|
import gradio as gr |
|
from transformers import pipeline |
|
from haystack.document_stores import FAISSDocumentStore |
|
from haystack.nodes import EmbeddingRetriever |
|
import numpy as np |
|
import openai |
|
import os |
|
|
|
|
|
document_store = FAISSDocumentStore.load( |
|
index_path=f"./documents/climate_gpt.faiss", |
|
config_path=f"./documents/climate_gpt.json", |
|
) |
|
|
|
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") |
|
system_template = {"role": os.environ["role"], "content": os.environ["content"]} |
|
|
|
dense = EmbeddingRetriever( |
|
document_store=document_store, |
|
embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", |
|
model_format="sentence_transformers", |
|
) |
|
|
|
|
|
def is_climate_change_related(sentence: str) -> bool: |
|
results = classifier( |
|
sequences=sentence, |
|
candidate_labels=["climate change related", "non climate change related"], |
|
) |
|
return results["labels"][np.argmax(results["scores"])] == "climate change related" |
|
|
|
|
|
def make_pairs(lst): |
|
"""from a list of even lenght, make tupple pairs""" |
|
return [(lst[i], lst[i + 1]) for i in range(0, len(lst), 2)] |
|
|
|
|
|
def gen_conv(query: str, history=[system_template], ipcc=True): |
|
"""return (answer:str, history:list[dict], sources:str)""" |
|
retrieve = ipcc and is_climate_change_related(query) |
|
sources = "" |
|
messages = history + [ |
|
{"role": "user", "content": query}, |
|
] |
|
|
|
if retrieve: |
|
docs = dense.retrieve(query=query, top_k=5) |
|
sources = "\n\n".join( |
|
[ |
|
"If relevant, use those extracts in your answer and give the reference of the information you used." |
|
] |
|
+ [ |
|
f"{d.meta['file_name']} Page {d.meta['page_number']}\n{d.content}" |
|
for d in docs |
|
] |
|
) |
|
messages.append({"role": "system", "content": sources}) |
|
|
|
answer = openai.ChatCompletion.create( |
|
model="gpt-3.5-turbo", |
|
messages=messages, |
|
temperature=0.2, |
|
|
|
)["choices"][0]["message"]["content"] |
|
|
|
if retrieve: |
|
messages.pop() |
|
|
|
sources = "\n\n".join( |
|
f"{d.meta['file_name']} Page {d.meta['page_number']}:\n{d.content}" |
|
for d in docs |
|
) |
|
messages.append({"role": "assistant", "content": answer}) |
|
gradio_format = make_pairs([a["content"] for a in messages[1:]]) |
|
|
|
return gradio_format, messages, sources |
|
|
|
|
|
def set_openai_api_key(text): |
|
"""Set the api key and return chain. |
|
If no api_key, then None is returned. |
|
""" |
|
openai.api_key = os.environ["api_key"] |
|
|
|
if text.startswith("sk-") and len(text) > 10: |
|
openai.api_key = text |
|
return f"You're all set: this is your api key: {openai.api_key}" |
|
|
|
|
|
|
|
with gr.Blocks(title="Eki IPCC Explorer") as demo: |
|
openai.api_key = os.environ["api_key"] |
|
gr.Markdown("# Climate GPT") |
|
with gr.Row(): |
|
gr.Markdown("First step: Add your OPENAI api key") |
|
openai_api_key_textbox = gr.Textbox( |
|
placeholder="Paste your OpenAI API key (sk-...) and hit Enter", |
|
show_label=False, |
|
lines=1, |
|
type="password", |
|
) |
|
|
|
gr.Markdown("""# Ask me anything, I'm a climate expert""") |
|
with gr.Row(): |
|
with gr.Column(scale=2): |
|
chatbot = gr.Chatbot() |
|
state = gr.State([system_template]) |
|
|
|
with gr.Row(): |
|
ask = gr.Textbox( |
|
show_label=False, placeholder="Enter text and press enter" |
|
).style(container=False) |
|
|
|
with gr.Column(scale=1, variant="panel"): |
|
|
|
gr.Markdown("### Sources") |
|
sources_textbox = gr.Textbox( |
|
interactive=False, show_label=False, max_lines=50 |
|
) |
|
|
|
ask.submit( |
|
fn=gen_conv, inputs=[ask, state], outputs=[chatbot, state, sources_textbox] |
|
) |
|
|
|
openai_api_key_textbox.change(set_openai_api_key, inputs=[openai_api_key_textbox]) |
|
openai_api_key_textbox.submit(set_openai_api_key, inputs=[openai_api_key_textbox]) |
|
|
|
demo.launch() |
|
|
