Update app.py

app.py

@@ -32,14 +32,15 @@ generator = load_text_generator()
 # Idea Generation Functions
 # ---------------------------
 def generate_ideas_with_hf(prompt):
-    # Use Hugging Face's text-generation pipeline (less creative than GPT‑3.5)
-    results = generator(prompt, max_length=150, num_return_sequences=1)
+    # Use Hugging Face's text-generation pipeline.
+    # Instead of using max_length, we use max_new_tokens so that new tokens are generated.
+    results = generator(prompt, max_new_tokens=50, num_return_sequences=1)
     idea_text = results[0]['generated_text']
     return idea_text
 
 def generate_ideas_with_openai(prompt, api_key):
     """
-    Generates research ideas using OpenAI's GPT‑3.5 model with streaming.
+    Generates research ideas using OpenAI's GPT-3.5 model with streaming.
     This function uses the latest OpenAI SDK v1.0 and asynchronous API calls.
     """
     openai.api_key = api_key
@@ -190,15 +191,14 @@ if st.button("Generate Knowledge Graph"):
             # Build a directed graph using NetworkX
             G = nx.DiGraph()
             for paper in data:
-                # Ensure each node has a 'title' key, even if it's an empty string.
-                G.add_node(paper["paper_id"], title=paper.get("title", ""))
+                # Ensure each node has a 'title' key, using the node id as fallback.
+                G.add_node(paper["paper_id"], title=paper.get("title", str(paper["paper_id"])))
                 for cited in paper["cited"]:
                     G.add_edge(paper["paper_id"], cited)
             
             st.subheader("Knowledge Graph")
             # Create an interactive visualization using Pyvis
             net = Network(height="500px", width="100%", directed=True)
-            # Use get() to avoid KeyError and provide a fallback label.
             for node, node_data in G.nodes(data=True):
                 net.add_node(node, label=node_data.get("title", str(node)))
             for source, target in G.edges():