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| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from rapidfuzz import process, fuzz | |
| from docx import Document | |
| from datetime import datetime | |
| from docx.oxml import OxmlElement | |
| from docx.shared import Cm | |
| bens_df = pd.read_excel('bens_tab.xlsx') | |
| data_crawler = pd.read_csv('data_crawler.csv', index_col=False) | |
| data_crawler = data_crawler[['Title', 'Price', 'Currency', 'Condition', 'Link', 'Marketplace']] | |
| def get_report(query, df_nearest): | |
| doc = Document() | |
| # Adding a title | |
| doc.add_heading('RELATÓRIO DE AVALIAÇÃO DE VALOR DE MERCADO – EXP.', level=1) | |
| # Adding some details | |
| doc.add_paragraph('Solicitante:') | |
| doc.add_paragraph('Interessado:') | |
| doc.add_paragraph('Proprietário:') | |
| doc.add_paragraph('Objetivo da Avaliação: Definir o valor de mercado dos bens abaixo discriminados para registro no Patrimônio do Município.') | |
| # Adding section heading and content | |
| doc.add_heading('1. Características do(s) Avaliando(s):', level=2) | |
| doc.add_paragraph('1.1 Avaliando 01:') | |
| doc.add_paragraph(f'Descrição: {query}') | |
| # Add evaluation section | |
| doc.add_heading('2. Avaliação:', level=2) | |
| doc.add_paragraph('2.1 Avaliando 01') | |
| # Add market research section | |
| doc.add_heading('3. Pesquisa de Mercado:', level=2) | |
| # Filter and rename columns in df_nearest | |
| df_nearest_filtered = df_nearest[['ID', 'Title', 'Price', 'Link', 'Marketplace']].copy() | |
| df_nearest_filtered.columns = ['Item', 'Descrição', 'Preço', 'Link', 'Fornecedor'] | |
| # Here you'll insert the df_nearest_filtered as a table | |
| if not df_nearest_filtered.empty: | |
| table = doc.add_table(rows=1, cols=len(df_nearest_filtered.columns)) | |
| table.style = 'Table Grid' | |
| table.autofit = False | |
| table.allow_autofit = False | |
| table.columns[0].width = Cm(3) | |
| table.rows[0].cells[0].width = Cm(3) | |
| hdr_cells = table.rows[0].cells | |
| for i, column_name in enumerate(df_nearest_filtered.columns): | |
| hdr_cells[i].text = column_name | |
| for index, row in df_nearest_filtered.iterrows(): | |
| row_cells = table.add_row().cells | |
| for i, value in enumerate(row): | |
| if i == 3: | |
| row_cells[i].text = str(value) | |
| else: | |
| row_cells[i].text = str(value) | |
| today_date = datetime.now().strftime('%d/%m/%Y') | |
| doc.add_paragraph(f'Lajeado/RS, {today_date}') | |
| doc.add_paragraph('Membros da Comissão:') | |
| # Save the document | |
| doc.save('laudo.docx') | |
| def fetch_data_to_dataframe(query, limit=50, source="mercadolibre"): | |
| if source == "mercadolibre": | |
| BASE_URL = "https://api.mercadolibre.com/sites/MLB/search" | |
| params = {'q': query, 'limit': limit} | |
| response = requests.get(BASE_URL, params=params) | |
| if response.status_code == 200: | |
| data = response.json() | |
| items = data.get('results', []) | |
| df = pd.DataFrame(items)[['title', 'price', 'currency_id', 'condition', 'permalink']] | |
| df.columns = ['Title', 'Price', 'Currency', 'Condition', 'Link'] | |
| df['Marketplace'] = "Mercado Livre" | |
| return df | |
| return pd.DataFrame() | |
| def refinar_resultados(df, include_word=[]): | |
| df['Title'] = df['Title'].astype(str) | |
| # Define a list of keywords to exclude, indicating multiples | |
| exclude_keywords = ["kit", "conjunto", "pacote", "caixa", "unidades", "Kits", " e "] | |
| # Add conditional exclusion for words not included in the query | |
