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#4251 Google Gemini File Upload with Prompt

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#4251 Google Gemini File Upload with Prompt #4251  Provide a Python application to upload a document to Gemini 2.5 with a prompt and do this in a Tkinter GUI While Gemini 2.5 is the latest and greatest in our lineage, the integration typically uses the Google Gen AI SDK. To get this running, you'll need an API key from Google AI Studio . The following application allows you to select a PDF or text file, enter a prompt, and see the response in a scrollable window. Prerequisites Install the library via terminal: pip install -U google-generativeai The Python Application import tkinter as tk from tkinter import filedialog, messagebox, scrolledtext import google.generativeai as genai import os # --- Configuration --- # Replace with your actual API key or set it as an environment variable API_KEY = "YOUR_GEMINI_API_KEY" genai.configure(api_key=API_KEY) class GeminiApp : def __init__ ( self, root ): self.root = root self.root.title( "Gem...
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#4250 Python Workflow for Knowledge Graph in RDF/SPARQL/Pyvis

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#4250 Python Workflow for Knowledge Graph in RDF/SPARQL/Pyvis #4250  I want to develop the following Python workflow:  CSV -> RDF,  RDF Schema -> SPARQL Query Builder,  Tkinter GUI to query RDF -> Pyvis to visualize graph from query results This is a classic Knowledge Graph engineering stack. To make this work seamlessly, you’ll want to use rdflib as your backbone—it handles the parsing, schema management, and SPARQL execution all in one go. Here is a conceptual breakdown and a starter implementation to get your workflow running. 1. The Core Engine: CSV to RDF To convert CSV to RDF, you map columns to URIs and Literals. Using a schema (RDFS) allows you to define classes and properties, ensuring your graph isn't just "flat" data. import pandas as pd from rdflib import Graph, Literal, RDF, URIRef, Namespace from rdflib.namespace import RDFS, FOAF # Initialize Graph and Namespace g = Graph() EX = Namespace( "http://example.org/" ) g.bind( "ex...
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