app.py

import os
import zipfile
import json
from dotenv import load_dotenv
from groq import Groq
import chromadb
from chromadb.config import Settings
import torch
from sentence_transformers import CrossEncoder
import gradio as gr
from datetime import datetime 
from huggingface_hub import hf_hub_download, HfApi, CommitOperationAdd
from pathlib import Path
import tempfile
import spaces  # for ZeroGPU
import requests  # for IP geolocation
import time

# Load environment variables and initialize clients
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
client = Groq(api_key=GROQ_API_KEY)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Get the token from environment variables
hf_token = os.getenv("HF_TOKEN")

# Initialize global variables
chroma_client = None
collection = None
reranker = None
embedding_function = None

def load_chroma_db():
    print("Using ChromaDB from Hugging Face dataset...")
    
    # Download the zipped database from Hugging Face
    zip_path = hf_hub_download(
        repo_id="Mr-Geo/chroma_db",
        filename="chroma_db.zip",
        repo_type="dataset",
        use_auth_token=hf_token
    )
    print(f"Downloaded database zip to: {zip_path}")
    
    # Extract to a temporary directory
    extract_dir = "/tmp"  # This will create /tmp/chroma_db
    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        print("Zip contents:", zip_ref.namelist())
        zip_ref.extractall(extract_dir)
    
    db_path = os.path.join(extract_dir, "chroma_db")
    print(f"Using ChromaDB path: {db_path}")
    print(f"Directory contents: {os.listdir(db_path)}")
    
    db = chromadb.PersistentClient(
        path=db_path,
        settings=Settings(
            anonymized_telemetry=False,
            allow_reset=True,
            is_persistent=True
        )
    )
    
    # Debug: Print collections
    collections = db.list_collections()
    print("Available collections:", collections)
    
    return db

# Check if running locally
if os.path.exists("./chroma_db/chroma.sqlite3"):
    print("Using local ChromaDB setup...")
    db = chromadb.PersistentClient(
        path="./chroma_db",
        settings=Settings(
            anonymized_telemetry=False,
            allow_reset=True,
            is_persistent=True
        )
    )
else:
    # Load from Hugging Face dataset
    db = load_chroma_db()

def initialize_system_sync():
    """Initialize the system components without GPU decoration"""
    global chroma_client, collection, reranker, embedding_function
    
    # Add GPU diagnostics
    print("\n=== GPU Diagnostics ===")
    print(f"CUDA available: {torch.cuda.is_available()}")
    if torch.cuda.is_available():
        print(f"Current CUDA device: {torch.cuda.current_device()}")
        print(f"Device name: {torch.cuda.get_device_name()}")
        print(f"Device memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")
    print("=====================\n")
    
    # Use the same ChromaDB client that was loaded from HF
    chroma_client = db  # Use the global db instance we created
    
    # Initialize the embedding function with retries
    max_retries = 3
    retry_delay = 5  # seconds
    
    for attempt in range(max_retries):
        try:
            print(f"\nAttempt {attempt + 1} of {max_retries} to initialize embedding function...")
            embedding_function = chromadb.utils.embedding_functions.SentenceTransformerEmbeddingFunction(
                model_name="sentence-transformers/all-mpnet-base-v2",
                device=DEVICE
            )
            break
        except Exception as e:
            print(f"Error initializing embedding function: {str(e)}")
            if attempt < max_retries - 1:
                print(f"Retrying in {retry_delay} seconds...")
                time.sleep(retry_delay)
            else:
                raise RuntimeError("Failed to initialize embedding function after multiple attempts")
    
    # Get the collection
    print("Getting collection...")
    collection = chroma_client.get_collection(name="website_content", embedding_function=embedding_function)
    print(f"Found {collection.count()} documents in collection")
    
    # Initialize the reranker with retries
    for attempt in range(max_retries):
        try:
            print(f"\nAttempt {attempt + 1} of {max_retries} to initialize reranker...")
            reranker = CrossEncoder(
                'cross-encoder/ms-marco-MiniLM-L-6-v2',
                device=DEVICE,
                max_length=512  # Add explicit max_length
            )
            if torch.cuda.is_available():
                reranker.model.to('cuda')
                print("Reranker moved to GPU")
            break
        except Exception as e:
            print(f"Error initializing reranker: {str(e)}")
            if attempt < max_retries - 1:
                print(f"Retrying in {retry_delay} seconds...")
                time.sleep(retry_delay)
            else:
                raise RuntimeError("Failed to initialize reranker after multiple attempts")

