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from flask import Flask, request |
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import os |
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from langchain.vectorstores import Chroma |
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from langchain.document_loaders import PyPDFLoader |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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import requests |
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app = Flask(__name__) |
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CHROMA_PATH = '/code/chroma_db' |
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if not os.path.exists(CHROMA_PATH): |
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os.makedirs(CHROMA_PATH) |
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AI71_API_KEY = os.environ.get('AI71_API_KEY') |
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account_sid = os.environ.get('TWILIO_ACCOUNT_SID') |
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auth_token = os.environ.get('TWILIO_AUTH_TOKEN') |
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client = Client(account_sid, auth_token) |
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from_whatsapp_number = 'whatsapp:+14155238886' |
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def download_file(url, ext): |
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local_filename = f'/tmp/uploaded_file{ext}' |
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with requests.get(url, stream=True) as r: |
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with open(local_filename, 'wb') as f: |
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for chunk in r.iter_content(chunk_size=8192): |
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f.write(chunk) |
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return local_filename |
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def save_pdf_and_update_database(pdf_filepath): |
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try: |
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document_loader = PyPDFLoader(pdf_filepath) |
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documents = document_loader.load() |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size=800, |
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chunk_overlap=80, |
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length_function=len, |
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is_separator_regex=False, |
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) |
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chunks = text_splitter.split_documents(documents) |
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embedding_function = HuggingFaceEmbeddings() |
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db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function) |
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db.add_documents(chunks) |
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db.persist() |
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print("PDF processed and data updated in Chroma.") |
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except Exception as e: |
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print(f"Error processing PDF: {e}") |
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def generate_response(query, chat_history): |
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response = '' |
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for chunk in AI71(AI71_API_KEY).chat.completions.create( |
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model="tiiuae/falcon-180b-chat", |
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messages=[ |
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{"role": "system", "content": "You are the best agricultural assistant. Remember to give a response in not more than 2 sentences."}, |
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{"role": "user", "content": f'''Answer the query based on history {chat_history}: {query}'''}, |
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], |
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stream=True, |
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): |
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if chunk.choices[0].delta.content: |
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response += chunk.choices[0].delta.content |
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return response.replace("###", '').replace('\nUser:', '') |
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def query_rag(query_text: str, chat_history): |
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db = Chroma(persist_directory=CHROMA_PATH, embedding_function=HuggingFaceEmbeddings()) |
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results = db.similarity_search_with_score(query_text, k=5) |
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if not results: |
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return "Sorry, I couldn't find any relevant information." |
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context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results]) |
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prompt = f"Context:\n{context_text}\n\nQuestion:\n{query_text}" |
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response = generate_response(prompt, chat_history) |
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return response |
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@app.route('/whatsapp', methods=['POST']) |
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def whatsapp_webhook(): |
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incoming_msg = request.values.get('Body', '').lower() |
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sender = request.values.get('From') |
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num_media = int(request.values.get('NumMedia', 0)) |
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chat_history = [] |
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if num_media > 0: |
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media_url = request.values.get('MediaUrl0') |
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content_type = request.values.get('MediaContentType0') |
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if content_type == 'application/pdf': |
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filepath = download_file(media_url, ".pdf") |
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save_pdf_and_update_database(filepath) |
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response_text = "PDF has been processed. You can now ask questions related to its content." |
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else: |
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response_text = "Unsupported file type. Please upload a PDF document." |
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else: |
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response_text = query_rag(incoming_msg, chat_history) |
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send_message(sender, response_text) |
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return '', 204 |
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def send_message(to, body): |
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try: |
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message = client.messages.create( |
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from_=from_whatsapp_number, |
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body=body, |
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to=to |
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) |
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print(f"Message sent with SID: {message.sid}") |
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except Exception as e: |
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print(f"Error sending message: {e}") |
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def send_initial_message(to_number): |
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send_message( |
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f'whatsapp:{to_number}', |
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'Welcome to the Agri AI Chatbot! How can I assist you today? You can send an image with "pest" or "disease" to classify it.' |
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) |
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if __name__ == "__main__": |
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send_initial_message('919080522395') |
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send_initial_message('916382792828') |
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app.run(host='0.0.0.0', port=7860) |