| exclude_patterns = [keyword for keyword in exclude_keywords if keyword not in include_word] | |
| # Combine all exclude patterns into a single regex pattern | |
| exclude_pattern = r'\b(' + '|'.join(exclude_patterns) + r')\b|\b(\d+)\s*(unidade|pacotes|caixas)\b' | |
| # Perform the filtering in one operation | |
| df_refinado = df[~df['Title'].str.contains(exclude_pattern, case=False, regex=True, na=False)] | |
| return df_refinado | |
| def get_best_match(query, choices, limit=50): | |
| # Using RapidFuzz for improved performance and fuzzy matching | |
| matches = process.extract(query, choices, scorer=fuzz.WRatio, limit=limit) | |
| return [match[0] for match in matches if match[1] > 75] | |
| def match_query_words_in_titles(query, title): | |
| """ | |
| Check if all words in the query have a close match within the title. | |
| Returns True if all words match to a certain degree; False otherwise. | |
| """ | |
| query_words = query.lower().split() | |
| match_threshold = 75 # Adjust this threshold as needed | |
| for word in query_words: | |
| # Find the best match for each word in the query within the title | |
| match_score = fuzz.partial_ratio(word, title.lower()) | |
| if match_score < match_threshold: | |
| return False # If any word doesn't match well enough, return False | |
| return True # All words matched well enough | |
| def filtrar_itens_similares(df, termo_pesquisa, limit=50): | |
| # Apply the match function to each title, filtering for those that match the query words | |
| matches = df['Title'].apply(lambda title: match_query_words_in_titles(termo_pesquisa, title)) | |
| df_filtrado = df[matches] | |
| # Further refine the list to the top N matches based on overall similarity to the query | |
| df_filtrado['Overall_Similarity'] = df_filtrado['Title'].apply(lambda title: fuzz.WRatio(termo_pesquisa, title)) | |
| df_filtrado = df_filtrado.sort_values('Overall_Similarity', ascending=False).head(limit) | |
| return df_filtrado | |
| def calcular_fator_avaliacao(titulo, EC, PU): | |
| filtered_df = bens_df[bens_df['TITULO'] == titulo] | |
| if filtered_df.empty: | |
| return None # Or handle the error as needed | |
| bem_info = filtered_df.iloc[0] | |
| VU, VR = bem_info['VIDA_UTIL'], bem_info['VALOR_RESIDUAL'] | |
| ec_pontuacao = {'Excelente': 10, 'Bom': 8, 'Regular': 5, 'Péssimo': 2}[EC] | |
| PU, PVU, PUB = float(PU), min(10 - ((PU - 1) * (10 / VU)), 10), min(10 - (((VU - PU) - 1) * (10 / VU)), 10) | |
| fator_avaliacao = max((4 * ec_pontuacao + 6 * PVU - 3 * PUB) / 100, VR) | |
| return fator_avaliacao | |
| def select_nearest_items(df, query): | |
| # Lower the title similarity threshold if necessary | |
| df['Title_Similarity'] = df['Title'].apply(lambda x: fuzz.WRatio(query, x)) | |
| df_filtered = df[df['Title_Similarity'] > 75] # Adjusted threshold | |
| # Calculate mode price in a more inclusive manner | |
| mode_price = df_filtered['Price'].mode() | |
| if mode_price.empty: | |
| target_price = df_filtered['Price'].median() | |
| else: | |
| target_price = mode_price.min() | |
| df_filtered['Distance'] = (df_filtered['Price'] - target_price).abs() | |
| df_sorted = df_filtered.sort_values(['Distance', 'Title_Similarity'], ascending=[True, False]) | |
| # Ensure diversity in marketplaces | |
| marketplaces_selected = set() | |
| results = [] | |
| for _, row in df_sorted.iterrows(): | |
| if row['Marketplace'] not in marketplaces_selected and len(marketplaces_selected) < 5: | |
| results.append(row) | |
