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| import gradio as gr | |
| from haystack.document_stores import FAISSDocumentStore | |
| from haystack.nodes import EmbeddingRetriever | |
| import openai | |
| import os | |
| from utils import ( | |
| make_pairs, | |
| set_openai_api_key, | |
| get_random_string, | |
| ) | |
| system_template = {"role": "system", "content": os.environ["content"]} | |
| retrieve_all = EmbeddingRetriever( | |
| document_store=FAISSDocumentStore.load( | |
| index_path="./documents/climate_gpt.faiss", | |
| config_path="./documents/climate_gpt.json", | |
| ), | |
| embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", | |
| model_format="sentence_transformers", | |
| ) | |
| retrieve_giec = EmbeddingRetriever( | |
| document_store=FAISSDocumentStore.load( | |
| index_path="./documents/climate_gpt_only_giec.faiss", | |
| config_path="./documents/climate_gpt_only_giec.json", | |
| ), | |
| embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", | |
| model_format="sentence_transformers", | |
| ) | |
| def chat( | |
| query: str, history: list = [system_template], report_type: str = "All available", threshold: float = 0.559 | |
| ) -> tuple: | |
| """retrieve relevant documents in the document store then query gpt-turbo | |
| Args: | |
| query (str): user message. | |
| history (list, optional): history of the conversation. Defaults to [system_template]. | |
| report_type (str, optional): should be "All available" or "IPCC only". Defaults to "All available". | |
| threshold (float, optional): similarity threshold, don't increase more than 0.568. Defaults to 0.56. | |
| Yields: | |
| tuple: chat gradio format, chat openai format, sources used. | |
| """ | |
| if report_type == "All available": | |
| retriever = retrieve_all | |
| elif report_type == "IPCC only": | |
| retriever = retrieve_giec | |
| else: | |
| raise Exception("report_type arg should be in (All available, IPCC only)") | |
| docs = retriever.retrieve(query=query, top_k=10) | |
| messages = history + [{"role": "user", "content": query}] | |
| sources = "\n\n".join( | |
| f"doc {i}: {d.meta['file_name']} page {d.meta['page_number']}\n{d.content}" | |
| for i, d in enumerate(docs, 1) | |
| if d.score > threshold | |
| ) | |
| if sources: | |
| messages.append({"role": "system", "content": f"{os.environ['sources']}\n\n{sources}"}) | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=messages, | |
| temperature=0.2, | |
| stream=True, | |
| ) | |
| if sources: | |
| messages.pop() | |
| complete_response = "" | |
| else: | |
| sources = "No environmental report was used to provide this answer." | |
| complete_response = ( | |
| "No relevant documents found, for a sourced answer you may want to try a more specific question.\n\n" | |
| ) | |
| messages.append({"role": "assistant", "content": complete_response}) | |
| for chunk in response: | |
| if chunk_message := chunk["choices"][0]["delta"].get("content", None): | |
| complete_response += chunk_message | |
| messages[-1]["content"] = complete_response | |
| gradio_format = make_pairs([a["content"] for a in messages[1:]]) | |
| yield gradio_format, messages, sources | |
| def test(feed: str): | |
| print(feed) | |
| def reset_textbox(): | |
| return gr.update(value="") | |
| # Gradio | |
| css_code = ".gradio-container {background-image: url('file=background.png');background-position: top right}" | |
| with gr.Blocks(title="🌍 ClimateGPT Ekimetrics", css=css_code) as demo: | |
| openai.api_key = os.environ["api_key"] | |
| user_id = gr.State([get_random_string(10)]) | |
| with gr.Tab("App"): | |
| gr.Markdown("# Welcome to Climate GPT 🌍 !") | |
| gr.Markdown( | |
| """ 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. | |
| \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. | |
| \n **Usage guideline:** more sources will be retrieved using precise questions. | |
| \n ⚠️ Always refer to the source to ensure the validity of the information communicated. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| chatbot = gr.Chatbot(elem_id="chatbot") | |
| state = gr.State([system_template]) | |
| with gr.Row(): | |
| ask = gr.Textbox( | |
| show_label=False, | |
| placeholder="Enter text and press enter", | |
| sample_inputs=["which country polutes the most ?"], | |
| ).style(container=False) | |
| print(f"Type from ask textbox {ask.type}") | |
| 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=chat, | |
| inputs=[ | |
| ask, | |
| state, | |
| gr.inputs.Dropdown( | |
| ["IPCC only", "All available"], | |
| default="All available", | |
| label="Select reports", | |
| ), | |
| ], | |
| outputs=[chatbot, state, sources_textbox], | |
| ) | |
| ask.submit(reset_textbox, [], [ask]) | |
| with gr.Accordion("Feedbacks", open=False): | |
| gr.Markdown("Please complete some feedbacks 🙏") | |
| feedback = gr.Textbox() | |
| feedback_save = gr.Button(value="submit feedback") | |
| feedback_save.click(test, inputs=[feedback]) | |
| with gr.Accordion("Add your personal openai api key - Option", open=False): | |
| openai_api_key_textbox = gr.Textbox( | |
| placeholder="Paste your OpenAI API key (sk-...) and hit Enter", | |
| show_label=False, | |
| lines=1, | |
| type="password", | |
| ) | |
| 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]) | |
| with gr.Tab("Information"): | |
| gr.Markdown( | |
| """ | |
| ## 📖 Reports used : \n | |
| - First Assessment Report on the Physical Science of Climate Change | |
| - Second assessment Report on Climate Change Adaptation | |
| - Third Assessment Report on Climate Change Mitigation | |
| - Food Outlook Biannual Report on Global Food Markets | |
| - IEA's report on the Role of Critical Minerals in Clean Energy Transitions | |
| - Limits to Growth | |
| - Outside The Safe operating system of the Planetary Boundary for Novel Entities | |
| - Planetary Boundaries Guiding | |
| - State of the Oceans report | |
| - Word Energy Outlook 2021 | |
| - Word Energy Outlook 2022 | |
| - The environmental impacts of plastics and micro plastics use, waste and polution ET=U and national measures | |
| - IPBES Global report - MArch 2022 | |
| \n | |
| IPCC is a United Nations body that assesses the science related to climate change, including its impacts and possible response options. | |
| The IPCC is considered the leading scientific authority on all things related to global climate change. | |
| """ | |
| ) | |
| with gr.Tab("Examples"): | |
| gr.Markdown("See here some examples on how to use the Chatbot") | |
| demo.queue(concurrency_count=16) | |
| demo.launch() | |