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| import gradio as gr | |
| import os | |
| import PyPDF2 | |
| import pandas as pd | |
| import docx | |
| import json | |
| from docx import Document | |
| from transformers import pipeline | |
| # Configurar Hugging Face API Token | |
| HF_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN") | |
| # Carregar o modelo DeepSeek Coder 1.3B | |
| chatbot_pipeline = pipeline("text-generation", model="deepseek-ai/deepseek-coder-1.3b-instruct", token=HF_API_TOKEN) | |
| def extract_files_from_folder(folder_path): | |
| """Scans a folder for PDF, TXT, CSV, DOCX, and IPYNB files.""" | |
| extracted_files = {"pdf": [], "txt": [], "csv": [], "docx": [], "ipynb": []} | |
| for root, _, files in os.walk(folder_path): | |
| for file_name in files: | |
| file_path = os.path.join(root, file_name) | |
| if file_name.endswith(".pdf"): | |
| extracted_files["pdf"].append(file_path) | |
| elif file_name.endswith(".txt"): | |
| extracted_files["txt"].append(file_path) | |
| elif file_name.endswith(".csv"): | |
| extracted_files["csv"].append(file_path) | |
| elif file_name.endswith(".docx"): | |
| extracted_files["docx"].append(file_path) | |
| elif file_name.endswith(".ipynb"): | |
| extracted_files["ipynb"].append(file_path) | |
| return extracted_files | |
| def get_text_from_pdf(pdf_files): | |
| text = "" | |
| for pdf_path in pdf_files: | |
| with open(pdf_path, "rb") as pdf_file: | |
| reader = PyPDF2.PdfReader(pdf_file) | |
| for page in reader.pages: | |
| text += page.extract_text() + "\n" | |
| return text | |
| def read_text_from_files(file_paths): | |
| text = "" | |
| for file_path in file_paths: | |
| with open(file_path, "r", encoding="utf-8", errors="ignore") as file: | |
| text += file.read() + "\n" | |
| return text | |
| def get_text_from_csv(csv_files): | |
| text = "" | |
| for csv_path in csv_files: | |
| df = pd.read_csv(csv_path) | |
| text += df.to_string() + "\n" | |
| return text | |
| def get_text_from_docx(docx_files): | |
| text = "" | |
| for docx_path in docx_files: | |
| doc = Document(docx_path) | |
| for para in doc.paragraphs: | |
| text += para.text + "\n" | |
| return text | |
| def combine_text_from_files(extracted_files): | |
| text = ( | |
| get_text_from_pdf(extracted_files["pdf"]) + | |
| read_text_from_files(extracted_files["txt"]) + | |
| get_text_from_csv(extracted_files["csv"]) + | |
| get_text_from_docx(extracted_files["docx"]) | |
| ) | |
| return text | |
| def generate_response(question, text): | |
| """Uses the DeepSeek Coder model to answer questions based on extracted text.""" | |
| prompt = f"Question: {question}\nBased on the following document content:\n{text[:3000]}" # Limite de 3000 caracteres | |
| response = chatbot_pipeline(prompt, max_length=500, truncation=True)[0]['generated_text'] | |
| return response.strip() | |
| def chatbot_interface(question): | |
| folder_path = "New_Data_Analytics/" | |
| extracted_files = extract_files_from_folder(folder_path) | |
| text = combine_text_from_files(extracted_files) | |
| if not text.strip(): | |
| return "No valid files found. Please upload supported file types." | |
| return generate_response(question, text) | |
| demo = gr.Interface( | |
| fn=chatbot_interface, | |
| inputs=gr.Textbox(label="Ask a question", placeholder="Type your question here..."), | |
| outputs=gr.Textbox(label="Answer") | |
| ) | |
| demo.launch() | |