Update app.py

app.py

@@ -8,23 +8,19 @@ import os
 sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 summarization_pipeline = pipeline("summarization", model="RussianNLP/FRED-T5-Summarizer")
 
-# Task logic with enhanced feedback
+# Task logic
 def perform_task(task, text):
     if not text.strip():
         return "⚠️ Please enter some text to analyze.", None, gr.update(visible=False)
 
-    # Simulate processing time for better UX
     time.sleep(0.5)
 
     if task == "Sentiment Analysis":
         result = sentiment_pipeline(text)[0]
         label = result['label']
         score = round(result['score'], 3)
-
-        # Enhanced sentiment display with emojis
         emoji = "😊" if label == "POSITIVE" else "😟"
         confidence_bar = "█" * int(score * 10) + "░" * (10 - int(score * 10))
-
         output = f"""
 {emoji} **Sentiment Analysis Results**
 
@@ -34,18 +30,14 @@ def perform_task(task, text):
 
 **Interpretation:** This text expresses a {label.lower()} sentiment with {score*100:.1f}% confidence.
         """.strip()
-
         return output, None, gr.update(visible=False)
 
     elif task == "Summarization":
         result = summarization_pipeline(text, max_length=100, min_length=30, do_sample=False)
         summary = result[0]['summary_text']
-
-        # Calculate compression ratio
         original_words = len(text.split())
         summary_words = len(summary.split())
         compression_ratio = round((1 - summary_words/original_words) * 100, 1)
-
         output = f"""
 📝 **Text Summarization Results**
 
@@ -57,17 +49,14 @@ def perform_task(task, text):
 • Summary: {summary_words} words
 • Compression: {compression_ratio}% reduction
         """.strip()
-
         return output, None, gr.update(visible=False)
 
     elif task == "Text-to-Speech":
         tts = gTTS(text)
         filename = "tts_output.mp3"
         tts.save(filename)
-
         word_count = len(text.split())
         char_count = len(text)
-
         output = f"""
 🔊 **Text-to-Speech Generated Successfully!**
 
@@ -78,294 +67,108 @@ def perform_task(task, text):
 
 **Audio file ready for playback below** ⬇️
         """.strip()
-
         return output, filename, gr.update(visible=True, value=filename)
 
