Update app.py

app.py

@@ -1,5 +1,6 @@
 import os
 import zipfile
+import json
 from dotenv import load_dotenv
 from groq import Groq
 import chromadb
@@ -9,6 +10,7 @@ from sentence_transformers import CrossEncoder
 import gradio as gr
 from datetime import datetime 
 from huggingface_hub import hf_hub_download, HfApi
+from pathlib import Path
 
 # Load environment variables and initialize clients
 load_dotenv()
@@ -162,20 +164,55 @@ def get_context(message):
     print(f"\nFinal context length: {total_chars} characters")
     return context
 
+def log_conversation(timestamp, user_message, assistant_response, model_name, context, error=None):
+    """Log conversation details to a JSON file"""
+    log_dir = Path("logs")
+    log_dir.mkdir(exist_ok=True)
+    
+    # Create a log entry
+    log_entry = {
+        "timestamp": timestamp,
+        "model_name": model_name,
+        "user_message": user_message,
+        "assistant_response": assistant_response,
+        "context": context,
+        "error": str(error) if error else None
+    }
+    
+    # Use the current date for the log file name
+    current_date = datetime.now().strftime("%Y-%m-%d")
+    log_file = log_dir / f"conversation_log_{current_date}.json"
+    
+    try:
+        # Load existing logs if file exists
+        if log_file.exists():
+            with open(log_file, 'r', encoding='utf-8') as f:
+                logs = json.load(f)
+        else:
+            logs = []
+        
+        # Append new log entry
+        logs.append(log_entry)
+        
+        # Write updated logs
+        with open(log_file, 'w', encoding='utf-8') as f:
+            json.dump(logs, f, ensure_ascii=False, indent=2)
+            
+        print(f"\n✍️ Logged conversation to {log_file}")
+    except Exception as e:
+        print(f"\n⚠️ Error logging conversation: {str(e)}")
+
 def chat_response(message, history, model_name):
     """Chat response function for Gradio interface"""
     try:
-        # Get context
+        # Get context and timestamp
         context = get_context(message)
-        
-        # Get current time and date
-        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         
         # Build messages list
         messages = [{
             "role": "system",
             "content": f"""You are an AI assistant for the British Antarctic Survey (BAS). Your responses should be based ONLY on the context provided below.
-
 IMPORTANT INSTRUCTIONS:
 1. ALWAYS thoroughly check the provided context before saying you don't have information
 2. If you find ANY relevant information in the context, use it - even if it's not complete
@@ -183,18 +220,16 @@ IMPORTANT INSTRUCTIONS:
 4. When citing sources, put them on a new line after the relevant information like this:
    Here is some information about BAS.
    Source: https://www.bas.ac.uk/example
-
 5. Do not say things like:
    - "I don't have access to real-time information"
    - "I cannot browse the internet"
    Instead, share what IS in the context, and only say "I don't have enough information" if you truly find nothing relevant to the users question.
-
 6. Keep responses:
    - With emojis where appropriate
    - Without duplicate source citations
    - Based strictly on the context below
 
-Current Time: {current_time}
+Current Time: {timestamp}
 
 Context: {context}"""
         }]
@@ -242,12 +277,11 @@ Context: {context}"""
                     # Create collapsible thinking section
                     if thinking_process:
                         final_response = f"""<details>
-<summary>🤔 Click here to see the 'thinking' process</summary>
-<hr>
+<summary>🤔 <u>Click to see 'thinking' process</u></summary>
 <div style="font-size: 0.9em;">
-<i>💭{thinking_process}</i>
+<i>💭{thinking_process}💭</i>
 </div>
-<hr>
+<hr style="margin: 0; height: 2px;">
 </details>
 
