Update app.py

app.py

@@ -1,30 +1,36 @@
 import gradio as gr
 import openai
 import json
-import requests
-from bs4 import BeautifulSoup
 from graphviz import Digraph
 import base64
 from io import BytesIO
 from PIL import Image
 
 def generate_knowledge_graph(api_key, user_input):
+    print("Setting OpenAI API key...")
     openai.api_key = api_key
 
-    completion = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo-16k",
-        messages=[
-            {
-                "role": "user",
-                "content": f"Help me understand following by describing as a detailed knowledge graph: {user_input}",
-            }
-        ],
-        function_call={"name": "knowledge_graph"},
+    print("Making API call to OpenAI...")
+    completion = openai.Completion.create(
+        engine="text-davinci-002",
+        prompt=f"Help me understand the following by describing it as a detailed knowledge graph: {user_input}",
+        max_tokens=100
     )
 
-    response_data = completion.choices[0]["message"]["function_call"]["arguments"]
-    response_dict = json.loads(response_data)
+    print("Received response from OpenAI.")
+    response_data = completion.choices[0].text
+    print(f"Response data: {response_data}")
 
+    # For demonstration, let's assume the response_data is a JSON string that can be converted to a dictionary.
+    # You'll need to write code to interpret the text-based response to generate this dictionary.
+    print("Converting response to JSON...")
+    try:
+        response_dict = json.loads(response_data)
+    except json.JSONDecodeError:
+        print("Failed to decode JSON. Using empty dictionary as a fallback.")
+        response_dict = {}
+
+    print("Generating knowledge graph using Graphviz...")
     dot = Digraph(comment="Knowledge Graph")
 
     # Add nodes to the graph
@@ -36,13 +42,16 @@ def generate_knowledge_graph(api_key, user_input):
         dot.edge(edge["from"], edge["to"], label=edge["relationship"])
 
     # Render to PNG format
+    print("Rendering graph to PNG format...")
     dot.format = "png"
     dot.render(filename="knowledge_graph", cleanup=True)
 
     # Convert PNG to base64 to display in Gradio
+    print("Converting PNG to base64...")
     with open("knowledge_graph.png", "rb") as img_file:
         img_base64 = base64.b64encode(img_file.read()).decode()
 
+    print("Returning base64 image to Gradio interface.")
     return f"data:image/png;base64,{img_base64}"
 
 iface = gr.Interface(
@@ -55,4 +64,5 @@ iface = gr.Interface(
     live=False
 )
 
+print("Launching Gradio interface...")
 iface.launch()