Update app.py

app.py

@@ -6,6 +6,7 @@ from PIL import Image
 import io
 import requests
 from bs4 import BeautifulSoup
+from ast import literal_eval
 
 # Function to scrape text from a website
 def scrape_text_from_url(url):
@@ -102,46 +103,36 @@ def generate_knowledge_graph(api_key, user_input):
     )
 
     response_data = completion.choices[0]["message"]["function_call"]["arguments"]
-    # Debugging: Print the type and value of response_data
-    print(f"Type of response_data: {type(response_data)}")
-    print(f"Value of response_data: {response_data}")
 
     try:
-        # Convert to dictionary if it's a string
         if isinstance(response_data, str):
-            response_data = json.loads(response_data)
-    except json.JSONDecodeError as e:
-        print(f"JSON Decode Error: {e}")
+            response_data = literal_eval(response_data)
+    except (ValueError, SyntaxError) as e:
+        print(f"Error in decoding JSON or literal_eval: {e}")
         return "Error in decoding JSON"
-    
-    # Convert to dictionary if it's a string
-    if isinstance(response_data, str):
-        response_data = json.loads(response_data)
 
-    # Visualizar o conhecimento usando Graphviz
+    if not isinstance(response_data, dict):
+        print("Unexpected data type for response_data")
+        return "Error: Unexpected data type"
+
     dot = Digraph(comment="Knowledge Graph", format='png')
     dot.attr(dpi='300')
     dot.attr(bgcolor='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
 
-# Define a title and description for the Gradio interface using Markdown
 title_and_description = """
 # Instagraph - Knowledge Graph Generator
 
@@ -169,7 +160,6 @@ with gr.Blocks() as app:
         outputs=[result_image]
     )
 
-# Enable queueing system for multiple users
 app.queue(concurrency_count=10)
 
 print("Iniciando a interface Gradio...")