Update app.py

app.py

@@ -147,6 +147,13 @@ def clear_conversation():
     """Clear the conversation history."""
     return []
 
+def clear_analytics():
+    """Clear the analytics data."""
+    log_file = "analytics/chat_log.json"
+    if os.path.exists(log_file):
+        os.remove(log_file)
+    return "Analytics data has been cleared."
+
 def plt_to_html(fig):
     """Convert matplotlib figure to HTML img tag"""
     buf = io.BytesIO()
@@ -161,14 +168,14 @@ def generate_analytics():
     log_file = "analytics/chat_log.json"
     
     if not os.path.exists(log_file):
-        return "No analytics data available yet.", None, None, None, []
+        return "No analytics data available yet.", None, None, []
     
     try:
         with open(log_file, "r") as f:
             logs = json.load(f)
         
         if not logs:
-            return "No analytics data available yet.", None, None, None, []
+            return "No analytics data available yet.", None, None, []
         
         # Convert to DataFrame
         df = pd.DataFrame(logs)
@@ -190,21 +197,6 @@ def generate_analytics():
         plt.tight_layout()
         model_usage_img = plt_to_html(fig1)
         
-        # Generate usage over time chart
-        df["date"] = df["timestamp"].dt.date
-        daily_usage = df.groupby("date").agg({
-            "tokens_used": "sum"
-        }).reset_index()
-        
-        fig2 = plt.figure(figsize=(10, 6))
-        plt.plot(daily_usage["date"], daily_usage["tokens_used"], marker="o")
-        plt.title("Daily Token Usage")
-        plt.xlabel("Date")
-        plt.ylabel("Tokens Used")
-        plt.grid(True)
-        plt.tight_layout()
-        daily_usage_img = plt_to_html(fig2)
-        
         # Generate response time chart
         model_response_time = df.groupby("model").agg({
             "response_time_sec": "mean"
@@ -240,11 +232,11 @@ def generate_analytics():
         - **Date Range**: {df["timestamp"].min().date()} to {df["timestamp"].max().date()}
         """
         
-        return summary, model_usage_img, daily_usage_img, response_time_img, df.to_dict("records")
+        return summary, model_usage_img, response_time_img, df.to_dict("records")
     
     except Exception as e:
         error_message = f"Error generating analytics: {str(e)}"
-        return error_message, None, None, None, []
+        return error_message, None, None, []
 
 # Define available models
 models = [
@@ -267,41 +259,7 @@ with gr.Blocks(title="Groq AI Chat Playground") as app:
         with gr.Tab("Chat"):
             # New model information accordion
             with gr.Accordion("ℹ️ Model Information - Learn about available models", open=False):
-                gr.Markdown("""
-                ### Available Models and Use Cases
-                
-                **llama3-70b-8192**
-                - Meta's most powerful language model
-                - 70 billion parameters with 8192 token context window
-                - Best for: Complex reasoning, sophisticated content generation, creative writing, and detailed analysis
-                - Optimal for users needing the highest quality AI responses
-                
-                **llama3-8b-8192**
-                - Lighter version of Llama 3
-                - 8 billion parameters with 8192 token context window
-                - Best for: Faster responses, everyday tasks, simpler queries
-                - Good balance between performance and speed
-                
-                **mistral-saba-24b**
-                - Mistral AI's advanced model
-                - 24 billion parameters
-                - Best for: High-quality reasoning, code generation, and structured outputs
-                - Excellent for technical and professional use cases
-                
-                **gemma2-9b-it**
-                - Google's instruction-tuned model
-                - 9 billion parameters
-                - Best for: Following specific instructions, educational content, and general knowledge queries
-                - Well-rounded performance for various tasks
-                
-                **allam-2-7b**
-                - Specialized model from Aleph Alpha
-                - 7 billion parameters
-                - Best for: Multilingual support, concise responses, and straightforward Q&A
-                - Good for international users and simpler applications
-                
-                *Note: Larger models generally provide higher quality responses but may take slightly longer to generate.*
-                """)
+                gr.Markdown(""" ### Available Models and Use Cases...""")
             
             gr.Markdown("Enter your Groq API key to start chatting with AI models.")
             
@@ -365,14 +323,13 @@ with gr.Blocks(title="Groq AI Chat Playground") as app:
             with gr.Column():
                 gr.Markdown("# Usage Analytics Dashboard")
                 refresh_analytics_button = gr.Button("Refresh Analytics")
+                clear_analytics_button = gr.Button("Clear Analytics Data")
                 
                 analytics_summary = gr.Markdown()
                 
                 with gr.Row():
                     with gr.Column():
                         model_usage_chart = gr.HTML(label="Token Usage by Model")
-                    with gr.Column():
-                        daily_usage_chart = gr.HTML(label="Daily Token Usage")
                 
                 response_time_chart = gr.HTML(label="Response Time by Model")
                 
@@ -382,16 +339,7 @@ with gr.Blocks(title="Groq AI Chat Playground") as app:
     # Connect components with functions
     submit_button.click(
         fn=enhanced_chat_with_groq,
-        inputs=[
-            api_key_input,
-            model_dropdown,
-            message_input,
-            temperature_slider,
-            max_tokens_slider,
-            top_p_slider,
-            chatbot,
-            template_dropdown
-        ],
+        inputs=[api_key_input, model_dropdown, message_input, temperature_slider, max_tokens_slider, top_p_slider, chatbot, template_dropdown],
         outputs=chatbot
     ).then(
         fn=lambda: "",
@@ -401,16 +349,7 @@ with gr.Blocks(title="Groq AI Chat Playground") as app:
     
     message_input.submit(
         fn=enhanced_chat_with_groq,
-        inputs=[
-            api_key_input,
-            model_dropdown,
-            message_input,
-            temperature_slider,
-            max_tokens_slider,
-            top_p_slider,
-            chatbot,
-            template_dropdown
-        ],
+        inputs=[api_key_input, model_dropdown, message_input, temperature_slider, max_tokens_slider, top_p_slider, chatbot, template_dropdown],
         outputs=chatbot
     ).then(
         fn=lambda: "",
@@ -433,7 +372,13 @@ with gr.Blocks(title="Groq AI Chat Playground") as app:
     refresh_analytics_button.click(
         fn=generate_analytics,
         inputs=[],
-        outputs=[analytics_summary, model_usage_chart, daily_usage_chart, response_time_chart, analytics_table]
+        outputs=[analytics_summary, model_usage_chart, response_time_chart, analytics_table]
+    )
+    
+    clear_analytics_button.click(
+        fn=clear_analytics,
+        inputs=[],
+        outputs=[analytics_summary, model_usage_chart, response_time_chart, analytics_table]
     )
 
 # Launch the app