mindmap iter 13

medapp.py

@@ -282,29 +282,21 @@ def mindmap_tab_content(video_data):
             with st.spinner("Generating Mindmap..."):
                 try:
                     # Generate Mindmap
-                    mindmap_html, mindmap_analysis = get_mindmap_data(
+                    mindmap_markdown = get_mindmap_data(
                         st.session_state.user_query,
                         st.session_state.relevant_passages,
-                        st.session_state.answer,
-                        video_data
+                        st.session_state.answer
                     )
-                    st.session_state.mindmap_html = mindmap_html
-                    st.session_state.mindmap_analysis = mindmap_analysis
+                    st.session_state.mindmap_markdown = mindmap_markdown
 
                     st.success("Mindmap generated successfully!")
                     logging.info("Mindmap generated successfully")
                 except Exception as e:
                     st.error(f"Error generating mindmap: {str(e)}")
                     logging.error(f"Error generating mindmap: {str(e)}", exc_info=True)
 
-    if st.session_state.get('mindmap_html'):
-        st.components.v1.html(st.session_state.mindmap_html, height=600)
-
-        st.subheader("Mindmap Analysis")
-        if st.session_state.mindmap_analysis:
-            st.markdown(st.session_state.mindmap_analysis)
-        else:
-            st.info("No mindmap analysis available yet.")
+    if st.session_state.get('mindmap_markdown'):
+        st.markdown(st.session_state.mindmap_markdown)
     else:
         st.info("Generate a mindmap by submitting a query in the Main tab and then clicking the 'Generate Mindmap' button above.")
 
@@ -323,4 +315,3 @@ def mermaid_to_html(mermaid_code):
 
 if __name__ == "__main__":
     main()
-

mindmap.py

@@ -9,7 +9,6 @@
 from pyvis.network import Network
 
 def extract_key_terms(relevant_passages):
-    # Extract key terms from the relevant passages
     context = " ".join([p['text'] for p in relevant_passages])
     client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
     response = client.chat.completions.create(
@@ -30,68 +29,29 @@ def extract_key_terms(relevant_passages):
     key_terms = response.choices[0].message.content.strip().split(',')
     return [term.strip() for term in key_terms if term.strip()]
 
-def generate_mindmap(query, relevant_passages, answer, all_data):
+def generate_mindmap(query, relevant_passages, answer):
     client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-    # Extract key terms from relevant passages
     key_terms = extract_key_terms(relevant_passages)
-
-    # Prepare the context for OpenAI
     key_terms_text = "\n".join([f"- {term}" for term in key_terms])
 
-    # Generate mindmap content using OpenAI
     response = client.chat.completions.create(
-        model="gpt-4o-mini",
+        model="gpt-4",
         messages=[
             {
                 "role": "system",
-                "content": (
-                    "Create a detailed and accurate mindmap structure based on the given query and key terms. "
-                    "Use Markdown format with # for main topics, ## for subtopics, and ### for further details. "
-                    "Ensure the content is grounded in the provided key terms and aligns with the USMLE Step 1 syllabus."
-                )
+                "content": "Create a detailed and accurate mindmap structure based on the given query and key terms. Use Markdown format with # for main topics, ## for subtopics, and ### for further details."
             },
             {
                 "role": "user",
-                "content": (
-                    f"Query: {query}\n\nKey Terms:\n{key_terms_text}\n\n"
-                    f"Answer: {answer}\n\n"
-                    "Create a detailed mindmap structure in Markdown format, ensuring it is comprehensive, informative, and grounded in the key terms:"
-                )
+                "content": f"Query: {query}\n\nKey Terms:\n{key_terms_text}\n\nAnswer: {answer}\n\nCreate a detailed mindmap structure in Markdown format:"
             }
         ],
         max_tokens=800,
         temperature=0.7,
     )
 
     mindmap_structure = response.choices[0].message.content.strip()
-
-    # Generate analysis and summary
-    analysis_response = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {
-                "role": "system",
-                "content": (
-                    "Provide a detailed analysis of how the query relates to the key terms and other topics in the USMLE Step 1 syllabus. "
-                    "Focus on the interconnections and ensure the analysis is accurate and informative."
-                )
-            },
-            {
-                "role": "user",
-                "content": (
-                    f"Query: {query}\n\nMindmap Structure:\n{mindmap_structure}\n\n"
-                    "Provide a detailed analysis highlighting the connections between topics, ensuring minimal fluff and maximum informational value:"
-                )
-            }
-        ],
-        max_tokens=800,
-        temperature=0.7,
-    )
-
-    analysis = analysis_response.choices[0].message.content.strip()
-
-    return mindmap_structure, analysis
+    return mindmap_structure
 
 def create_mindmap(mindmap_structure):
     G = nx.DiGraph()
@@ -183,7 +143,6 @@ def create_mindmap(mindmap_structure):
 
     return content
 
-def get_mindmap_data(query, relevant_passages, answer, all_data):
-    mindmap_structure, analysis = generate_mindmap(query, relevant_passages, answer, all_data)
-    mindmap_html = create_mindmap(mindmap_structure)
-    return mindmap_html, analysis
+def get_mindmap_data(query, relevant_passages, answer):
+    mindmap_structure = generate_mindmap(query, relevant_passages, answer)
+    return mindmap_structure