Update app.py

app.py

@@ -7,20 +7,25 @@ import io
 import requests
 from bs4 import BeautifulSoup
 
-# Function to generate a knowledge graph from text
-def generate_knowledge_graph_from_text(api_key, 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, 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):
+# Function to scrape text from a website
+def scrape_text_from_url(url):
+    response = requests.get(url)
+    if response.status_code != 200:
+        return "Error: Could not retrieve content from URL."
+    soup = BeautifulSoup(response.text, "html.parser")
+    paragraphs = soup.find_all("p")
+    text = " ".join([p.get_text() for p in paragraphs])
+    return text
+
+def generate_knowledge_graph(api_key, user_input):
     openai.api_key = api_key
+
+    # Check if input is URL or text
+    if user_input.startswith("http://") or user_input.startswith("https://"):
+        user_input = scrape_text_from_url(user_input)
+
+    # Chamar a API da OpenAI
+    print("Chamando a API da OpenAI...")
     completion = openai.ChatCompletion.create(
         model="gpt-3.5-turbo-16k",
         messages=[
@@ -96,55 +101,68 @@ 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
+    print(response_data)
+    print("Type of response_data:", type(response_data))
+    print("Value of response_data:", response_data)
+
+    # Convert to dictionary if it's a string
+    if isinstance(response_data, str):
+        response_data = json.loads(response_data)
 
-# Function to generate a knowledge graph from response data
-def generate_knowledge_graph(response_data):
+    # Visualizar o conhecimento usando Graphviz
+    print("Gerando o conhecimento usando Graphviz...")
     dot = Digraph(comment="Knowledge Graph", format='png')
     dot.attr(dpi='300')
-    dot.attr(bgcolor='white')  # Set background color to white
+    dot.attr(bgcolor='transparent')
+
+    # 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
+    print("Renderizando o gráfico para o formato PNG...")
     image_data = dot.pipe()
     image = Image.open(io.BytesIO(image_data))
-    return image
 
-# Function to scrape text from a website
-def scrape_text_from_url(url):
-    response = requests.get(url)
-    if response.status_code != 200:
-        return "Error: Could not retrieve content from URL."
-    soup = BeautifulSoup(response.text, "html.parser")
-    paragraphs = soup.find_all("p")
-    text = " ".join([p.get_text() for p in paragraphs])
-    return text
+    print("Gráfico gerado com sucesso!")
+
+    return image
 
+# Define a title and description for the Gradio interface using Markdown
 title_and_description = """
 # Instagraph - Knowledge Graph Generator
 
 **Created by [ArtificialGuyBR](https://twitter.com/ArtificialGuyBR)**
 
-This interactive knowledge graph generator allows you to input either text or a URL. 
-If you provide text, it will generate a knowledge graph based on the text you provide. 
-If you provide a URL, it will scrape the content from the webpage and generate a knowledge graph from that.
+This interactive knowledge graph generator is inspired by [this GitHub project](https://github.com/yoheinakajima/instagraph/).
 
-To get started, enter your OpenAI API Key and either your text or a URL.
+Enter your OpenAI API Key and a question, and let the AI create a detailed knowledge graph for you.
 """
 
+# Create the Gradio interface with queueing enabled and concurrency_count set to 10
 iface = gr.Interface(
-    fn=generate_knowledge_graph_from_text,
+    fn=generate_knowledge_graph,
     inputs=[
         gr.inputs.Textbox(label="OpenAI API Key", type="password"),
-        gr.inputs.Textbox(label="Text or URL", type="text"),
+        gr.inputs.Textbox(label="User Input for Graph or URL", type="text"),
     ],
     outputs=gr.outputs.Image(type="pil", label="Generated Knowledge Graph"),
     live=False,
     title=title_and_description,
 )
 
+# Enable queueing system for multiple users
+iface.queue(concurrency_count=10)
+
+print("Iniciando a interface Gradio...")
 iface.launch()