| marketplaces_selected.add(row['Marketplace']) | |
| if len(results) >= 5: | |
| break | |
| return pd.DataFrame(results) | |
| def search_with_fallback(query, df, limit=50): | |
| query_parts = query.split() | |
| include_conjunto = "conjunto" in query.lower() | |
| for i in range(len(query_parts), 0, -1): | |
| simplified_query = " ".join(query_parts[:i]) | |
| include_word = ["conjunto"] if include_conjunto else [] # Ensure include_word is a list | |
| df_refinado = refinar_resultados(df, include_word=include_word) | |
| df_filtrado = filtrar_itens_similares(df_refinado, simplified_query, limit=limit) | |
| if not df_filtrado.empty: | |
| return df_filtrado | |
| return pd.DataFrame() | |
| def integrated_app(query, titulo, EC, PU, selected_rows): | |
| include_conjunto = "conjunto" in query.lower() | |
| df_mercadolibre = fetch_data_to_dataframe(query, 50, "mercadolibre") | |
| df_combined = pd.concat([df_mercadolibre, data_crawler], ignore_index=True) | |
| if df_combined.empty: | |
| return "Nenhum dado encontrado. Tente uma consulta diferente.", pd.DataFrame() | |
| # Pass whether "conjunto" is part of the original query | |
| include_word = ["conjunto"] if include_conjunto else [] | |
| df_refined = refinar_resultados(df_combined, include_word=include_word) | |
| df_similares = search_with_fallback(query, df_refined) | |
| if df_similares.empty: | |
| return "Nenhum item similar encontrado.", pd.DataFrame() | |
| df_nearest = select_nearest_items(df_similares, query) | |
| if df_nearest.empty: | |
| return "Nenhum resultado próximo encontrado.", pd.DataFrame() | |
| if selected_rows.strip(): | |
| selected_indices = [int(idx) for idx in selected_rows.split(',') if idx.isdigit()] | |
| df_nearest = df_nearest.iloc[selected_indices] | |
| # After calculating df_nearest and before returning it | |
| df_nearest.reset_index(drop=True, inplace=True) | |
| df_nearest['ID'] = df_nearest.index | |
| # Reorder DataFrame columns to make ID first | |
| column_order = ['ID'] + [col for col in df_nearest.columns if col != 'ID'] | |
| df_nearest = df_nearest[column_order] | |
| fator_avaliacao = calcular_fator_avaliacao(titulo, EC, PU) | |
| valor_avaliacao = df_nearest['Price'].mean() * fator_avaliacao | |
| get_report(query, df_nearest) | |
| return f"Valor Médio do Bem: R$ {df_nearest['Price'].mean():.2f}, Fator de Avaliação: {fator_avaliacao*100:.2f}%, Valor de Avaliação: R$ {valor_avaliacao:.2f}", df_nearest, 'laudo.docx' | |
| iface = gr.Interface(fn=integrated_app, | |
| inputs=[gr.Textbox(label="Digite sua consulta"), | |
| gr.Dropdown(label="Classificação Contábil do Bem", choices=bens_df['TITULO'].unique().tolist(), value="MOBILIÁRIO EM GERAL"), | |
| gr.Radio(label="Estado de Conservação do Bem", choices=['Excelente', 'Bom', 'Regular', 'Péssimo'], value="Excelente"), | |
| gr.Number(label="Período utilizado (anos)", value=1), | |
| gr.Textbox(label="IDs das linhas selecionadas (separadas por vírgula)", placeholder="Exemplo: 0,2,5")], # Using Textbox for IDs | |
| outputs=[gr.Textbox(label="Cálculo"), gr.Dataframe(label="Resultados da Pesquisa"), gr.File(label="Laudo de Avaliação")], | |
| theme=gr.themes.Monochrome(), | |
| title=f""" | |
| <p style="text-align: left;"> | |
| <b><span style='color: grey; font-size: 50px;'>aval</span><span style='color: orange; font-size: 50px;'>ia</span><span style='color: grey; font-size: 50px;'>.se</span></b> | |
| """, | |
| description="""<p style="text-align: left;">Aplicativo para avaliação de Bens Móveis</p>""") | |
| iface.launch() | |