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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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classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") |
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system_template = { |
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"role": "system", |
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"content": "You have been a climate change expert for 30 years. You answer questions about climate change in an educationnal and concise manner.", |
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} |
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document_store = FAISSDocumentStore.load( |
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index_path=f"./climate_gpt.faiss", |
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config_path=f"./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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def is_climate_change_related(sentence: str) -> bool: |
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results = classifier( |
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sequences=sentence, |
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candidate_labels=["climate change related", "non climate change related"], |
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) |
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return results["labels"][np.argmax(results["scores"])] == "climate change related" |
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def make_pairs(lst): |
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"""from a list of even lenght, make tupple pairs""" |
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return [(lst[i], lst[i + 1]) for i in range(0, len(lst), 2)] |
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def gen_conv(query: str, history=[system_template], ipcc=True): |
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"""return (answer:str, history:list[dict], sources:str)""" |
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retrieve = ipcc and is_climate_change_related(query) |
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sources = "" |
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messages = history + [ |
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{"role": "user", "content": query}, |
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] |
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if retrieve: |
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docs = dense.retrieve(query=query, top_k=5) |
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sources = "\n\n".join( |
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["If relevant, use those extracts from IPCC reports in your answer"] |
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+ [ |
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f"{d.meta['path']} Page {d.meta['page_id']} paragraph {d.meta['paragraph_id']}:\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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answer = "(top 5 documents retrieved) " + answer |
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sources = "\n\n".join( |
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f"{d.meta['path']} Page {d.meta['page_id']} paragraph {d.meta['paragraph_id']}:\n{d.content[:100]} [...]" |
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for d in docs |
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) |
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messages.append({"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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def connect(text): |
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openai.api_key = text |
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return "You're all set" |
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with gr.Blocks(title="Eki IPCC Explorer") as demo: |
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with gr.Row(): |
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with gr.Column(): |
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api_key = gr.Textbox(label="Open AI api key") |
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connect_btn = gr.Button(value="Connect") |
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with gr.Column(): |
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result = gr.Textbox(label="Connection") |
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connect_btn.click(connect, inputs=api_key, outputs=result, api_name="Connection") |
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gr.Markdown( |
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""" |
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# Ask me anything, I'm an IPCC report |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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chatbot = gr.Chatbot() |
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state = gr.State([system_template]) |
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with gr.Row(): |
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ask = gr.Textbox( |
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show_label=False, placeholder="Enter text and press enter" |
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).style(container=False) |
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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, inputs=[ask, state], outputs=[chatbot, state, sources_textbox] |
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) |
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demo.launch(share=True) |
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