@spaces.GPU(memory="40g")
def initialize_system():
    """GPU-decorated initialization for Gradio context"""
    initialize_system_sync()

@spaces.GPU(memory="40g")  # Add GPU decorator for get_context
def get_context(message):
    global collection, reranker  # Access global variables
    
    results = collection.query(
        query_texts=[message],
        n_results=500,
        include=["metadatas", "documents", "distances"]
    )
    
    print(f"\n=== Search Results ===")
    print(f"Initial ChromaDB results found: {len(results['documents'][0])}")
    
    # Rerank all results
    rerank_pairs = [(message, doc) for doc in results['documents'][0]]
    rerank_scores = reranker.predict(rerank_pairs)
    
    # Create list of results with scores
    all_results = []
    url_chunks = {}  # Group chunks by URL
    
    # Group chunks by URL and store their scores
    for score, doc, metadata in zip(rerank_scores, results['documents'][0], results['metadatas'][0]):
        url = metadata['url']
        if url not in url_chunks:
            url_chunks[url] = []
        url_chunks[url].append({'text': doc, 'metadata': metadata, 'score': score})
    
    # For each URL, select the best chunks while maintaining diversity
    for url, chunks in url_chunks.items():
        # Sort chunks for this URL by score
        chunks.sort(key=lambda x: x['score'], reverse=True)
        
        # Take up to 5 chunks per URL, but only if their scores are good
        selected_chunks = []
        for chunk in chunks[:5]:  # 5 chunks per URL
            # Only include if score is decent
            if chunk['score'] > -10:  # Increased threshold to ensure higher relevance
                selected_chunks.append(chunk)
        
        # Add selected chunks to final results
        all_results.extend(selected_chunks)
    
    # Sort all results by score for final ranking
    all_results.sort(key=lambda x: x['score'], reverse=True)
    
    # Take only top 20 results maximum
    all_results = all_results[:20]
    
    print(f"\nFinal results after reranking and filtering: {len(all_results)}")
    if all_results:
        print("\nTop Similarity Scores and URLs:")
        for i, result in enumerate(all_results[:20], 1):  # Show only top 20 in logs
            print(f"{i}. Score: {result['score']:.4f} - URL: {result['metadata']['url']}")
    print("=" * 50)
    
    # Build context from filtered results
    context = "\nRelevant Information:\n"
    total_chars = 0
    max_chars = 30000  # To ensure we don't exceed token limits
    
    for result in all_results:
        chunk_text = f"\nSource: {result['metadata']['url']}\n{result['text']}\n"
        if total_chars + len(chunk_text) > max_chars:
            break
        context += chunk_text
        total_chars += len(chunk_text)
    
    print(f"\nFinal context length: {total_chars} characters")
    return context

def get_ip_info(ip_address):
    """Get geolocation info for an IP address"""
    if not ip_address:
        return {"country": "Unknown", "city": "Unknown", "region": "Unknown"}
        
    # Handle local/private IPs
    if ip_address in ['127.0.0.1', 'localhost', '0.0.0.0'] or ip_address.startswith(('10.', '172.', '192.168.')):
        return {"country": "Local Network", "city": "Local", "region": "Local"}
    
    try:
        # Add user-agent to be a good API citizen
        headers = {
            'User-Agent': 'BAS-Website-Chat/1.0'
        }
        
        response = requests.get(
            f'https://ipapi.co/{ip_address}/json/',
            headers=headers,
            timeout=5  # 5 second timeout
        )
        
        if response.status_code == 200:
            data = response.json()
            
            # Check for error responses
            if 'error' in data:
                print(f"IP API error: {data.get('reason', 'Unknown error')}")
                return {"country": "Unknown", "city": "Unknown", "region": "Unknown"}
                
            return {
                "country": data.get("country_name", "Unknown"),
                "city": data.get("city", "Unknown"),
                "region": data.get("region", "Unknown"),
                "latitude": data.get("latitude"),
                "longitude": data.get("longitude"),
                "timezone": data.get("timezone"),
                "org": data.get("org")
            }
        else:
            print(f"IP API returned status code: {response.status_code}")
            
    except requests.exceptions.Timeout:
        print(f"Timeout getting IP info for {ip_address}")
    except requests.exceptions.RequestException as e:
        print(f"Error getting IP info: {str(e)}")
    except Exception as e:
        print(f"Unexpected error getting IP info: {str(e)}")
        
    return {"country": "Unknown", "city": "Unknown", "region": "Unknown"}

def log_conversation(timestamp, user_message, assistant_response, model_name, context, error=None, client_ip=None):
    """Log conversation details to JSON file - local directory or HuggingFace Dataset repository"""
    # Get IP geolocation
    ip_info = get_ip_info(client_ip) if client_ip else {"country": "Unknown", "city": "Unknown"}
    
