Update app.py

app.py

@@ -3,17 +3,16 @@ 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
+import re
 
 # Ensure necessary NLTK data is downloaded
 nltk.download('punkt', quiet=True)
 nltk.download('stopwords', quiet=True)
-nltk.download('punkt_tab', quiet=True)
 
 # Set up Mistral API key
 if 'MISTRAL_API_KEY' not in os.environ:
@@ -38,24 +37,24 @@ def calculate_readability(text: str) -> float:
     average_words_per_sentence = words / sentences
     return 206.835 - 1.015 * average_words_per_sentence
 
-# Function to run inference and analysis
-def run_inference_and_analysis(task, system_prompt, input_text, temperature, top_p, max_tokens, stop, random_seed, safe_prompt):
+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', {
-        'task': pxt.String,
-        'system': pxt.String,
-        'input_text': pxt.String,
-        'timestamp': pxt.Timestamp,
-        'temperature': pxt.Float,
-        'top_p': pxt.Float,
-        'max_tokens': pxt.Int,
-        'stop': pxt.String,
-        'random_seed': pxt.Int,
-        'safe_prompt': pxt.Bool
+        'task': pxt.StringType(),
+        'system': pxt.StringType(),
+        'input_text': pxt.StringType(),
+        'timestamp': pxt.TimestampType(),
+        'temperature': pxt.FloatType(),
+        'top_p': pxt.FloatType(),
+        'max_tokens': pxt.IntType(),
+        'min_tokens': pxt.IntType(),
+        'stop': pxt.StringType(),
+        'random_seed': pxt.IntType(),
+        'safe_prompt': pxt.BoolType()
     })
     
-    # Insert new row into Pixeltable
+    # Insert new row
     t.insert([{
         'task': task,
         'system': system_prompt,
@@ -64,6 +63,7 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
         'temperature': temperature,
         'top_p': top_p,
         'max_tokens': max_tokens,
+        'min_tokens': min_tokens,
         'stop': stop,
         'random_seed': random_seed,
         'safe_prompt': safe_prompt
@@ -80,56 +80,36 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
         '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
     }
     
-    # Add computed columns for model responses and analysis
-    t.add_computed_column(open_mistral_nemo=chat_completions(model='open-mistral-nemo', **common_params))
-    t.add_computed_column(mistral_medium=chat_completions(model='mistral-medium', **common_params))
+    # Run inference with both models
+    t['open_mistral_nemo'] = chat_completions(model='open-mistral-nemo', **common_params)
+    t['mistral_medium'] = chat_completions(model='mistral-medium', **common_params)
     
     # Extract responses
-    t.add_computed_column(omn_response=t.open_mistral_nemo.choices[0].message.content.astype(pxt.String))
-    t.add_computed_column(ml_response=t.mistral_medium.choices[0].message.content.astype(pxt.String))
+    t['omn_response'] = t.open_mistral_nemo.choices[0].message.content
+    t['ml_response'] = t.mistral_medium.choices[0].message.content
     
-    # Add computed columns for analysis
-    t.add_computed_column(large_sentiment_score=get_sentiment_score(t.ml_response))
-    t.add_computed_column(large_keywords=extract_keywords(t.ml_response))
-    t.add_computed_column(large_readability_score=calculate_readability(t.ml_response))
-    t.add_computed_column(open_sentiment_score=get_sentiment_score(t.omn_response))
-    t.add_computed_column(open_keywords=extract_keywords(t.omn_response))
-    t.add_computed_column(open_readability_score=calculate_readability(t.omn_response))
+    # Run analysis
+    t['large_sentiment_score'] = get_sentiment_score(t.ml_response)
+    t['large_keywords'] = extract_keywords(t.ml_response)
+    t['large_readability_score'] = calculate_readability(t.ml_response)
+    t['open_sentiment_score'] = get_sentiment_score(t.omn_response)
+    t['open_keywords'] = extract_keywords(t.omn_response)
+    t['open_readability_score'] = calculate_readability(t.omn_response)
     
