Fixed Reload Button

The Reload button now reloads your suggestions and excludes the previous suggestions giving you new suggestions.
The animation is fixed

app/main.py

@@ -12,6 +12,7 @@ load_dotenv()  # Load environment variables from .env into os.environ
 
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi import Query
 from pydantic import BaseModel
 
 # Import LangChain components
@@ -298,37 +299,44 @@ def get_llm(model_name: str):
 ###############################################################################
 
 @app.get("/suggestions", response_model=Dict[str, Any])
-async def get_suggestions(model: str = "Mistral", business_idea: str = "", question: str = "") -> Dict[str, Any]:
-    # Construct your prompt using the business idea and the current question.
-    suggestion_template_str = (
+async def get_suggestions(
+    model: str = "Mistral",
+    business_idea: str = "",
+    question: str = "",
+    exclude: List[str] = Query([]),            # ⬅️ now accepts multiple ?exclude=
+) -> Dict[str, Any]:
+    # Construct your base prompt
+    prompt = (
         "For the following business question, provide exactly 3 concise bullet-point suggestions. "
-        "Business idea: \"{business_idea}\". "
-        "Question: \"{question}\". "
-        "Do not include any extra text.\nSuggestions:\n-"
+        f"Business idea: \"{business_idea}\". "
+        f"Question: \"{question}\". "
+        "Do not include any extra text."
     )
+    # If the client provided exclusions, tell the model not to repeat them
+    if exclude:
+        prompt += " Do not repeat any of these suggestions: " + ", ".join(exclude) + "."
+
+    prompt += "\nSuggestions:\n-"
 
-    # (Initialize and run your LLMChain here as you already do.)
-    # For example:
     try:
-        llm = get_llm(model)  # Use your existing get_llm function
+        llm = get_llm(model)  # your existing get_llm
         suggestion_prompt = PromptTemplate(
-            input_variables=["business_idea", "question"],
-            template=suggestion_template_str
+            input_variables=["prompt"],
+            template=prompt
         )
         suggestion_chain = LLMChain(llm=llm, prompt=suggestion_prompt)
-        raw_text = await asyncio.to_thread(
-            suggestion_chain.run,
-            {"business_idea": business_idea, "question": question}
-        )
-
+        # Run the chain without additional inputs, since prompt is fully baked
+        raw_text = await asyncio.to_thread(suggestion_chain.run, {})
+        # Strip out any leading numbers, bullets or whitespace
         suggestion_array = [
-            re.sub(r'^\s*\d+[\).\s]*', '', line).strip()
+            re.sub(r'^\s*[\-\d\.\)\s]+', '', line).strip()
             for line in raw_text.split("\n") if line.strip()
         ]
         if not suggestion_array:
             suggestion_array = ["No suggestions available"]
         return {"suggestions": suggestion_array}
     except Exception as e:
+        # preserve your original error wrapping
         raise HTTPException(status_code=500, detail=f"Error generating suggestions: {str(e)}")
 
 @app.post("/generate")