cleaner app
Browse files
app.py
CHANGED
@@ -1,7 +1,6 @@
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import gradio as gr
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from transformers import pipeline
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from haystack.document_stores import FAISSDocumentStore
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from haystack.nodes import EmbeddingRetriever
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import numpy as np
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import openai
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import os
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@@ -15,12 +14,21 @@ from utils import (
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get_random_string,
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)
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classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
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system_template = {"role": os.environ["role"], "content": os.environ["content"]}
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"""return (answer:str, history:list[dict], sources:str)
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Args:
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@@ -31,57 +39,32 @@ def gen_conv(query: str, report_type, history=[system_template], ipcc=True):
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Returns:
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_type_: _description_
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"""
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if report_type == "IPCC only":
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document_store = FAISSDocumentStore.load(
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index_path="./documents/climate_gpt_only_giec.faiss",
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config_path="./documents/climate_gpt_only_giec.json",
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)
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else:
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document_store = FAISSDocumentStore.load(
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index_path="./documents/climate_gpt.faiss",
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config_path="./documents/climate_gpt.json",
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)
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dense = EmbeddingRetriever(
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document_store=document_store,
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embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
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model_format="sentence_transformers",
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)
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sources = ""
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[os.environ["sources"]]
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+ [
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f"{d.meta['file_name']} Page {d.meta['page_number']}\n{d.content}"
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for d in docs
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]
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)
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messages.append({"role": "system", "content": sources})
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answer = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=messages,
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temperature=0.2,
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)["choices"][0]["message"]["content"]
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if retrieve:
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messages.pop()
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sources = "\n\n".join(
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f"{d.meta['file_name']} Page {d.meta['page_number']}:\n{d.content}"
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for d in docs
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)
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else:
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sources = "No environmental report was used to provide this answer."
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gradio_format = make_pairs([a["content"] for a in messages[1:]])
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return gradio_format, messages, sources
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@@ -123,20 +106,18 @@ with gr.Blocks(title="🌍 ClimateGPT Ekimetrics", css=css_code) as demo:
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with gr.Column(scale=1, variant="panel"):
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gr.Markdown("### Sources")
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sources_textbox = gr.Textbox(
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interactive=False, show_label=False, max_lines=50
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)
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ask.submit(
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fn=gen_conv,
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inputs=[
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ask,
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gr.inputs.Dropdown(
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["IPCC only", "All available"],
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default="All available",
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label="Select reports",
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),
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state,
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],
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outputs=[chatbot, state, sources_textbox],
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)
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@@ -153,12 +134,8 @@ with gr.Blocks(title="🌍 ClimateGPT Ekimetrics", css=css_code) as demo:
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lines=1,
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type="password",
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)
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openai_api_key_textbox.change(
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)
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openai_api_key_textbox.submit(
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set_openai_api_key, inputs=[openai_api_key_textbox]
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)
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with gr.Tab("Information"):
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gr.Markdown(
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import gradio as gr
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from haystack.document_stores import FAISSDocumentStore
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from haystack.nodes import EmbeddingRetriever
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import numpy as np
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import openai
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import os
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get_random_string,
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)
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system_template = {"role": os.environ["role"], "content": os.environ["content"]}
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only_ipcc_document_store = FAISSDocumentStore.load(
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index_path="./documents/climate_gpt_only_giec.faiss",
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config_path="./documents/climate_gpt_only_giec.json",
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)
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document_store = FAISSDocumentStore.load(
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index_path="./documents/climate_gpt.faiss",
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config_path="./documents/climate_gpt.json",
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)
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def gen_conv(query: str, history=[system_template], report_type="All available", threshold=0.56):
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"""return (answer:str, history:list[dict], sources:str)
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Args:
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Returns:
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_type_: _description_
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"""
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dense = EmbeddingRetriever(
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document_store=document_store if report_type == "All available" else only_ipcc_document_store,
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embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
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model_format="sentence_transformers",
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)
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messages = history + [{"role": "user", "content": query}]
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docs = dense.retrieve(query=query, top_k=10)
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sources = "\n\n".join(
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f"doc {i}: {d.meta['file_name']} page {d.meta['page_number']}\n{d.content}"
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for i, d in enumerate(docs, 1)
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if d.score > threshold
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)
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if sources:
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messages.append({"role": "system", "content": f"{os.environ['sources']}\n\n{sources}"})
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else:
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messages.append({"role": "system", "content": "no relevant document available."})
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sources = "No environmental report was used to provide this answer."
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answer = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages, temperature=0.2,)["choices"][0][
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"message"
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]["content"]
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messages[-1] = {"role": "assistant", "content": answer}
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gradio_format = make_pairs([a["content"] for a in messages[1:]])
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return gradio_format, messages, sources
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with gr.Column(scale=1, variant="panel"):
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gr.Markdown("### Sources")
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sources_textbox = gr.Textbox(interactive=False, show_label=False, max_lines=50)
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ask.submit(
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fn=gen_conv,
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inputs=[
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ask,
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state,
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gr.inputs.Dropdown(
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["IPCC only", "All available"],
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default="All available",
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label="Select reports",
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),
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],
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outputs=[chatbot, state, sources_textbox],
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)
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lines=1,
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type="password",
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)
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openai_api_key_textbox.change(set_openai_api_key, inputs=[openai_api_key_textbox])
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openai_api_key_textbox.submit(set_openai_api_key, inputs=[openai_api_key_textbox])
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with gr.Tab("Information"):
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gr.Markdown(
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