-    # Retrieve results
+    # Get results
     results = t.select(
         t.omn_response, t.ml_response,
         t.large_sentiment_score, t.open_sentiment_score,
         t.large_keywords, t.open_keywords,
         t.large_readability_score, t.open_readability_score
     ).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,
-        t.large_sentiment_score,
-        t.open_keywords,
-        t.large_keywords,
-        t.open_readability_score,
-        t.large_readability_score
-    ).order_by(t.timestamp, asc=False).collect().to_pandas()
-    params = t.select(
-        t.timestamp,
-        t.temperature,
-        t.top_p,
-        t.max_tokens,
-        t.stop,
-        t.random_seed,
-        t.safe_prompt
-    ).order_by(t.timestamp, asc=False).collect().to_pandas()
-       
+    
     return (
         results['omn_response'][0],
         results['ml_response'][0],
@@ -138,309 +118,91 @@ def run_inference_and_analysis(task, system_prompt, input_text, temperature, top
         results['large_keywords'][0],
         results['open_keywords'][0],
         results['large_readability_score'][0],
-        results['open_readability_score'][0],
-        history,
-        responses,
-        analysis,
-        params
+        results['open_readability_score'][0]
     )
 
 def gradio_interface():
-    with gr.Blocks(theme=gr.themes.Base(), title="Pixeltable LLM Studio") as demo:
-        # Enhanced Header with Branding
-        gr.HTML("""
-            <div style="text-align: center; padding: 20px; background: linear-gradient(to right, #4F46E5, #7C3AED);" class="shadow-lg">
-                <img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" 
-                     alt="Pixeltable" style="max-width: 200px; margin-bottom: 15px;" />
-                <h1 style="color: white; font-size: 2.5rem; margin-bottom: 10px;">LLM Studio</h1>
-                <p style="color: #E5E7EB; font-size: 1.1rem;">
-                    Powered by Pixeltable's Unified AI Data Infrastructure
-                </p>
-            </div>
-        """)
+    with gr.Blocks() as demo:
+        gr.Markdown("# LLM Prompt Studio")
 
-        # Product Overview Cards
         with gr.Row():
             with gr.Column():
-                gr.HTML("""
-                    <div style="padding: 20px; background-color: white; border-radius: 10px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); margin: 10px;">
-                        <h3 style="color: #4F46E5; margin-bottom: 10px;">🚀 Why Pixeltable?</h3>
-                        <ul style="list-style-type: none; padding-left: 0;">
-                            <li style="margin-bottom: 8px;">✨ Unified data management for AI workflows</li>
-                            <li style="margin-bottom: 8px;">📊 Automatic versioning and lineage tracking</li>
-                            <li style="margin-bottom: 8px;">⚡ Seamless model integration and deployment</li>
-                            <li style="margin-bottom: 8px;">🔍 Advanced querying and analysis capabilities</li>
-                        </ul>
-                    </div>
-                """)
+                # Input components
+                task = gr.Textbox(label="Task")
+                system_prompt = gr.Textbox(label="System Prompt", lines=3)
+                input_text = gr.Textbox(label="Input Text", lines=3)
+
+                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)
+                    min_tokens = gr.Number(label="Min Tokens", value=None)
+                    stop = gr.Textbox(label="Stop Sequences (comma-separated)")
+                    random_seed = gr.Number(label="Random Seed", value=None)
+                    safe_prompt = gr.Checkbox(label="Safe Prompt", value=False)
+
+                # Example prompts
+                examples = [
+                    ["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],
+                    
+                    ["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, 300, "The end", None, False]
+                ]
+
+                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
+                )
+
+                submit_btn = gr.Button("Run Analysis")
 
