Theming and HTML generation

app.py

@@ -4,6 +4,9 @@ import pandas as pd
 import numpy as np
 import os
 from datetime import datetime
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
 # Initialize the classification pipelines
 sentiment_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
@@ -22,6 +25,74 @@ CRITICAL_KEYWORDS = {
     'sensitive': ['racist', 'discrimination', 'harassment', 'abuse', 'offensive']
 }
 
+def create_charts(df):
+    """
+    Create visualization charts using Plotly
+    """
+    # Create subplot figure
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=("Urgency Distribution", "Sentiment Analysis",
+                       "Brand Impact Assessment", "Critical Issues Breakdown"),
+        specs=[[{"type": "pie"}, {"type": "pie"}],
+               [{"type": "pie"}, {"type": "bar"}]]
+    )
+
+    # 1. Urgency Distribution Pie Chart
+    urgency_counts = df['urgency'].value_counts()
+    fig.add_trace(
+        go.Pie(labels=urgency_counts.index,
+               values=urgency_counts.values,
+               marker=dict(colors=['#ff0000', '#ff6666', '#ffcccc', '#ffe6e6'])),
+        row=1, col=1
+    )
+
+    # 2. Sentiment Analysis Pie Chart
+    sentiment_counts = df['sentiment'].value_counts()
+    fig.add_trace(
+        go.Pie(labels=sentiment_counts.index,
+               values=sentiment_counts.values,
+               marker=dict(colors=['#00cc00', '#ff0000', '#cccccc'])),
+        row=1, col=2
+    )
+
+    # 3. Brand Impact Pie Chart
+    impact_counts = df['brand_impact'].value_counts()
+    fig.add_trace(
+        go.Pie(labels=impact_counts.index,
+               values=impact_counts.values,
+               marker=dict(colors=['#ff0000', '#ff9933', '#ffcc00'])),
+        row=2, col=1
+    )
+
+    # 4. Critical Issues Bar Chart
+    critical_issues = df['critical_issues'].str.split('|', expand=True).stack()
+    critical_counts = critical_issues[critical_issues != 'none'].value_counts()
+    fig.add_trace(
+        go.Bar(x=critical_counts.index,
+               y=critical_counts.values,
+               marker_color='#ff0000'),
+        row=2, col=2
+    )
+
+    # Update layout
+    fig.update_layout(
+        height=800,
+        showlegend=True,
+        title_text="Social Media Analysis Dashboard",
+        title_x=0.5,
+        title_font_size=20,
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)'
+    )
+
+    # Save the figure
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    chart_file = f"analysis_dashboard_{timestamp}.html"
+    fig.write_html(chart_file)
+
+    return chart_file
+
 def classify_text(text, labels, classifier):
     """
     Perform zero-shot classification on text
@@ -73,22 +144,22 @@ def generate_recommendations(row):
     recommendations = []
 
     if row['urgency'] == 'critical':
-        recommendations.append("Immediate escalation required - Route to crisis management team")
+        recommendations.append("🚨 IMMEDIATE ESCALATION REQUIRED - Route to crisis management team")
 
     if 'security' in row['critical_issues']:
-        recommendations.append("Engage security team for immediate investigation")
+        recommendations.append("🔒 Engage security team for immediate investigation")
     elif 'fraud' in row['critical_issues']:
-        recommendations.append("Route to fraud prevention team for investigation")
+        recommendations.append("⚠️ Route to fraud prevention team for investigation")
     elif 'compliance' in row['critical_issues']:
-        recommendations.append("Escalate to legal/compliance team for review")
+        recommendations.append("📜 Escalate to legal/compliance team for review")
 
     if row['brand_impact'] == 'severe':
-        recommendations.append("Engage PR team for reputation management strategy")
+        recommendations.append("📢 Engage PR team for reputation management strategy")
 
     if row['sentiment'] == 'negative':
-        recommendations.append("Priority customer outreach needed for resolution")
+        recommendations.append("🔥 Priority customer outreach needed for resolution")
 
-    return ' | '.join(recommendations) if recommendations else "Standard response protocol"
+    return ' | '.join(recommendations) if recommendations else "✅ Standard response protocol"
 
 def process_csv(file):
     """
@@ -100,7 +171,7 @@ def process_csv(file):
 
         # Verify required columns
         if 'post_id' not in df.columns or 'text' not in df.columns:
-            return None, "Error: CSV must contain 'post_id' and 'text' columns"
+            return None, None, "Error: CSV must contain 'post_id' and 'text' columns"
 
         # Perform comprehensive analysis
         analysis_results = []
@@ -135,6 +206,9 @@ def process_csv(file):
         # Generate recommendations
         results_df['recommendations'] = results_df.apply(generate_recommendations, axis=1)
 
