Update app.py

app.py

@@ -4,16 +4,37 @@ import json
 from graphviz import Digraph
 from PIL import Image
 import io
+import requests
+from bs4 import BeautifulSoup
 
-def generate_knowledge_graph(api_key, user_input):
-    openai.api_key = api_key
-
-    # Ensure both API key and user input are provided
+# 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")
 
-    # Chamar a API da OpenAI
-    print("Chamando a API da OpenAI...")
+    # 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
+    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=[
@@ -91,19 +112,14 @@ def generate_knowledge_graph(api_key, user_input):
     )
 
     response_data = completion.choices[0]["message"]["function_call"]["arguments"]
-    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)
+    return 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='transparent')
+    dot.attr(bgcolor='white')  # Set background color to white
 
     # Estilizar os nós
     dot.attr('node', shape='box', style='filled', fillcolor='lightblue', fontcolor='black')
@@ -118,31 +134,40 @@ def generate_knowledge_graph(api_key, user_input):
         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))
 
-    print("Gráfico gerado com sucesso!")
-
     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
+
 # 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 is inspired by [this GitHub project](https://github.com/yoheinakajima/instagraph/).
+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.
 
-Enter your OpenAI API Key and a question, and let the AI create a detailed knowledge graph for you.
+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,
+    fn=generate_knowledge_graph_from_text,
     inputs=[
         gr.inputs.Textbox(label="OpenAI API Key", type="password"),
-        gr.inputs.Textbox(label="User Input for Graph", type="text"),
+        gr.inputs.Textbox(label="Text or URL", type="text"),
     ],
     outputs=gr.outputs.Image(type="pil", label="Generated Knowledge Graph"),
     live=False,