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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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create_user_id, |
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to_completion, |
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) |
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import numpy as np |
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from datetime import datetime |
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from azure.storage.fileshare import ShareServiceClient |
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system_template = {"role": "system", "content": os.environ["content"]} |
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openai.api_type = "azure" |
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openai.api_key = os.environ["api_key"] |
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openai.api_base = os.environ["ressource_endpoint"] |
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openai.api_version = "2022-12-01" |
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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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credential = { |
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"account_key": os.environ["account_key"], |
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"account_name": os.environ["account_name"], |
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} |
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account_url = os.environ["account_url"] |
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file_share_name = "climategpt" |
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service = ShareServiceClient(account_url=account_url, credential=credential) |
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share_client = service.get_share_client(file_share_name) |
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user_id = create_user_id(10) |
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def chat( |
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user_id: str, |
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query: str, |
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history: list = [system_template], |
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report_type: str = "All available", |
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threshold: float = 0.555, |
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) -> tuple: |
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"""retrieve relevant documents in the document store then query gpt-turbo |
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Args: |
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query (str): user message. |
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history (list, optional): history of the conversation. Defaults to [system_template]. |
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report_type (str, optional): should be "All available" or "IPCC only". Defaults to "All available". |
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threshold (float, optional): similarity threshold, don't increase more than 0.568. Defaults to 0.56. |
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Yields: |
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tuple: chat gradio format, chat openai format, sources used. |
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""" |
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if report_type == "All available": |
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retriever = retrieve_all |
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elif report_type == "IPCC only": |
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retriever = retrieve_giec |
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else: |
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raise Exception("report_type arg should be in (All available, IPCC only)") |
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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( |
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{"role": "system", "content": f"{os.environ['sources']}\n\n{sources}"} |
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) |
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response = openai.Completion.create( |
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engine="climateGPT", |
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prompt=to_completion(messages), |
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temperature=0.2, |
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stream=True, |
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max_tokens=1024, |
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) |
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if sources: |
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complete_response = "" |
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messages.pop() |
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else: |
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sources = "No environmental report was used to provide this answer." |
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complete_response = "No relevant documents found, for a sourced answer you may want to try a more specific question.\n\n" |
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messages.append({"role": "assistant", "content": complete_response}) |
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timestamp = str(datetime.now().timestamp()) |
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file = user_id[0] + timestamp + ".json" |
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logs = { |
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"user_id": user_id[0], |
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"prompt": query, |
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"retrived": sources, |
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"report_type": report_type, |
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"prompt_eng": messages[0], |
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"answer": messages[-1]["content"], |
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"time": timestamp, |
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} |
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log_on_azure(file, logs, share_client) |
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for chunk in response: |
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if ( |
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chunk_message := chunk["choices"][0].get("text") |
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) and chunk_message != "<|im_end|>": |
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complete_response += chunk_message |
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messages[-1]["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 save_feedback(feed: str, user_id): |
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if len(feed) > 1: |
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timestamp = str(datetime.now().timestamp()) |
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file = user_id[0] + timestamp + ".json" |
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logs = { |
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"user_id": user_id[0], |
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"feedback": feed, |
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"time": timestamp, |
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} |
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log_on_azure(file, logs, share_client) |
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return "Thanks for your feedbacks" |
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def reset_textbox(): |
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return gr.update(value="") |
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def log_on_azure(file, logs, share_client): |
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file_client = share_client.get_file_client(file) |
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file_client.upload_file(str(logs)) |
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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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user_id_state = gr.State([user_id]) |
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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:** when a user sends a message, the system retrieves the most relevant paragraphs from scientific reports that are semantically related to the user's question. These paragraphs are then used to generate a comprehensive and well-sourced answer using a language model. |
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\n **Usage guideline:** the more specific your questions, the more relevant will the documents retrieved be. |
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\n ⚠️ 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(elem_id="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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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=chat, |
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inputs=[ |
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user_id_state, |
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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( |
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save_feedback, |
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inputs=[feedback, user_id_state], |
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) |
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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( |
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set_openai_api_key, inputs=[openai_api_key_textbox] |
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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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""" |
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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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demo.launch() |
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