+        # Create visualization dashboard
+        dashboard_file = create_charts(results_df)
+
         # Add analysis timestamp
         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
         output_file = f"social_media_analysis_{timestamp}.csv"
@@ -149,34 +223,51 @@ def process_csv(file):
         severe_impact = len(results_df[results_df['brand_impact'] == 'severe'])
 
         summary = f"""
-Analysis Summary:
-----------------
+🎯 Real-Time Social Media Intelligence Report
+==========================================
+
+📊 Key Metrics:
+-------------
 Total Posts Analyzed: {total_posts}
 Critical Issues Requiring Immediate Attention: {critical_posts}
 Negative Sentiment Posts: {negative_sentiment}
 Severe Brand Impact Posts: {severe_impact}
 
-The detailed analysis has been saved to: {output_file}
+⚡ Quick Actions Required:
+----------------------
+- {critical_posts} posts need immediate escalation
+- {severe_impact} posts require PR team intervention
+- {negative_sentiment} posts need customer satisfaction follow-up
+
+💡 AI-Powered Analysis Complete:
+----------------------------
+Detailed analysis saved to: {output_file}
+Interactive dashboard saved to: {dashboard_file}
         """
 
-        return output_file, summary
+        return output_file, dashboard_file, summary
 
     except Exception as e:
-        return None, f"Error processing CSV: {str(e)}"
+        return None, None, f"Error processing CSV: {str(e)}"
 
-# Create example CSV file
+# Create example CSV file with more diverse cases
 def create_example_file():
     """
     Create an example CSV file for demonstration
     """
     example_data = {
-        'post_id': range(1, 6),
+        'post_id': range(1, 11),
         'text': [
-            "I absolutely love your product! Best purchase ever!",
-            "My account appears to have been hacked and unauthorized charges made. Need immediate assistance!",
-            "This service is terrible, never using it again. Going to share my experience on social media.",
-            "Your app is constantly crashing. Please fix this issue.",
-            "Concerned about potential compliance violations in your recent policy update."
+            "Just experienced a major security breach! My account was hacked and sensitive data leaked. This is unacceptable! #cybersecurity #breach",
+            "Thank you for the amazing customer service! The team went above and beyond to help me. Truly impressed! 🌟",
+            "Your latest app update is constantly crashing. Can't access my account for 3 days now. Fix this ASAP!",
+            "Noticed some suspicious charges on my account. Possible fraud? Need immediate assistance! 🚨",
+            "Love the new features you've added! Makes my work so much easier. Keep innovating! 👏",
+            "Planning to file a legal complaint due to repeated policy violations. This needs attention.",
+            "System down again? This is the third time this week. Considering switching to your competitor.",
+            "Your product has completely transformed our business operations. Best investment ever! 🚀",
+            "Experiencing discrimination from your staff. This is unacceptable and I'm reporting it.",
+            "Warning to others: Potential scam detected in recent transactions. Be careful!"
         ]
     }
     df = pd.DataFrame(example_data)
@@ -184,38 +275,54 @@ def create_example_file():
     df.to_csv(example_file, index=False)
     return example_file
 
-# Create the example file before launching the interface
+# Create the example file
 example_file = create_example_file()
 
+# Create Gradio interface with custom theme
+theme = gr.themes.Base(
+    primary_hue="red",
+    secondary_hue="red",
+)
+
+css = """
+.gradio-container {
+    background: linear-gradient(to bottom right, #ffffff, #ffecec);
+}
+"""
+
 # Create Gradio interface
 iface = gr.Interface(
     fn=process_csv,
     inputs=[
         gr.File(
-            label="Upload CSV file",
+            label="Upload CSV File 📁",
             file_types=[".csv"]
         )
     ],
     outputs=[
-        gr.File(label="Download Detailed Analysis"),
-        gr.Textbox(label="Analysis Summary", max_lines=10)
+        gr.File(label="Download Detailed Analysis Report 📊"),
+        gr.File(label="Download Interactive Dashboard 📈"),
+        gr.Textbox(label="Real-Time Analysis Summary 📱", max_lines=15)
     ],
-    title="Enhanced Social Media Analysis System",
+    title="🚀 NoCode Ninjas: AI-Powered Social Media Intelligence Platform",
     description="""
-    Comprehensive social media post analyzer that provides:
-    - Sentiment Analysis
-    - Urgency Classification
-    - Brand Impact Assessment
-    - Critical Issue Detection
-    - Actionable Recommendations
-
-    Input CSV format:
-    - Required columns: 'post_id', 'text'
-    - Each row should contain a unique post ID and the post text
+    ### Enterprise-Grade Social Media Analytics with Advanced AI
+
+    Transform your social media monitoring with our cutting-edge AI analysis platform:
+
+    🎯 **Real-Time Sentiment Analysis**
+    🔍 **Urgent Issue Detection**
+    ⚡ **Instant Crisis Alerts**
+    📊 **Brand Impact Assessment**
+    🤖 **AI-Driven Recommendations**
+
+    *Trusted by leading brands for proactive social media management and crisis prevention.*
     """,
     examples=[
         [example_file]
-    ]
+    ],
+    theme=theme,
+    css=css
 )
 
 if __name__ == "__main__":
@@ -223,4 +330,4 @@ if __name__ == "__main__":
         iface.launch()
     finally:
         if os.path.exists(example_file):
-            os.remove(example_file)
+            os.remove(example_file)