             with gr.Column():
-                gr.HTML("""
-                    <div style="padding: 20px; background-color: white; border-radius: 10px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); margin: 10px;">
-                        <h3 style="color: #4F46E5; margin-bottom: 10px;">💡 Features</h3>
-                        <ul style="list-style-type: none; padding-left: 0;">
-                            <li style="margin-bottom: 8px;">🔄 Compare multiple LLM models side-by-side</li>
-                            <li style="margin-bottom: 8px;">📈 Track and analyze model performance</li>
-                            <li style="margin-bottom: 8px;">🎯 Experiment with different prompts and parameters</li>
-                            <li style="margin-bottom: 8px;">📝 Automatic analysis with sentiment and readability scores</li>
-                        </ul>
-                    </div>
-                """)
-
-        # Main Interface
-        with gr.Tabs() as tabs:
-            with gr.TabItem("🎯 Experiment", id=0):
+                # Output components
+                omn_response = gr.Textbox(label="Open-Mistral-Nemo Response")
+                ml_response = gr.Textbox(label="Mistral-Medium Response")
+                
                 with gr.Row():
-                    with gr.Column(scale=1):
-                        gr.HTML("""
-                            <div style="padding: 15px; background-color: #F3F4F6; border-radius: 8px; margin-bottom: 15px;">
-                                <h3 style="color: #4F46E5; margin-bottom: 10px;">Experiment Setup</h3>
-                                <p style="color: #6B7280; font-size: 0.9rem;">Configure your prompt engineering experiment below</p>
-                            </div>
-                        """)
-                        
-                        # Define output components first
-                        omn_response = gr.Textbox(
-                            label="Open-Mistral-Nemo Response",
-                            elem_classes="output-style"
-                        )
-                        ml_response = gr.Textbox(
-                            label="Mistral-Medium Response",
-                            elem_classes="output-style"
-                        )
-                        large_sentiment = gr.Number(label="Mistral-Medium Sentiment")
-                        open_sentiment = gr.Number(label="Open-Mistral-Nemo Sentiment")
-                        large_keywords = gr.Textbox(label="Mistral-Medium Keywords")
-                        open_keywords = gr.Textbox(label="Open-Mistral-Nemo Keywords")
-                        large_readability = gr.Number(label="Mistral-Medium Readability")
-                        open_readability = gr.Number(label="Open-Mistral-Nemo Readability")
-
-                        # Now define input components
-                        task = gr.Textbox(
-                            label="Task Category",
-                            placeholder="e.g., Sentiment Analysis, Text Generation, Summarization",
-                            elem_classes="input-style"
-                        )
-                        system_prompt = gr.Textbox(
-                            label="System Prompt",
-                            placeholder="Define the AI's role and task...",
-                            lines=3,
-                            elem_classes="input-style"
-                        )
-                        input_text = gr.Textbox(
-                            label="Input Text",
-                            placeholder="Enter your prompt or text to analyze...",
-                            lines=4,
-                            elem_classes="input-style"
-                        )
-
-                        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)
-                            min_tokens = gr.Number(label="Min Tokens", value=None)
-                            stop = gr.Textbox(label="Stop Sequences (comma-separated)")
-                            random_seed = gr.Number(label="Random Seed", value=None)
-                            safe_prompt = gr.Checkbox(label="Safe Prompt", value=False)
-
-                        # Add Examples Section with enhanced styling
-                        gr.HTML("""
-                            <div style="padding: 15px; background-color: #F3F4F6; border-radius: 8px; margin: 20px 0;">
-                                <h3 style="color: #4F46E5; margin-bottom: 10px;">📚 Example Prompts</h3>
-                                <p style="color: #6B7280; font-size: 0.9rem;">Try these pre-configured examples to get started</p>
-                            </div>
-                        """)
-
-                        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, 300, "The end", None, False]
-                        ]
-
-                        with gr.Group(elem_classes="examples-container"):
-                            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
-                            )
-
-                        submit_btn = gr.Button(
-                            "🚀 Run Analysis",
-                            variant="primary",
-                            scale=1,
-                            min_width=200
-                        )
-                        
-                    with gr.Column(scale=1):
-                        gr.HTML("""
-                            <div style="padding: 15px; background-color: #F3F4F6; border-radius: 8px; margin-bottom: 15px;">
-                                <h3 style="color: #4F46E5; margin-bottom: 10px;">Results</h3>
-                                <p style="color: #6B7280; font-size: 0.9rem;">Compare model outputs and analysis metrics</p>
-                            </div>
-                        """)
-                        
-                        with gr.Group():
-                            omn_response = gr.Textbox(
-                                label="Open-Mistral-Nemo Response",
-                                elem_classes="output-style"
-                            )
-                            ml_response = gr.Textbox(
-                                label="Mistral-Medium Response",
-                                elem_classes="output-style"
-                            )
-
