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import re |
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from collections import defaultdict |
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from climateqa.utils import get_image_from_azure_blob_storage |
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from climateqa.engine.chains.prompts import audience_prompts |
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from PIL import Image |
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from io import BytesIO |
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import base64 |
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def make_pairs(lst:list)->list: |
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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 serialize_docs(docs:list)->list: |
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new_docs = [] |
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for doc in docs: |
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new_doc = {} |
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new_doc["page_content"] = doc.page_content |
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new_doc["metadata"] = doc.metadata |
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new_docs.append(new_doc) |
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return new_docs |
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def parse_output_llm_with_sources(output:str)->str: |
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content_parts = re.split(r'\[(Doc\s?\d+(?:,\s?Doc\s?\d+)*)\]', output) |
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parts = [] |
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for part in content_parts: |
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if part.startswith("Doc"): |
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subparts = part.split(",") |
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subparts = [subpart.lower().replace("doc","").strip() for subpart in subparts] |
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subparts = [f"""<a href="#doc{subpart}" class="a-doc-ref" target="_self"><span class='doc-ref'><sup>{subpart}</sup></span></a>""" for subpart in subparts] |
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parts.append("".join(subparts)) |
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else: |
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parts.append(part) |
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content_parts = "".join(parts) |
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return content_parts |
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def process_figures(docs:list)->tuple: |
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gallery=[] |
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used_figures =[] |
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figures = '<div class="figures-container"><p></p> </div>' |
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docs_figures = [d for d in docs if d.metadata["chunk_type"] == "image"] |
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for i, doc in enumerate(docs_figures): |
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if doc.metadata["chunk_type"] == "image": |
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if doc.metadata["figure_code"] != "N/A": |
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title = f"{doc.metadata['figure_code']} - {doc.metadata['short_name']}" |
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else: |
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title = f"{doc.metadata['short_name']}" |
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if title not in used_figures: |
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used_figures.append(title) |
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try: |
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key = f"Image {i+1}" |
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image_path = doc.metadata["image_path"].split("documents/")[1] |
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img = get_image_from_azure_blob_storage(image_path) |
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buffered = BytesIO() |
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max_image_length = 500 |
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img_resized = img.resize((max_image_length, int(max_image_length * img.size[1]/img.size[0]))) |
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img_resized.save(buffered, format="PNG") |
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img_str = base64.b64encode(buffered.getvalue()).decode() |
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figures = figures + make_html_figure_sources(doc, i, img_str) |
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gallery.append(img) |
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except Exception as e: |
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print(f"Skipped adding image {i} because of {e}") |
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return figures, gallery |
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def generate_html_graphs(graphs:list)->str: |
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categories = defaultdict(list) |
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for graph in graphs: |
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category = graph['metadata']['category'] |
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categories[category].append(graph['embedding']) |
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html_code = ''' |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>Graphs by Category</title> |
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<style> |
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.tab-content { |
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display: none; |
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} |
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.tab-content.active { |
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display: block; |
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} |
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.tabs { |
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margin-bottom: 20px; |
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} |
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.tab-button { |
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background-color: #ddd; |
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border: none; |
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padding: 10px 20px; |
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cursor: pointer; |
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margin-right: 5px; |
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} |
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.tab-button.active { |
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background-color: #ccc; |
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} |
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</style> |
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<script> |
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function showTab(tabId) { |
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var contents = document.getElementsByClassName('tab-content'); |
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var buttons = document.getElementsByClassName('tab-button'); |
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for (var i = 0; i < contents.length; i++) { |
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contents[i].classList.remove('active'); |
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buttons[i].classList.remove('active'); |
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} |
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document.getElementById(tabId).classList.add('active'); |
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document.querySelector('button[data-tab="'+tabId+'"]').classList.add('active'); |
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} |
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</script> |
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</head> |
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<body> |
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<div class="tabs"> |
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''' |
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for i, category in enumerate(categories.keys()): |
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active_class = 'active' if i == 0 else '' |
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html_code += f'<button class="tab-button {active_class}" onclick="showTab(\'tab-{i}\')" data-tab="tab-{i}">{category}</button>' |
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html_code += '</div>' |
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for i, (category, embeds) in enumerate(categories.items()): |
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active_class = 'active' if i == 0 else '' |
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html_code += f'<div id="tab-{i}" class="tab-content {active_class}">' |
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for embed in embeds: |
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html_code += embed |
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html_code += '</div>' |
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html_code += ''' |
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</body> |
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</html> |
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''' |
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return html_code |
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def make_html_source(source,i): |
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meta = source.metadata |
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content = source.page_content.strip() |
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toc_levels = [] |
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for j in range(2): |
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level = meta[f"toc_level{j}"] |
