from subprocess import run import gradio as gr from huggingface_hub import snapshot_download from corporate_emission_reports.inference import extract_emissions run(["sh", "install-llamacpp.sh"]) MODEL_PATH = snapshot_download("nopperl/emissions-extraction-lora-merged-GGUF") def predict(input_method, document_file, document_url): document_path = document_file if input_method == "File" else document_url emissions = extract_emissions(document_path, MODEL_PATH, model_name="ggml-model-Q5_K_M.gguf") return emissions.model_dump_json() with open("description.md", "r") as f: description = f.read().strip() with open("article.md", "r") as f: article = f.read().strip() interface = gr.Interface( predict, inputs=[gr.Radio(choices=["File", "URL"], value="File"), gr.File(type="filepath", file_types=[".pdf"], file_count="single", label="Report File"), gr.Textbox(label="Report URL")], outputs=gr.JSON(), description=description, examples = [ ["URL", None, "https://www.deutsche-boerse.com/resource/blob/3373890/1cdeb942b1a02ce3495e25240dfdfe81/data/DBG-Detailed-GRI-index-Deutsche-Bo%CC%88rse-Group-AR-2022.pdf"], ["URL", None, "https://mpmaterials.com/downloads/MP_MATERIALS_2021_ESG_REPORT_A.pdf"], ], article=article, analytics_enabled=False, cache_examples=True, ) interface.queue().launch()