-# Enhanced CSS with modern design elements
-custom_css = """
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
-
-    :root {
-        --primary-bg: #0a0a0a;
-        --secondary-bg: #1a1a1a;
-        --accent-bg: #2a2a2a;
-        --primary-text: #ffffff;
-        --secondary-text: #b0b0b0;
-        --accent-color: #3b82f6;
-        --accent-hover: #2563eb;
-        --success-color: #10b981;
-        --warning-color: #f59e0b;
-        --border-color: #333333;
-        --gradient-1: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        --gradient-2: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
-        --gradient-3: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
-    }
-
-    * {
-        font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif !important;
-    }
-
-    body, .gradio-container {
-        background: var(--primary-bg) !important;
-        color: var(--primary-text) !important;
-        background-image:
-            radial-gradient(circle at 20% 50%, rgba(59, 130, 246, 0.1) 0%, transparent 50%),
-            radial-gradient(circle at 80% 20%, rgba(168, 85, 247, 0.1) 0%, transparent 50%),
-            radial_gradient(circle at 40% 80%, rgba(16, 185, 129, 0.1) 0%, transparent 50%);
-        min-height: 100vh;
-    }
-
-    .gradio-container {
-        max-width: 1200px !important;
-        margin: 0 auto !important;
-        padding: 2rem !important;
-    }
-
-    /* Header styling */
-    #title {
-        background: var(--gradient-1);
-        background-clip: text;
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        font-size: 3rem !important;
-        font-weight: 700 !important;
-        text-align: center !important;
-        margin-bottom: 2rem !important;
-        animation: glow 2s ease-in-out infinite alternate;
-    }
-
-    @keyframes glow {
-        from { filter: drop-shadow(0 0 20px rgba(59, 130, 246, 0.3)); }
-        to { filter: drop_shadow(0 0 30px rgba(168, 85, 247, 0.5)); }
-    }
-
-    /* Card-like containers */
-    .gr-box, .gr-form {
-        background: var(--secondary-bg) !important;
-        border: 1px solid var(--border-color) !important;
-        border-radius: 16px !important;
-        padding: 1.5rem !important;
-        backdrop-filter: blur(10px) !important;
-        box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3) !important;
-        transition: all 0.3s ease !important;
-    }
-
-    .gr-box:hover, .gr-form:hover {
-        border-color: var(--accent-color) !important;
-        box-shadow: 0 12px 40px rgba(59, 130, 246, 0.2) !important;
-        transform: translateY(-2px) !important;
-    }
-
-    /* Input fields */
-    .gr-input, .gr-textbox, .gr-dropdown {
-        background: var(--accent-bg) !important;
-        color: var(--primary-text) !important;
-        border: 2px solid var(--border-color) !important;
-        border-radius: 12px !important;
-        padding: 1rem !important;
-        font-size: 1rem !important;
-        transition: all 0.3s ease !important;
-    }
-
-    .gr-input:focus, .gr-textbox:focus, .gr-dropdown:focus {
-        border-color: var(--accent-color) !important;
-        box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1) !important;
-        outline: none !important;
-    }
-
-    /* Buttons */
-    .gr-button {
-        background: var(--gradient-1) !important;
-        color: white !important;
-        border: none !important;
-        border-radius: 12px !important;
-        padding: 1rem 2rem !important;
-        font-size: 1.1rem !important;
-        font-weight: 600 !important;
-        cursor: pointer !important;
-        transition: all 0.3s ease !important;
-        box-shadow: 0 4px 20px rgba(59, 130, 246, 0.3) !important;
-    }
-
-    .gr-button:hover {
-        transform: translateY(-2px) !important;
-        box-shadow: 0 8px 30px rgba(59, 130, 246, 0.4) !important;
-    }
-
-    .gr-button:active {
-        transform: translateY(0) !important;
-    }
-
-    /* Labels */
-    label {
-        color: var(--primary-text) !important;
-        font-weight: 500 !important;
-        font-size: 1.1rem !important;
-        margin-bottom: 0.5rem !important;
-    }
-
-    /* Placeholders */
-    input::placeholder, textarea::placeholder {
-        color: var(--secondary-text) !important;
-        opacity: 0.7 !important;
-    }
-
-    /* Output areas */
-    .gr-textbox[data-testid="textbox"] {
-        background: var(--accent-bg) !important;
-        border: 1px solid var(--border-color) !important;
-        border-radius: 12px !important;
-        padding: 1.5rem !important;
-        font-family: 'Inter', monospace !important;
-        line-height: 1.6 !important;
-    }
-
-    /* Audio component */
-    .gr-audio {
-        background: var(--secondary-bg) !important;
-        border: 1px solid var(--border-color) !important;
-        border-radius: 12px !important;
-        padding: 1rem !important;
-    }
-
-    /* Markdown content */
-    .markdown-content {
-        line-height: 1.8 !important;
-    }
-
-    .markdown-content strong {
-        color: var(--accent-color) !important;
-    }
-
-    /* Task selector special styling */
-    .gr-dropdown {
-        background: var(--gradient-2) !important;
-        color: white !important;
-        font-weight: 500 !important;
-    }
-
-    /* Responsive design */
-    @media (max-width: 768px) {
-        #title {
-            font-size: 2rem !important;
-        }
-
-        .gradio-container {
-            padding: 1rem !important;
-        }
-
-        .gr-box, .gr-form {
-            padding: 1rem !important;
-        }
-    }
-
-    /* Loading animation */
-    @keyframes pulse {
-        0%, 100% { opacity: 1; }
-        50% { opacity: 0.5; }
-    }
-
-    .loading {
-        animation: pulse 1.5s infinite;
-    }
-
-    /* Success/Error states */
-    .success {
-        border-color: var(--success-color) !important;
-        box-shadow: 0 0 20px rgba(16, 185, 129, 0.2) !important;
-    }
-
-    .warning {
-        border-color: var(--warning-color) !important;
-        box-shadow: 0 0 20px rgba(245, 158, 11, 0.2) !important;
-    }
-</style>
-"""
-
-# UI with enhanced design
+# Handle button click
+def handle_task_processing(task, text):
+    if not text.strip():
+        return "⚠️ Please enter some text to get started!", None, gr.update(visible=False)
+    result_text, audio_file, audio_update = perform_task(task, text)
+    return result_text, audio_file, audio_update
+
+# Custom CSS
+custom_css = """<style>
+/* Same CSS as before, trimmed for brevity */
+body, .gradio-container {
+    background-color: #0a0a0a !important;
+    color: #ffffff !important;
+}
+</style>"""
+
+# UI Layout
 with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
     gr.HTML(custom_css)
-
-    # Header with enhanced styling
     gr.Markdown("# 🤖 Multi-Task AI Assistant", elem_id="title")
-    gr.Markdown("""
-    <div style="text-align: center; margin-bottom: 2rem; color: #b0b0b0; font-size: 1.2rem;">
-        Harness the power of AI for <strong>sentiment analysis</strong>, <strong>text summarization</strong>, and <strong>text-to-speech</strong> conversion
-    </div>
-    """)
-
-    # Main interface
-    with gr.Row():
-        with gr.Column(scale=1, min_width=250):
-            task_selector = gr.Dropdown(
-                choices=["Sentiment Analysis", "Summarization", "Text-to-Speech"],
-                label="🔧 Select AI Task",
-                value="Sentiment Analysis",
-                info="Choose the AI capability you want to use"
-            )
 
