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import gradio as gr
from langchain import PromptTemplate, LlamaCpp, LLMChain, FAISS
from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.callbacks.manager import CallbackManager
from langchain.chains.question_answering import load_qa_chain
import dill
def greet(name):
db = dill.load(open("client_embebedings.mate", "rb"))
return name+db
#docs = db.similarity_search(name)
#return chain.run(input_documents=docs, question=name)
def greet1(name):
return "Hello ya client" +name
iface = gr.Blocks()
with iface:
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
chain = load_qa_chain( LlamaCpp(
model_path="llama-13B-Q4_K_M.gguf",
temperature=0,
top_p= 1,
callback_manager=callback_manager,
verbose=True,
), chain_type="stuff")
name = gr.Textbox(label="Name")
output = gr.Textbox(label="Output Box")
greet_btn = gr.Button("Greet")
greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
greet1_btn = gr.Button("Greet1")
greet1_btn.click(fn=greet1, inputs=name, outputs=output, api_name="testing")
iface.launch()
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