File size: 1,878 Bytes
9c22e84 8c6f23b | 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 | import faiss
from sentence_transformers import SentenceTransformer
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
import gradio as gr
import numpy as np
import pickle
model = SentenceTransformer("all-MiniLM-L6-v2")
index = faiss.read_index("solar_vectors.index")
with open("chunks.pkl", "rb") as f:
chunks= pickle.load(f)
llm = ChatGroq(model="mixtral-8x7b-32768",temperature=0.2)
def retrieve_relevant_text(query, top_k=1):
query_embedding = model.encode([query])
distances, indices = index.search(np.array(query_embedding), top_k)
return [chunks[i] for i in indices[0]]
def generate_response(user_query):
retrieved_text = retrieve_relevant_text(user_query, top_k=4)
system_message = "You are an intelligent assistant that provides accurate, helpful information about solar energy based on the information provided(if not, answer according to your knowledge)."
prompt_template = ChatPromptTemplate.from_messages([
("system", system_message),
("human", f"Use the following information to answer: {retrieved_text} \n\nUser Query: {user_query}")
])
chain = prompt_template | llm
response = chain.invoke({"text": user_query})
return response.content
def gradio_chatbot(user_input):
response = generate_response(user_input)
return response
with gr.Blocks() as demo:
gr.Markdown("# 🌞 SolarAI 🌞")
with gr.Row():
user_input = gr.Textbox(
placeholder="Ask me anything about solar energy...",
lines=2,
interactive=True
)
with gr.Row():
output_box = gr.Textbox(
lines=12,
interactive=True,
label="Chatbot Response"
)
submit_btn = gr.Button("Ask")
submit_btn.click(fn=gradio_chatbot, inputs=user_input, outputs=output_box)
demo.launch() |