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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 openai |
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import os |
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from utils import ( |
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make_pairs, |
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set_openai_api_key, |
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get_random_string, |
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) |
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system_template = {"role": "system", "content": os.environ["content"]} |
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retrieve_all = EmbeddingRetriever( |
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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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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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retrieve_giec = EmbeddingRetriever( |
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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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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 chat(query: str, history: list = [system_template], report_type="All available", threshold=0.56): |
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retriever = retrieve_all if report_type == "All available" else retrieve_giec |
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docs = retriever.retrieve(query=query, top_k=10) |
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messages = history + [{"role": "user", "content": query}] |
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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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response = 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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complete_response = "" |
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for chunk in response: |
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complete_response += chunk["choices"][0]["delta"].get("content", "") |
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messages[-1] = {"role": "assistant", "content": complete_response} |
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gradio_format = make_pairs([a["content"] for a in messages[1:]]) |
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yield gradio_format, messages, sources |
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def test(feed: str): |
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print(feed) |
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def reset_textbox(): |
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return gr.update(value="") |
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css_code = ".gradio-container {background-image: url('file=background.png');background-position: top right}" |
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with gr.Blocks(title="🌍 ClimateGPT Ekimetrics", css=css_code) as demo: |
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openai.api_key = os.environ["api_key"] |
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user_id = gr.State([get_random_string(10)]) |
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with gr.Tab("App"): |
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gr.Markdown("# Welcome to Climate GPT 🌍 !") |
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gr.Markdown( |
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""" Climate GPT is an interactive exploration tool designed to help you easily find relevant information based on of Environmental reports such as IPCCs and other environmental reports. |
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\n **How does it work:** This Chatbot is a combination of two technologies. FAISS search applied to a vast amount of scientific climate reports and TurboGPT to generate human-like text from the part of the document extracted from the database. |
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\n ⚠️ Warning: Always refer to the source to ensure the validity of the information communicated. |
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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, |
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placeholder="Enter text and press enter", |
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sample_inputs=["which country polutes the most ?"], |
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).style(container=False) |
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print(f"Type from ask textbox {ask.type}") |
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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=chat, |
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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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ask.submit(reset_textbox, [], [ask]) |
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with gr.Accordion("Feedbacks", open=False): |
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gr.Markdown("Please complete some feedbacks 🙏") |
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feedback = gr.Textbox() |
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feedback_save = gr.Button(value="submit feedback") |
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feedback_save.click(test, inputs=[feedback]) |
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with gr.Accordion("Add your personal openai api key - Option", open=False): |
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openai_api_key_textbox = gr.Textbox( |
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placeholder="Paste your OpenAI API key (sk-...) and hit Enter", |
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show_label=False, |
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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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""" |
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## 📖 Reports used : \n |
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- First Assessment Report on the Physical Science of Climate Change |
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- Second assessment Report on Climate Change Adaptation |
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- Third Assessment Report on Climate Change Mitigation |
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- Food Outlook Biannual Report on Global Food Markets |
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- IEA's report on the Role of Critical Minerals in Clean Energy Transitions |
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- Limits to Growth |
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- Outside The Safe operating system of the Planetary Boundary for Novel Entities |
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- Planetary Boundaries Guiding |
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- State of the Oceans report |
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- Word Energy Outlook 2021 |
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- Word Energy Outlook 2022 |
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- The environmental impacts of plastics and micro plastics use, waste and polution ET=U and national measures |
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- IPBES Global report - MArch 2022 |
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\n |
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IPCC is a United Nations body that assesses the science related to climate change, including its impacts and possible response options. |
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The IPCC is considered the leading scientific authority on all things related to global climate change. |
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""" |
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) |
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with gr.Tab("Examples"): |
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gr.Markdown("See here some examples on how to use the Chatbot") |
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demo.queue(concurrency_count=16) |
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