    # 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,
        "client_ip": client_ip,
        "location": ip_info
    }
    
    # Check if running on Hugging Face Spaces
    is_hf_space = os.getenv('SPACE_ID') is not None
    current_date = datetime.now().strftime("%Y-%m-%d")
    
    if is_hf_space:
        try:
            # Initialize Hugging Face API
            api = HfApi(token=hf_token)
            filename = f"conversation_logs/daily_{current_date}.json"
            
            # Check if the dataset repository exists, if not create it
            try:
                api.repo_info(repo_id="Mr-Geo/chroma_db", repo_type="dataset")
            except Exception:
                api.create_repo(
                    repo_id="Mr-Geo/chroma_db",
                    repo_type="dataset",
                    private=True
                )
            
            try:
                # Try to download existing file
                existing_file = api.hf_hub_download(
                    repo_id="Mr-Geo/chroma_db",
                    filename=filename,
                    repo_type="dataset",
                    token=hf_token
                )
                # Load existing logs
                with open(existing_file, 'r', encoding='utf-8') as f:
                    logs = json.load(f)
            except Exception:
                # File doesn't exist yet, start with empty list
                logs = []
            
            # Append new log entry
            logs.append(log_entry)
            
            # Create temporary file with updated logs
            with tempfile.NamedTemporaryFile(mode='w', encoding='utf-8', delete=False, suffix='.json') as temp_file:
                json.dump(logs, temp_file, ensure_ascii=False, indent=2)
                temp_file_path = temp_file.name
            
            # Push to the dataset repository
            operations = [
                CommitOperationAdd(
                    path_in_repo=filename,
                    path_or_fileobj=temp_file_path
                )
            ]
            
            api.create_commit(
                repo_id="Mr-Geo/chroma_db",
                repo_type="dataset",
                operations=operations,
                commit_message=f"Update conversation logs for {current_date}"
            )
            
            # Clean up temporary file
            os.unlink(temp_file_path)
            
        except Exception as e:
            print(f"\n⚠️ Error logging conversation to HuggingFace: {str(e)}")
    else:
        # Local environment - save to file
        try:
            log_dir = Path("logs")
            log_dir.mkdir(exist_ok=True)
            
            log_file = log_dir / f"conversation_log_{current_date}.json"
            
            # 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)
                
        except Exception as e:
            print(f"\n⚠️ Error logging conversation locally: {str(e)}")

def chat_response(message, history, model_name, request: gr.Request):
    """Chat response function for Gradio interface"""
    try:
        # Get client IP address with better proxy handling
        client_ip = None
        if request:
            # Try to get real IP from headers in order of reliability
            client_ip = (
                request.headers.get('X-Forwarded-For', '').split(',')[0].strip() or
                request.headers.get('X-Real-IP') or
                request.headers.get('CF-Connecting-IP') or  # Cloudflare
                request.client.host
            )
            print(f"\nClient IP detected: {client_ip}")
            print(f"Request headers: {request.headers}")
        
        # Append 'at BAS' to the user's message
        message += " at BAS"
        
        # Get context and timestamp
        context = get_context(message)
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        
        # Build messages list starting with a clean system message for history
        messages = []
        
        # Add history first without context
        if history:
            for h in history:
                messages.append({"role": h["role"], "content": str(h["content"])})
        
        # Add current message
        messages.append({"role": "user", "content": str(message)})
        
        # Insert system message with context at the beginning
        messages.insert(0, {
            "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.
3. If you find time-sensitive information in the context, share it - it's current as of when the context was retrieved.
4. When citing sources, you MUST always provide the URL source 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 user's question.
6. Keep responses:
   - With emojis where appropriate.
   - Without duplicate source citations.
   - Based on the context below.

