File size: 2,499 Bytes
c623717 302c422 fd925d9 c623717 fd925d9 302c422 fd925d9 302c422 c623717 b6ca784 c623717 b6ca784 c623717 b6ca784 c623717 b6ca784 c623717 b6ca784 c623717 fd925d9 302c422 c623717 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import os
import json
import gradio as gr
from openai import OpenAI
from pinecone import Pinecone
from dotenv import load_dotenv
load_dotenv(verbose=True)
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
INDEX_NAME = "whatsapp-history-1"
EMBED_MODEL = "text-embedding-3-small"
TOP_K = 5
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
pc = Pinecone(api_key=PINECONE_API_KEY)
index = pc.Index(INDEX_NAME)
def retrieve_context(query):
response = client.embeddings.create(
model=EMBED_MODEL,
input=query
)
query_emb = response.data[0].embedding
# Use keyword argument 'vector' for the query
result = index.query(vector=query_emb, top_k=TOP_K, include_metadata=True)
contexts = []
for match in result.matches:
meta = match.metadata
contexts.append(f"{meta['sender']}: {meta['message']}")
return "\n".join(contexts)
def respond(message, chat_history_json):
chat_history = json.loads(chat_history_json)
context = retrieve_context(message)
system_prompt = (
"You are a helpful assistant for carching. Use the following past conversation data on whatsapp "
"to answer the user's question if relevant:\n\n" + context
)
messages = [{"role": "system", "content": system_prompt}]
messages.extend(chat_history or [])
messages.append({"role": "user", "content": message})
response = client.chat.completions.create(
model="gpt-5-mini",
messages=messages,
)
bot_reply = response.choices[0].message.content
chat_history.append({"role": "user", "content": message})
chat_history.append({"role": "assistant", "content": bot_reply})
# Return the history directly and the serialized state
return chat_history, json.dumps(chat_history)
with gr.Blocks() as demo:
gr.Markdown("# Customer Support Chatbot")
# Set chatbot type to 'messages' to resolve the warning
chatbot = gr.Chatbot(type="messages")
msg = gr.Textbox(placeholder="Ask a question...", show_label=False)
state = gr.State(json.dumps([]))
with gr.Row():
submit_btn = gr.Button("Send")
def submit_message(msg, state):
# Clear the textbox after submission
return "", *respond(msg, state)
# Bind submit button AND hitting enter in textbox
submit_btn.click(fn=submit_message, inputs=[msg, state], outputs=[msg, chatbot, state])
msg.submit(fn=submit_message, inputs=[msg, state], outputs=[msg, chatbot, state])
demo.launch() |