-                        with gr.Group():
-                            with gr.Row():
-                                with gr.Column():
-                                    gr.HTML("<h4>📊 Sentiment Analysis</h4>")
-                                    large_sentiment = gr.Number(label="Mistral-Medium")
-                                    open_sentiment = gr.Number(label="Open-Mistral-Nemo")
-                                
-                                with gr.Column():
-                                    gr.HTML("<h4>📈 Readability Scores</h4>")
-                                    large_readability = gr.Number(label="Mistral-Medium")
-                                    open_readability = gr.Number(label="Open-Mistral-Nemo")
-
-                            gr.HTML("<h4>🔑 Key Terms</h4>")
-                            with gr.Row():
-                                large_keywords = gr.Textbox(label="Mistral-Medium Keywords")
-                                open_keywords = gr.Textbox(label="Open-Mistral-Nemo Keywords")
-
-            with gr.TabItem("📊 History & Analysis", id=1):
-                with gr.Tabs():
-                    with gr.TabItem("Prompt History"):
-                        history = gr.DataFrame(
-                            headers=["Timestamp", "Task", "System Prompt", "Input Text"],
-                            wrap=True,
-                            elem_classes="table-style"
-                        )
-                    
-                    with gr.TabItem("Model Responses"):
-                        responses = gr.DataFrame(
-                            headers=["Timestamp", "Open-Mistral-Nemo", "Mistral-Medium"],
-                            wrap=True,
-                            elem_classes="table-style"
-                        )
-                    
-                    with gr.TabItem("Analysis Results"):
-                        analysis = gr.DataFrame(
-                            headers=[
-                                "Timestamp",
-                                "Open-Mistral-Nemo Sentiment",
-                                "Mistral-Medium Sentiment",
-                                "Open-Mistral-Nemo Keywords",
-                                "Mistral-Medium Keywords",
-                                "Open-Mistral-Nemo Readability",
-                                "Mistral-Medium Readability"
-                            ],
-                            wrap=True,
-                            elem_classes="table-style"
-                        )
-                        
-                    with gr.TabItem("Model Parameters"):
-                        params = gr.DataFrame(
-                            headers=[
-                                "Timestamp",
-                                "Temperature",
-                                "Top P",
-                                "Max Tokens",
-                                "Stop Sequences",
-                                "Random Seed",
-                                "Safe Prompt"
-                            ],
-                            wrap=True,
-                            elem_classes="table-style"
-                        )
-
-        # Footer with links and additional info
-        gr.HTML("""
-            <div style="text-align: center; padding: 20px; margin-top: 30px; border-top: 1px solid #E5E7EB;">
-                <div style="margin-bottom: 20px;">
-                    <h3 style="color: #4F46E5;">Built with Pixeltable</h3>
-                    <p style="color: #6B7280;">The unified data infrastructure for AI applications</p>
-                </div>
-                <div style="display: flex; justify-content: center; gap: 20px;">
-                    <a href="https://github.com/pixeltable/pixeltable" target="_blank" 
-                       style="color: #4F46E5; text-decoration: none;">
-                        📚 Documentation
-                    </a>
-                    <a href="https://github.com/pixeltable/pixeltable" target="_blank"
-                       style="color: #4F46E5; text-decoration: none;">
-                        💻 GitHub
-                    </a>
-                    <a href="https://join.slack.com/t/pixeltablecommunity/shared_invite/zt-21fybjbn2-fZC_SJiuG6QL~Ai8T6VpFQ" target="_blank"
-                       style="color: #4F46E5; text-decoration: none;">
-                        💬 Community
-                    </a>
-                </div>
-            </div>
-        """)
-
-        # Custom CSS
-        gr.HTML("""
-            <style>
-                .examples-container {
-                    margin: 20px 0;
-                    padding: 15px;
-                    border: 1px solid #E5E7EB;
-                    border-radius: 8px;
-                    background-color: white;
-                }
-                .examples-container .gr-samples-table {
-                    border-collapse: separate;
-                    border-spacing: 0 8px;
-                }
-                .examples-container .gr-samples-table tr {
-                    background-color: #F9FAFB;
-                    border: 1px solid #E5E7EB;
-                    border-radius: 6px;
-                    transition: all 0.2s;
-                }
-                .examples-container .gr-samples-table tr:hover {
-                    background-color: #F3F4F6;
-                    border-color: #4F46E5;
-                    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-                }
-                .examples-container .gr-samples-table td {
-                    padding: 12px;
-                    border: none;
-                }
-            </style>
-        """)
+                    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")
 
         submit_btn.click(
             run_inference_and_analysis,
             inputs=[
-                task, system_prompt, input_text, 
-                temperature, top_p, max_tokens, 
-                stop, random_seed, safe_prompt
+                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  # Added params here
+                omn_response, ml_response,
+                large_sentiment, open_sentiment,
+                large_keywords, open_keywords,
+                large_readability, open_readability
             ]
         )