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import gradio as gr
from gradio_client import Client
from qdrant_client import QdrantClient
from load_encoder import encoder
from utils import NeuralSearcher
import spaces
import time
import os
mytheme = gr.Theme.from_hub("JohnSmith9982/small_and_pretty")
collection_name = os.getenv("QDRANT_COLLECTION")
QDRANT_API_KEY = os.getenv("QDRANT_API")
QDRANT_URL = os.getenv("QDRANT_URL")
qdrant_client = QdrantClient(
url=QDRANT_URL,
api_key=QDRANT_API_KEY,
)
api_client = Client("eswardivi/Phi-3-mini-128k-instruct")
@spaces.GPU(duration=120)
def reply(message, history):
global encoder
global api_client
global qdrant_client
txt2txt = NeuralSearcher(collection_name, qdrant_client, encoder)
context = txt2txt.search(message)
to_phi = f"Instructions: you are a useful assistant focused on providing valuable content on Climate-related Financial Disclosures; Context: {context}; User prompt: {message}"
response = api_client.predict(
to_phi, # str in 'Message' Textbox component
0.2, # float (numeric value between 0 and 1) in 'Temperature' Slider component
True, # bool in 'Sampling' Checkbox component
512, # float (numeric value between 128 and 4096) in 'Max new tokens' Slider component
api_name="/chat"
)
this_hist = ''
for char in response:
this_hist += char
time.sleep(0.0001)
yield this_hist
demo = gr.ChatInterface(fn=reply, title="cLLiMateChat", theme=mytheme)
demo.launch(server_name="0.0.0.0", share=False) |