.
+            This application demonstrates how [Pixeltable](https://github.com/pixeltable/pixeltable) can be used for rapid and incremental prompt engineering 
+            and model comparison workflows. It showcases Pixeltable's ability to directly store, version, index, 
+            and transform data while providing an interactive interface to experiment with different prompts and models.
 
-                Remember, effective prompt engineering often requires experimentation and iteration. Use this tool to systematically improve your prompts and understand how different inputs and parameters affect the LLM outputs.
+            Remember, effective prompt engineering often requires experimentation and iteration. Use this tool to systematically improve your prompts and understand how different inputs and parameters affect the LLM outputs.
             """
-            )
+        )
 
-          with gr.Row():
-              with gr.Column():
-                  with gr.Accordion("What does it do?", open=False):
-                      gr.Markdown(
-                          """
-                          1. **Data Organization**: Pixeltable uses tables and views to organize data, similar to traditional databases but with enhanced capabilities for AI workflows.
-                          2. **Computed Columns**: These are dynamically generated columns based on expressions applied to columns.
-                          3. **Data Storage**: All prompts, responses, and analysis results are stored in Pixeltable tables.
-                          4. **Versioning**: Every operations are automatically versioned, allowing you to track changes over time.
-                          5. **UDFs**: Sentiment scores, keywords, and readability scores are computed dynamically.
-                          6. **Querying**: The history and analysis tabs leverage Pixeltable's querying capabilities to display results.
-                          """
-                      )
-                
-              with gr.Column():
-                  with gr.Accordion("How does it work?", open=False):
-                      gr.Markdown(
-                          """
-                          1. **Define your task**: This helps you keep track of different experiments. 
-                          2. **Set up your prompt**: Enter a system prompt in the "System Prompt" field. Write your specific input or question in the "Input Text" field
-                          3. **Adjust parameters (optional)**: Adjust temperature, top_p, token limits, etc., to control the model's output.
-                          4. **Run the analysis**: Click the "Run Inference and Analysis" button.
-                          5. **Review the results**: Compare the responses from both models and exmaine the scores.
-                          6. **Iterate and refine**: Based on the results, refine your prompt or adjust parameters.
-                      """
-                      )
-  
-          with gr.Row():
-              with gr.Column():
-                  task = gr.Textbox(label="Task (Arbitrary Category)")
-                  system_prompt = gr.Textbox(label="System Prompt")
-                  input_text = gr.Textbox(label="Input Text")
+        with gr.Row():
+            with gr.Column():
+                with gr.Accordion("What does it do?", open=False):
+                    gr.Markdown(
+                        """
+                        1. **Data Organization**: Pixeltable uses tables and views to organize data, similar to traditional databases but with enhanced capabilities for AI workflows.
+                        2. **Computed Columns**: These are dynamically generated columns based on expressions applied to columns.
+                        3. **Data Storage**: All prompts, responses, and analysis results are stored in Pixeltable tables.
+                        4. **Versioning**: Every operations are automatically versioned, allowing you to track changes over time.
+                        5. **UDFs**: Sentiment scores, keywords, and readability scores are computed dynamically.
+                        6. **Querying**: The history and analysis tabs leverage Pixeltable's querying capabilities to display results.
+                        """
+                    )
+            
+            with gr.Column():
+                with gr.Accordion("How does it work?", open=False):
+                    gr.Markdown(
+                        """
+                        1. **Define your task**: This helps you keep track of different experiments. 
+                        2. **Set up your prompt**: Enter a system prompt in the "System Prompt" field. Write your specific input or question in the "Input Text" field
+                        3. **Adjust parameters (optional)**: Adjust temperature, top_p, token limits, etc., to control the model's output.
+                        4. **Run the analysis**: Click the "Run Inference and Analysis" button.
+                        5. **Review the results**: Compare the responses from both models and exmaine the scores.
+                        6. **Iterate and refine**: Based on the results, refine your prompt or adjust parameters.
+                        """
+                    )
+
+        with gr.Row():
+            with gr.Column():
+                task = gr.Textbox(label="Task (Arbitrary Category)")
+                system_prompt = gr.Textbox(label="System Prompt")
+                input_text = gr.Textbox(label="Input Text")
 
-                  with gr.Accordion("Advanced Settings", open=False):
+                with gr.Accordion("Advanced Settings", open=False):
                     temperature = gr.Slider(minimum=0, maximum=1, value=0.7, step=0.1, label="Temperature")
                     top_p = gr.Slider(minimum=0, maximum=1, value=0.9, step=0.1, label="Top P")
                     max_tokens = gr.Number(label="Max Tokens", value=300)
@@ -220,9 +214,9 @@ def gradio_interface():
                     random_seed = gr.Number(label="Random Seed", value=None)
                     safe_prompt = gr.Checkbox(label="Safe Prompt", value=False)
 
-                  submit_btn = gr.Button("Run Inference and Analysis")
+                submit_btn = gr.Button("Run Inference and Analysis")
 
-                  with gr.Tabs():
+                with gr.Tabs():
                     with gr.Tab("Prompt Input"):
                         history = gr.Dataframe(
                             headers=["Task", "System Prompt", "Input Text", "Timestamp"],
@@ -262,59 +256,59 @@ def gradio_interface():
                                 "Safe Prompt"
                             ],
                             wrap=True
-                          )
+                        )
 
