app.py

import os
import gradio as gr
import requests
import json
import logging
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# API Keys configuration
COHERE_API_KEY = os.getenv("COHERE_API_KEY")
MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")

if not COHERE_API_KEY or not MISTRAL_API_KEY:
    raise ValueError("Missing required API keys in environment variables")

# API endpoints configuration
COHERE_API_URL = "https://api.cohere.ai/v1/chat"
MISTRAL_API_URL = "https://api.mistral.ai/v1/chat/completions"
VECTOR_API_URL = "https://sendthat.cc"
HISTORY_INDEX = "history"

# Model configurations
MODELS = {
    "Cohere": {
        "name": "command-r-08-2024",
        "api_url": COHERE_API_URL,
        "api_key": COHERE_API_KEY
    },
    "Mistral": {
        "name": "ft:open-mistral-nemo:ef730d29:20241022:2a0e7d46",
        "api_url": MISTRAL_API_URL,
        "api_key": MISTRAL_API_KEY
    }
}

def search_document(query, k):
    try:
        url = f"{VECTOR_API_URL}/search/{HISTORY_INDEX}"
        payload = {"text": query, "k": k}
        headers = {"Content-Type": "application/json"}
        response = requests.post(url, json=payload, headers=headers)
        response.raise_for_status()
        return response.json(), "", k
    except requests.exceptions.RequestException as e:
        logging.error(f"Error in search: {e}")
        return {"error": str(e)}, query, k

def generate_answer_cohere(question, context, citations):
    headers = {
        "Authorization": f"Bearer {MODELS['Cohere']['api_key']}",
        "Content-Type": "application/json"
    }
    
    prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer the question based on the given context. Include citations as [1], [2], etc.:"
    
    payload = {
        "message": prompt,
        "model": MODELS['Cohere']['name'],
        "preamble": "You are an AI-assistant chatbot. Provide thorough responses with citations.",
        "chat_history": []
    }
    
    try:
        response = requests.post(MODELS['Cohere']['api_url'], headers=headers, json=payload)
        response.raise_for_status()
        answer = response.json()['text']
        
        answer += "\n\nSources:"
        for i, citation in enumerate(citations, 1):
            answer += f"\n[{i}] {citation}"
        
        return answer
    except requests.exceptions.RequestException as e:
        logging.error(f"Error in generate_answer_cohere: {e}")
        return f"An error occurred: {str(e)}"

def generate_answer_mistral(question, context, citations):
    headers = {
        "Authorization": f"Bearer {MODELS['Mistral']['api_key']}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }
    
    prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer the question based on the given context. Include citations as [1], [2], etc.:"
    
    payload = {
        "model": MODELS['Mistral']['name'],
        "messages": [
            {
                "role": "user",
                "content": prompt
            }
        ]
    }
    
    try:
        response = requests.post(MODELS['Mistral']['api_url'], headers=headers, json=payload)
        response.raise_for_status()
        answer = response.json()['choices'][0]['message']['content']
        
        answer += "\n\nSources:"
        for i, citation in enumerate(citations, 1):
            answer += f"\n[{i}] {citation}"
        
        return answer
    except requests.exceptions.RequestException as e:
        logging.error(f"Error in generate_answer_mistral: {e}")
        return f"An error occurred: {str(e)}"

def answer_question(question, model_choice, k=3):
    # Search the vector database
    search_results, _, _ = search_document(question, k)
    
    # Extract and combine the retrieved contexts
    if "results" in search_results:
        contexts = []
        citations = []
        for item in search_results['results']:
            contexts.append(item['metadata']['content'])
            citations.append(f"{item['metadata'].get('title', 'Unknown Source')} - {item['metadata'].get('source', 'No source provided')}")
        combined_context = " ".join(contexts)
    else:
        logging.error(f"Error in database search or no results found: {search_results}")
        combined_context = ""
        citations = []
    
    # Generate answer using the selected model
    if model_choice == "Cohere":
        return generate_answer_cohere(question, combined_context, citations)
    else:
        return generate_answer_mistral(question, combined_context, citations)

def chatbot(message, history, model_choice):
    response = answer_question(message, model_choice)
    return response

# Create Gradio interface
with gr.Blocks(theme="soft") as iface:
    gr.Markdown("# History Chatbot")
    gr.Markdown("Ask me anything about history, and I'll provide answers with citations!")
    
    with gr.Row():
        model_choice = gr.Radio(
            choices=["Cohere", "Mistral"],
            value="Cohere",
            label="Choose LLM Model",
            info="Select which AI model to use for generating responses"
        )
    
    chatbot_interface = gr.ChatInterface(
        fn=lambda message, history, model: chatbot(message, history, model),
        additional_inputs=[model_choice],
        chatbot=gr.Chatbot(height=300),
        textbox=gr.Textbox(placeholder="Ask a question about history...", container=False, scale=7),
        examples=[
            "Why was Anne Hutchinson banished from Massachusetts?",
            "What were the major causes of World War I?",
            "Who was the first President of the United States?",
            "What was the significance of the Industrial Revolution?"
        ],
        cache_examples=False,
        retry_btn=None,
        undo_btn="Delete Previous",
        clear_btn="Clear",
    )

# Launch the app
if __name__ == "__main__":
    iface.launch()