 {final_response}"""
@@ -259,49 +293,64 @@ Context: {context}"""
                 else:
                     final_response += content
                     yield final_response
-                    
+        
+        
+        log_conversation(timestamp, message, final_response, model_name, context)
         print("\n=== LLM Response End ===\n")
 
     except Exception as e:
         error_msg = f"An error occurred: {str(e)}"
         print(f"\nERROR: {error_msg}")
+        
+        log_conversation(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 
+                       message, error_msg, model_name, context, error=e)
         yield error_msg
-
+        
 if __name__ == "__main__":
     try:
         print("\n=== Starting Application ===")
         
-        # Initialise system
+        
+        Path("logs").mkdir(exist_ok=True)
+        
+        
         print("Initialising ChromaDB...")
         chroma_client, collection, reranker = initialize_system()
         print(f"Found {collection.count()} documents in collection")
         
         print("\nCreating Gradio interface...")
         
-        # Create a simple Gradio interface
+        
         demo = gr.Blocks()
         
         with demo:
-            gr.Markdown("# British Antarctic Survey Website Chat Assistant 🌍")
-            gr.Markdown("Ask questions about the BAS website. This system accesses text data from 11,982 unique BAS URLs (6GB vector database) 📚")
-            # Add model selector
+            gr.Markdown("# 🌍❄️British Antarctic Survey Website Chat Assistant 🧊🤖")
+            gr.Markdown("Accesses text data from 11,982 unique BAS URLs (6GB [Vector Database](https://huggingface.co/datasets/Mr-Geo/chroma_db/tree/main/) 📚 extracted 02/02/2025) | Created with open source technologies: [Gradio](https://gradio.app) for the interface 🎨, [Groq](https://groq.com) API for rapid LLM processing ⚡, and [Chroma](https://www.trychroma.com/) as the vector database 💻🔍")
             model_selector = gr.Dropdown(
                 choices=[
-                    "llama-3.3-70b-versatile",
                     "llama-3.1-8b-instant",
+                    "llama-3.3-70b-versatile",
+                    "llama-3.3-70b-specdec",
                     "mixtral-8x7b-32768",
                     "deepseek-r1-distill-llama-70b"
                 ],
-                value="llama-3.3-70b-versatile",
-                label="Select AI Model 🤖",
-                info="Choose which AI model to use for responses - select 'deepseek-r1-distill-llama-70b' for the 'thinking' AI search"
+                value="llama-3.1-8b-instant",
+                label="Select AI Large Language Model 🤖",
+                info="Choose which AI model to use for responses - Select 'deepseek' for the 'thinking' AI search 💭"
             )
             
-            chatbot = gr.Chatbot(height=600)
-            msg = gr.Textbox(placeholder="Ask a question or select an example question... 🤔", label="Your question")
-            clear = gr.Button("Clear 🧹")
+            chatbot = gr.Chatbot(height=550)
+            with gr.Row(equal_height=True):
+                msg = gr.Textbox(
+                    placeholder="What would you like to know? Or choose an example question...❓",
+                    label="Your question",
+                    show_label=True,
+                    container=True,
+                    scale=20
+                )
+                send = gr.Button("Send ⬆️", scale=1, min_width=50)
+            clear = gr.Button("Clear chat history 🧹")
             
-            # Add example questions
             gr.Examples(
                 examples=[
                     "What research stations does BAS operate in Antarctica? 🏔️",
@@ -324,11 +373,13 @@ if __name__ == "__main__":
             msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
                 bot, [chatbot, model_selector], chatbot
             )
+            send.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+                bot, [chatbot, model_selector], chatbot
+            )
             
             clear.click(lambda: None, None, chatbot, queue=False)
-        
-        # Launch with minimal configuration
-        demo.queue()
+
+            gr.Markdown("<footer style='text-align: center; margin-top: 5px;'>🤖 AI-generated content; while the Chat Assistant strives for accuracy, errors may occur; please thoroughly check critical information 🤖<br>⚠️ <strong><u>Disclaimer: This system was not produced by the British Antarctic Survey (BAS) and AI generated output does not reflect the views or opinions of BAS</u></strong> ⚠️ <br>(just a bit of fun :D)</footer>")
         demo.launch(
             server_name="0.0.0.0",
             server_port=7860,