ensure topic list

Author: yilinxia

backend/app/main.py

@@ -4,6 +4,7 @@
 from services.ai_service import AITopicProcessor
 import os
 import asyncio
+import re
 
 app = Flask(__name__)
 CORS(
@@ -70,8 +71,26 @@ def ai_process():
         )
         loop.close()
         # print(f"AI processing complete. Result length: {len(str(ai_result))}")  # Debug log
-        print(ai_result)
-        return jsonify({"success": True, "result": ai_result})
+
+        # Ensure both lists are lowercase or normalized if needed
+        def extract_topic_name(s):
+            # Try to extract the topic name before the first colon or dash, and strip formatting
+            # Handles cases like '**visual-programming-editor:** ...' or 'visual-programming-editor: ...'
+            match = re.match(r'[*]*([a-zA-Z0-9\-]+)[*]*[:：]', s.strip())
+            if match:
+                return match.group(1)
+            # Fallback: if the string is just the topic name
+            return s.strip().strip('*')
+
+        intersection = []
+        for ai_item in ai_result:
+            topic_name = extract_topic_name(ai_item)
+            if topic_name in selected_topics:
+                intersection.append(ai_item)
+
+        # print("Selected topics:", selected_topics)
+        # print("Intersection:", intersection)
+        return jsonify({"success": True, "result": intersection})
 
     except Exception as e:
         # print(f"Error occurred: {str(e)}")  # Debug log

backend/app/services/ai_service.py

@@ -46,8 +46,8 @@ async def process_with_openai(
         self, prompt: str, topics: List[str], model: str, search_term: str
     ) -> List[str]:
         try:
-            full_prompt = f"""Search term: {search_term}\nCurrent topics: {', '.join(topics)}\n\n{prompt}\n\nPlease provide suggestions as a simple list, one per line. Keep each suggestion concise."""
-
+            full_prompt = f"""Search term: {search_term}\nCurrent topics: {', '.join(topics)}\n\n{prompt}\n\You only return a list of the k topics. K can be any number."""
+            # print(full_prompt)
             response = self.openai_client.chat.completions.create(
                 model=model,
                 messages=[{"role": "user", "content": full_prompt}],
@@ -70,7 +70,7 @@ async def process_with_gemini(self, prompt: str, topics: List[str], search_term:
             if not self.gemini_client:
                 raise HTTPException(status_code=500, detail="Gemini client not initialized")
 
-            full_prompt = f"""Search term: {search_term}\nCurrent topics: {', '.join(topics)}\n\n{prompt}\n\n Please provide suggestions as a simple list, one per line. Keep each suggestion concise."""
+            full_prompt = f"""Search term: {search_term}\nCurrent topics: {', '.join(topics)}\n\n{prompt}\n\n You only return a list of the k topics. K can be any number. No other text."""
 
             response = self.gemini_client.generate_content(full_prompt)
 

src/views/TopicHistogram.tsx

@@ -202,6 +202,10 @@ const TopicHistogram: FC = () => {
     // Add API key input modal - initialize to false instead of !apiKey
     const [showApiKeyModal, setShowApiKeyModal] = useState(false);
 
+    // Add state for AI suggestions
+    const [llmSuggestionsState, setLlmSuggestionsState] = useState<string[]>([]);
+    const [isLlmProcessing, setIsLlmProcessing] = useState(false);
+
     // Function to handle topic click
     const handleTopicClick = (topic: string) => {
         if (!apiKey) {
@@ -381,6 +385,49 @@ const TopicHistogram: FC = () => {
         setTopicExplanation("");
     };
 
+    // Function to handle AI suggestions request
+    const handleRequestSuggestions = async (model: string, prompt: string, apiKey: string, topics: string[]) => {
+        setIsLlmProcessing(true);
+        try {
+            console.log('Requesting AI suggestions with:', { model, prompt, apiKey, topics });
+            const response = await fetch(API_ENDPOINTS.AI_PROCESS, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({
+                    selectedModel: model,
+                    customPrompt: prompt,
+                    apiKey: apiKey,
+                    selectedTopics: topics,
+                    searchTerm: searchTerm
+                })
+            });
+
+            if (!response.ok) {
+                throw new Error(`HTTP error! status: ${response.status}`);
+            }
+
+            const data = await response.json();
+            console.log('Received AI suggestions:', data);
+
+            if (data.success && Array.isArray(data.result)) {
+                setLlmSuggestionsState(data.result);
+            } else {
+                throw new Error('Invalid response format from AI service');
+            }
+        } catch (error) {
+            console.error('Error getting AI suggestions:', error);
+            notify({
+                message: "Failed to get AI suggestions. Please try again.",
+                type: "error"
+            });
+            setLlmSuggestionsState([]);
+        } finally {
+            setIsLlmProcessing(false);
+        }
+    };
+
     return (
         <main className="container-fluid py-4" style={{ height: '100vh', overflowY: 'auto' }}>
             {/* API Key Modal */}
@@ -694,13 +741,10 @@ const TopicHistogram: FC = () => {
             {/* Step 2: Topic Refinement */}
             {currentStep === 2 && (
                 <TopicRefiner
-                    isLlmProcessing={false}
-                    llmSuggestions={[]}
-                    setLlmSuggestions={() => { }}
-                    onRequestSuggestions={async (model, prompt, apiKey, topics) => {
-                        // TODO: Implement AI suggestions
-                        console.log('Requesting suggestions with:', { model, prompt, apiKey, topics });
-                    }}
+                    isLlmProcessing={isLlmProcessing}
+                    llmSuggestions={llmSuggestionsState}
+                    setLlmSuggestions={setLlmSuggestionsState}
+                    onRequestSuggestions={handleRequestSuggestions}
                     selectedTopics={selectedTopics}
                     selectLlmSuggestion={(suggestion) => {
                         if (!selectedTopics.includes(suggestion)) {
@@ -715,6 +759,7 @@ const TopicHistogram: FC = () => {
                     searchTerm={searchTerm}
                 />
             )}
+            {/* {console.log(onRequestSuggestions)} */}
         </main>
     );
 };