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if level != "N/A": |
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toc_levels.append(level) |
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else: |
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break |
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toc_levels = " > ".join(toc_levels) |
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if len(toc_levels) > 0: |
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name = f"<b>{toc_levels}</b><br/>{meta['name']}" |
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else: |
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name = meta['name'] |
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score = meta['reranking_score'] |
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if score > 0.8: |
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color = "score-green" |
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elif score > 0.5: |
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color = "score-orange" |
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else: |
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color = "score-red" |
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relevancy_score = f"<p class=relevancy-score>Relevancy score: <span class='{color}'>{score:.1%}</span></p>" |
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if meta["chunk_type"] == "text": |
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card = f""" |
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<div class="card" id="doc{i}"> |
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<div class="card-content"> |
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<h2>Doc {i} - {meta['short_name']} - Page {int(meta['page_number'])}</h2> |
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<p>{content}</p> |
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{relevancy_score} |
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</div> |
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<div class="card-footer"> |
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<span>{name}</span> |
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<a href="{meta['url']}#page={int(meta['page_number'])}" target="_blank" class="pdf-link"> |
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<span role="img" aria-label="Open PDF">🔗</span> |
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</a> |
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</div> |
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</div> |
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""" |
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else: |
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if meta["figure_code"] != "N/A": |
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title = f"{meta['figure_code']} - {meta['short_name']}" |
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else: |
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title = f"{meta['short_name']}" |
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card = f""" |
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<div class="card card-image"> |
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<div class="card-content"> |
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<h2>Image {i} - {title} - Page {int(meta['page_number'])}</h2> |
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<p class='ai-generated'>AI-generated description</p> |
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<p>{content}</p> |
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{relevancy_score} |
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</div> |
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<div class="card-footer"> |
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<span>{name}</span> |
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<a href="{meta['url']}#page={int(meta['page_number'])}" target="_blank" class="pdf-link"> |
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<span role="img" aria-label="Open PDF">🔗</span> |
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</a> |
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</div> |
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</div> |
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""" |
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return card |
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def make_html_papers(df,i): |
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title = df['title'][i] |
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content = df['abstract'][i] |
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url = df['doi'][i] |
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publication_date = df['publication_year'][i] |
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subtitle = df['subtitle'][i] |
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card = f""" |
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<div class="card" id="doc{i}"> |
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<div class="card-content"> |
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<h2>Doc {i+1} - {title}</h2> |
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<p>{content}</p> |
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</div> |
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<div class="card-footer"> |
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<span>{subtitle}</span> |
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<a href="{url}" target="_blank" class="pdf-link"> |
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<span role="img" aria-label="Open paper">🔗</span> |
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</a> |
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</div> |
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</div> |
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""" |
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return card |
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def make_html_figure_sources(source,i,img_str): |
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meta = source.metadata |
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content = source.page_content.strip() |
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score = meta['reranking_score'] |
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if score > 0.8: |
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color = "score-green" |
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elif score > 0.5: |
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color = "score-orange" |
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else: |
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color = "score-red" |
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toc_levels = [] |
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if len(toc_levels) > 0: |
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name = f"<b>{toc_levels}</b><br/>{meta['name']}" |
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else: |
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name = meta['name'] |
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relevancy_score = f"<p class=relevancy-score>Relevancy score: <span class='{color}'>{score:.1%}</span></p>" |
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if meta["figure_code"] != "N/A": |
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title = f"{meta['figure_code']} - {meta['short_name']}" |
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else: |
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title = f"{meta['short_name']}" |
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card = f""" |
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<div class="card card-image"> |
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<div class="card-content"> |
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<h2>Image {i} - {title} - Page {int(meta['page_number'])}</h2> |
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<img src="data:image/png;base64, { img_str }" alt="Alt text" /> |
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<p class='ai-generated'>AI-generated description</p> |
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<p>{content}</p> |
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{relevancy_score} |
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</div> |
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<div class="card-footer"> |
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<span>{name}</span> |
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<a href="{meta['url']}#page={int(meta['page_number'])}" target="_blank" class="pdf-link"> |
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<span role="img" aria-label="Open PDF">🔗</span> |
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</a> |
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</div> |
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</div> |
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""" |
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return card |
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def make_toolbox(tool_name,description = "",checked = False,elem_id = "toggle"): |
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if checked: |
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span = "<span class='checkmark'>✓</span>" |
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else: |
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span = "<span class='loader'></span>" |
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toolbox = f""" |
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<div class="dropdown"> |
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<label for="{elem_id}" class="dropdown-toggle"> |
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{span} |
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{tool_name} |
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</label> |
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</div> |
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""" |
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return toolbox |
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