Current Time: {timestamp}

Context: {context}"""
        })
        
        print("\n\n==========START Contents of the message being sent to the LLM==========\n")
        print(messages)
        print("\n\n==========END Contents of the message being sent to the LLM==========\n")
        
        # Get response
        response = ""
        completion = client.chat.completions.create(
            model=model_name,
            messages=messages,
            temperature=0.7,
            max_tokens=2500,
            top_p=0.95,
            stream=True
        )
        
        print("\n=== LLM Response Start ===")
        thinking_process = ""
        final_response = ""
        is_thinking = False
        
        for chunk in completion:
            if chunk.choices[0].delta.content:
                content = chunk.choices[0].delta.content
                print(content, end='', flush=True)
                
                # Check for thinking tags
                if "<think>" in content:
                    is_thinking = True
                    continue
                elif "</think>" in content:
                    is_thinking = False
                    # Create collapsible thinking section
                    if thinking_process:
                        final_response = f"""<details>
<summary>🤔 <u>Click to see 'thinking' process</u></summary>
<div style="font-size: 0.9em;">
<i>💭{thinking_process}</i>
</div>
<hr style="margin: 0; height: 2px;">
</details>

{final_response}"""
                    continue
                
                # Append content to appropriate section
                if is_thinking:
                    thinking_process += content
                else:
                    final_response += content
                    yield final_response
        
        log_conversation(timestamp, message, final_response, model_name, context, client_ip=client_ip)
        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, client_ip=client_ip)
        yield error_msg
        
if __name__ == "__main__":
    try:
        print("\n=== Starting Application ===")
        
        Path("logs").mkdir(exist_ok=True)
        
        print("Initialising ChromaDB...")
        initialize_system_sync()  # Use the synchronous version for initial setup
        
        if collection is None:
            raise RuntimeError("Failed to initialize collection")
            
        print(f"Found {collection.count()} documents in collection")
        
        print("\nCreating Gradio interface...")
        
        demo = gr.Blocks()
        
        with demo:
            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 UI 🎨, [Hugging Face](https://huggingface.co/) models for embeddings ⚡, and [Chroma](https://www.trychroma.com/) as the vector database 💻")
            model_selector = gr.Dropdown(
                choices=[
                    ("Llama 3.3 - Versatile 🦙✨", "llama-3.3-70b-versatile"),                    
                    ("Llama 4 - Latest 🚀", "meta-llama/llama-4-scout-17b-16e-instruct"),
                    ("Mistral Saba - Balanced ⚖️", "mistral-saba-24b"),
                    ("DeepSeek - Reasoning 🧠🔍", "deepseek-r1-distill-llama-70b"),
                    ("Compound Beta - Agentic & Live Search 🛠️🔎", "compound-beta")
                ],
                value="llama-3.3-70b-versatile",
                label="Select AI Large Language Model 🤖",
                info="Please try out the other AI models to use for responses (all LLMs are running on [GroqCloud](https://groq.com/groqrack/)) - Compound Beta includes live internet searching! 🔎"
            )
            
            chatbot = gr.Chatbot(height=600, type="messages")
            with gr.Row(equal_height=True):
                msg = gr.Textbox(
                    placeholder="What would you like to know about BAS? Or choose an example question...❓",
                    label="Your question 🤔",
                    show_label=True,
                    container=True,
                    submit_btn=True,
                    scale=20,
                )
            clear = gr.Button("Clear Chat History 🧹 (Click here if any errors are returned and ask question again)")
            
            gr.Examples(
                examples=[
                    "What research stations does BAS operate in Antarctica? 🏔️",
                    "Tell me about the RRS Sir David Attenborough 🚢",
                    "What are the latest climate research findings from BAS? 📊",
                    "What current projects is BAS working on in Antarctica? 🔬",
                    "What's the latest news about BAS's Antarctic operations? 📰",
                    "What's happening at Rothera Research Station right now? 🌡️"
                ],
                inputs=msg,
            )
            
            def user(user_message, history):
                history = history or []
                return "", history + [{"role": "user", "content": user_message}]
            
            def bot(history, model_name, request: gr.Request):
                history = history or []
                if history and history[-1]["role"] == "user":
                    user_message = history[-1]["content"]
                    history_without_last = history[:-1]
                    for response in chat_response(user_message, history_without_last, model_name, request):
                        history_with_response = history + [{"role": "assistant", "content": response}]
                        yield history_with_response
            
            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
                bot, [chatbot, model_selector], chatbot
            )
            
            clear.click(lambda: [], None, chatbot, queue=False)  # Updated to return empty list
            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,
            share=False,
            show_api=False
        )
        
    except Exception as e:
        print(f"\nERROR: {str(e)}")
        raise