app.py

from flask import Flask, request
import os
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
import requests
from twilio.rest import Client


# Flask app
app = Flask(__name__)

# ChromaDB path
CHROMA_PATH = '/code/chroma_db'
if not os.path.exists(CHROMA_PATH):
    os.makedirs(CHROMA_PATH)
def initialize_chroma():
    try:
        # Initialize Chroma
        embedding_function = HuggingFaceEmbeddings()  # Use your desired embedding function
        db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
        
        # Perform an initial operation to ensure the database is correctly initialized
        db.similarity_search_with_score("test query", k=1)
        print("Chroma initialized successfully.")
    except Exception as e:
        print(f"Error initializing Chroma: {e}")

initialize_chroma()


# Set AI71 API key
AI71_API_KEY = os.environ.get('AI71_API_KEY')
account_sid = os.environ.get('TWILIO_ACCOUNT_SID')
auth_token = os.environ.get('TWILIO_AUTH_TOKEN')
client = Client(account_sid, auth_token)
from_whatsapp_number = 'whatsapp:+14155238886'

# Download file utility
def download_file(url, ext):
    local_filename = f'/code/uploaded_file{ext}'
    with requests.get(url, stream=True) as r:
        with open(local_filename, 'wb') as f:
            for chunk in r.iter_content(chunk_size=8192):
                f.write(chunk)
    return local_filename

# Process PDF and save to ChromaDB
def save_pdf_and_update_database(pdf_filepath):
    try:
        document_loader = PyPDFLoader(pdf_filepath)
        documents = document_loader.load()
        
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=800,
            chunk_overlap=80,
            length_function=len,
            is_separator_regex=False,
        )
        chunks = text_splitter.split_documents(documents)
        
        embedding_function = HuggingFaceEmbeddings()
        db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
        
        db.add_documents(chunks)
        db.persist()
        print("PDF processed and data updated in Chroma.")
    except Exception as e:
        print(f"Error processing PDF: {e}")

# Generate response using Falcon model
def generate_response(query, chat_history):
    response = ''
    for chunk in AI71(AI71_API_KEY).chat.completions.create(
            model="tiiuae/falcon-180b-chat",
            messages=[
                {"role": "system", "content": "You are the best agricultural assistant. Remember to give a response in not more than 2 sentences."},
                {"role": "user", "content": f'''Answer the query based on history {chat_history}: {query}'''},
            ],
            stream=True,
    ):
        if chunk.choices[0].delta.content:
            response += chunk.choices[0].delta.content
    return response.replace("###", '').replace('\nUser:', '')

# Query the RAG system
def query_rag(query_text: str, chat_history):
    try:
        # Ensure the database is initialized
        initialize_chroma()

        embedding_function = HuggingFaceEmbeddings()
        db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
        
        results = db.similarity_search_with_score(query_text, k=5)
        
        if not results:
            return "Sorry, I couldn't find any relevant information."
        
        context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
        
        prompt = f"Context:\n{context_text}\n\nQuestion:\n{query_text}"
        response = generate_response(prompt, chat_history)
        
        return response
    except Exception as e:
        print(f"Error querying RAG system: {e}")
        return "An error occurred while querying the RAG system."


# Flask route to handle WhatsApp webhook
@app.route('/whatsapp', methods=['POST'])
def whatsapp_webhook():
    incoming_msg = request.values.get('Body', '').lower()
    sender = request.values.get('From')
    num_media = int(request.values.get('NumMedia', 0))

    chat_history = []  # You need to handle chat history appropriately

    if num_media > 0:
        media_url = request.values.get('MediaUrl0')
        content_type = request.values.get('MediaContentType0')

        if content_type == 'application/pdf':
            filepath = download_file(media_url, ".pdf")
            save_pdf_and_update_database(filepath)
            response_text = "PDF has been processed. You can now ask questions related to its content."
        else:
            response_text = "Unsupported file type. Please upload a PDF document."
    else:
        response_text = query_rag(incoming_msg, chat_history)

    # Assuming you have a function to send a message back to the user
    send_message(sender, response_text)
    return '', 204
    
def send_message(to, body):
    try:
        message = client.messages.create(
            from_=from_whatsapp_number,
            body=body,
            to=to
        )
        print(f"Message sent with SID: {message.sid}")
    except Exception as e:
        print(f"Error sending message: {e}")
        
def send_initial_message(to_number):
    send_message(
        f'whatsapp:{to_number}',
        'Welcome to the Agri AI Chatbot! How can I assist you today? You can send an image with "pest" or "disease" to classify it.'
    )
if __name__ == "__main__":
    send_initial_message('919080522395')
    send_initial_message('916382792828')
    app.run(host='0.0.0.0', port=7860)