-                  # Define the examples
-                  examples = [
-                      # Example 1: Sentiment Analysis
-                      ["Sentiment Analysis", 
-                      "You are an AI trained to analyze the sentiment of text. Provide a detailed analysis of the emotional tone, highlighting key phrases that indicate sentiment.",
-                      "The new restaurant downtown exceeded all my expectations. The food was exquisite, the service impeccable, and the ambiance was perfect for a romantic evening. I can't wait to go back!",
-                      0.3, 0.95, 200, None, "", None, False],
-                      
-                      # Example 2: Creative Writing
-                      ["Story Generation",
-                      "You are a creative writer. Generate a short, engaging story based on the given prompt. Include vivid descriptions and an unexpected twist.",
-                      "In a world where dreams are shared, a young girl discovers she can manipulate other people's dreams.",
-                      0.9, 0.8, 500, 200, "The end.", None, False]
-                  ]
+                # Define the examples
+                examples = [
+                    # Example 1: Sentiment Analysis
+                    ["Sentiment Analysis", 
+                    "You are an AI trained to analyze the sentiment of text. Provide a detailed analysis of the emotional tone, highlighting key phrases that indicate sentiment.",
+                    "The new restaurant downtown exceeded all my expectations. The food was exquisite, the service impeccable, and the ambiance was perfect for a romantic evening. I can't wait to go back!",
+                    0.3, 0.95, 200, None, "", None, False],
+                    
+                    # Example 2: Creative Writing
+                    ["Story Generation",
+                    "You are a creative writer. Generate a short, engaging story based on the given prompt. Include vivid descriptions and an unexpected twist.",
+                    "In a world where dreams are shared, a young girl discovers she can manipulate other people's dreams.",
+                    0.9, 0.8, 500, 200, "The end.", None, False]
+                ]
             
-              with gr.Column():
-                  omn_response = gr.Textbox(label="Open-Mistral-Nemo Response")
-                  ml_response = gr.Textbox(label="Mistral-Medium Response")
-                  
-                  with gr.Row():
-                      large_sentiment = gr.Number(label="Mistral-Medium Sentiment")
-                      open_sentiment = gr.Number(label="Open-Mistral-Nemo Sentiment")
-                  
-                  with gr.Row():
-                      large_keywords = gr.Textbox(label="Mistral-Medium Keywords")
-                      open_keywords = gr.Textbox(label="Open-Mistral-Nemo Keywords")
-                  
-                  with gr.Row():
-                      large_readability = gr.Number(label="Mistral-Medium Readability")
-                      open_readability = gr.Number(label="Open-Mistral-Nemo Readability")
-                      
-          gr.Examples(
-                examples=examples,
-                inputs=[task, system_prompt, input_text, temperature, top_p, max_tokens, min_tokens, stop, random_seed, safe_prompt],
-                outputs=[omn_response, ml_response, large_sentiment, open_sentiment, large_keywords, open_keywords, large_readability, open_readability],
-                fn=run_inference_and_analysis,
-                cache_examples=True,
-            )
+            with gr.Column():
+                omn_response = gr.Textbox(label="Open-Mistral-Nemo Response")
+                ml_response = gr.Textbox(label="Mistral-Medium Response")
+                
+                with gr.Row():
+                    large_sentiment = gr.Number(label="Mistral-Medium Sentiment")
+                    open_sentiment = gr.Number(label="Open-Mistral-Nemo Sentiment")
+                
+                with gr.Row():
+                    large_keywords = gr.Textbox(label="Mistral-Medium Keywords")
+                    open_keywords = gr.Textbox(label="Open-Mistral-Nemo Keywords")
+                
+                with gr.Row():
+                    large_readability = gr.Number(label="Mistral-Medium Readability")
+                    open_readability = gr.Number(label="Open-Mistral-Nemo Readability")
+                    
+        gr.Examples(
+            examples=examples,
+            inputs=[task, system_prompt, input_text, temperature, top_p, max_tokens, min_tokens, stop, random_seed, safe_prompt],
+            outputs=[omn_response, ml_response, large_sentiment, open_sentiment, large_keywords, open_keywords, large_readability, open_readability],
+            fn=run_inference_and_analysis,
+            cache_examples=True,
+        )
 
-          gr.Markdown(
-              """
-              For more information, visit [Pixeltable's GitHub repository](https://github.com/pixeltable/pixeltable).
-              """
-          )
-          
-          submit_btn.click(
-              run_inference_and_analysis,
-              inputs=[task, system_prompt, input_text, temperature, top_p, max_tokens, min_tokens, stop, random_seed, safe_prompt],
-              outputs=[omn_response, ml_response, large_sentiment, open_sentiment, large_keywords, open_keywords, large_readability, open_readability, history, responses, analysis, params ]
-          )
-      
+        gr.Markdown(
+            """
+            For more information, visit [Pixeltable's GitHub repository](https://github.com/pixeltable/pixeltable).
+            """
+        )
+        
+        submit_btn.click(
+            run_inference_and_analysis,
+            inputs=[task, system_prompt, input_text, temperature, top_p, max_tokens, min_tokens, stop, random_seed, safe_prompt],
+            outputs=[omn_response, ml_response, large_sentiment, open_sentiment, large_keywords, open_keywords, large_readability, open_readability, history, responses, analysis, params]
+        )
+    
     return demo
 
 # Launch the Gradio interface