Update app.py

app.py

@@ -9,32 +9,18 @@ from bs4 import BeautifulSoup
 
 # Function to generate a knowledge graph from text
 def generate_knowledge_graph_from_text(api_key, user_input):
-    # Ensure the API key and user input are provided
-    if not api_key or not user_input:
-        raise ValueError("Please provide both the OpenAI API Key and User Input")
-
-    # Process user input
     response_data = process_user_input(api_key, user_input)
     return generate_knowledge_graph(response_data)
 
 # Function to generate a knowledge graph from a URL
-def generate_knowledge_graph_from_url(api_key, url):
-    # Ensure the API key and URL are provided
-    if not api_key or not url:
-        raise ValueError("Please provide both the OpenAI API Key and a URL")
-
-    # Scrape text from the provided URL
-    text = scrape_text_from_url(url)
-
-    # Process the scraped text
+def generate_knowledge_graph_from_url(api_key, user_input):
+    text = scrape_text_from_url(user_input)
     response_data = process_user_input(api_key, text)
     return generate_knowledge_graph(response_data)
 
 # Function to process user input and call OpenAI API
 def process_user_input(api_key, user_input):
     openai.api_key = api_key
-
-    # Call the OpenAI API
     completion = openai.ChatCompletion.create(
         model="gpt-3.5-turbo-16k",
         messages=[
@@ -110,33 +96,22 @@ def process_user_input(api_key, user_input):
         ],
         function_call={"name": "knowledge_graph"},
     )
-
     response_data = completion.choices[0]["message"]["function_call"]["arguments"]
     return response_data
 
 # Function to generate a knowledge graph from response data
 def generate_knowledge_graph(response_data):
-    # Visualizar o conhecimento usando Graphviz
     dot = Digraph(comment="Knowledge Graph", format='png')
     dot.attr(dpi='300')
     dot.attr(bgcolor='white')  # Set background color to white
-
-    # Estilizar os nós
     dot.attr('node', shape='box', style='filled', fillcolor='lightblue', fontcolor='black')
-
     for node in response_data.get("nodes", []):
         dot.node(node["id"], f"{node['label']} ({node['type']})", color=node.get("color", "lightblue"))
-
-    # Estilizar as arestas
     dot.attr('edge', color='black', fontcolor='black')
-
     for edge in response_data.get("edges", []):
         dot.edge(edge["from"], edge["to"], label=edge["relationship"], color=edge.get("color", "black"))
-
-    # Renderizar para o formato PNG
     image_data = dot.pipe()
     image = Image.open(io.BytesIO(image_data))
-
     return image
 
 # Function to scrape text from a website
@@ -149,7 +124,6 @@ def scrape_text_from_url(url):
     text = " ".join([p.get_text() for p in paragraphs])
     return text
 
-# Define a title and description for the Gradio interface using Markdown
 title_and_description = """
 # Instagraph - Knowledge Graph Generator
 
@@ -162,7 +136,6 @@ If you provide a URL, it will scrape the content from the webpage and generate a
 To get started, enter your OpenAI API Key and either your text or a URL.
 """
 
-# Create the Gradio interface with queueing enabled and concurrency_count set to 10
 iface = gr.Interface(
     fn=generate_knowledge_graph_from_text,
     inputs=[
@@ -174,8 +147,4 @@ iface = gr.Interface(
     title=title_and_description,
 )
 
-# Enable queueing system for multiple users
-iface.queue(concurrency_count=10)
-
-print("Iniciando a interface Gradio...")
 iface.launch()