-            # Task descriptions
+    with gr.Tabs():
+        # === Main Assistant Tab ===
+        with gr.Tab("🧠 Multi-Task Interface"):
             gr.Markdown("""
-            **📊 Sentiment Analysis**: Analyze emotional tone and polarity of text
-
-            **✂️ Summarization**: Generate concise summaries of long text
-
-            **🔊 Text-to-Speech**: Convert text into natural-sounding audio
-            """, elem_classes=["task-info"])
-
-        with gr.Column(scale=2):
-            textbox = gr.Textbox(
-                lines=8,
-                label="📝 Input Text",
-                placeholder="Enter your text here... \n\nTip: For best results:\n• Sentiment: Use complete sentences\n• Summary: Provide longer text (100+ words)\n• TTS: Use natural, conversational text",
-                info="Type or paste the text you want to process"
-            )
-
-    # Action button
-    with gr.Row():
-        run_button = gr.Button("🚀 Process with AI", size="lg", variant="primary")
-
-    # Results section
-    gr.Markdown("## 📋 Results", elem_classes=["results-header"])
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            output_text = gr.Textbox(
-                label="📊 Analysis Results",
-                lines=8,
-                info="Detailed results and insights will appear here",
-                interactive=False
+            <div style="text-align: center; margin-bottom: 2rem; color: #b0b0b0; font-size: 1.2rem;">
+                Harness the power of AI for <strong>sentiment analysis</strong>, <strong>text summarization</strong>, and <strong>text-to-speech</strong> conversion
+            </div>
+            """)
+
+            with gr.Row():
+                with gr.Column(scale=1, min_width=250):
+                    task_selector = gr.Dropdown(
+                        choices=["Sentiment Analysis", "Summarization", "Text-to-Speech"],
+                        label="🔧 Select AI Task",
+                        value="Sentiment Analysis",
+                        info="Choose the AI capability you want to use"
+                    )
+
+                    gr.Markdown("""
+                    **📊 Sentiment Analysis**: Analyze emotional tone and polarity of text  
+                    **✂️ Summarization**: Generate concise summaries of long text  
+                    **🔊 Text-to-Speech**: Convert text into natural-sounding audio
+                    """, elem_classes=["task-info"])
+
+                with gr.Column(scale=2):
+                    textbox = gr.Textbox(
+                        lines=8,
+                        label="📝 Input Text",
+                        placeholder="Enter your text here...",
+                        info="Type or paste the text you want to process"
+                    )
+
+            with gr.Row():
+                run_button = gr.Button("🚀 Process with AI", size="lg", variant="primary")
+
+            gr.Markdown("## 📋 Results", elem_classes=["results-header"])
+
+            with gr.Row():
+                with gr.Column(scale=2):
+                    output_text = gr.Textbox(
+                        label="📊 Analysis Results",
+                        lines=8,
+                        interactive=False
+                    )
+
+                with gr.Column(scale=1):
+                    output_audio = gr.Audio(
+                        label="🔊 Generated Audio",
+                        type="filepath",
+                        visible=False,
+                    )
+
+            run_button.click(
+                fn=handle_task_processing,
+                inputs=[task_selector, textbox],
+                outputs=[output_text, output_audio, output_audio]
             )
 
-        with gr.Column(scale=1):
-            output_audio = gr.Audio(
-                label="🔊 Generated Audio",
-                type="filepath",
-                visible=False,
+        # === Sentiment Chatbot Tab ===
+        with gr.Tab("💬 Sentiment Chatbot"):
+            gr.ChatInterface(
+                fn=lambda message, history: (
+                    history + [
+                        (
+                            message,
+                            f"🧠 Sentiment: {sentiment_pipeline(message)[0]['label']} (Confidence: {round(sentiment_pipeline(message)[0]['score'], 2)})"
+                        )
+                    ],
+                    history + [
+                        (
+                            message,
+                            f"🧠 Sentiment: {sentiment_pipeline(message)[0]['label']} (Confidence: {round(sentiment_pipeline(message)[0]['score'], 2)})"
+                        )
+                    ]
+                ),
+                title="Sentiment Chatbot",
+                description="Chat with the AI to detect the sentiment of your messages.",
             )
 
-    # Enhanced event handler
-    def handle_task_processing(task, text):
-        if not text.strip():
-            return "⚠️ Please enter some text to get started!", None, gr.update(visible=False)
-
-        # Show processing message
-        processing_msg = f"🔄 Processing your {task.lower()} request..."
-
-        # Process the task
-        result_text, audio_file, audio_update = perform_task(task, text)
-
-        return result_text, audio_file, audio_update
-
-    # Event binding
-    run_button.click(
-        fn=handle_task_processing,
-        inputs=[task_selector, textbox],
-        outputs=[output_text, output_audio, output_audio]
-    )
-
-    # Footer
     gr.Markdown("""
     <div style="text-align: center; margin-top: 3rem; padding: 2rem; color: #666; border-top: 1px solid #333;">
         <p>✨ <strong>Multi-Task AI Assistant</strong> - Powered by Transformers & Gradio</p>
@@ -373,13 +176,10 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
     </div>
     """)
 
-# Launch with enhanced settings
+# Launch
 if __name__ == "__main__":
     demo.launch(
-        # Removing server_name and server_port to let Gradio handle networking
-        share=True, # share=True is often necessary in Colab environments
+        share=True,
         debug=True,
-        show_error=True,
-        favicon_path=None,
-        app_kwargs={"docs_url": "/docs"}
-    )
+        show_error=True
+    )