updated main.py

main.py

@@ -16,11 +16,9 @@ from llama_index.core import Document
 
 PERSIST_DIR = "./storage"
 EMBED_MODEL = "./all-MiniLM-L6-v2"
-EMBED_MODEL = "./all-MiniLM-L6-v2"
-EMBED_MODEL = "./all-MiniLM-L6-v2"
 LLM_MODEL = "llama3-8b-8192" 
 CSV_FILE_PATH = "shl_assessments.csv"
-GROQ_API_KEY = st.secrets["GROQ_API_KEY"]  or os.getenv["GROQ_API_KEY"]  or os.getenv("GROQ_API_KEY")
+GROQ_API_KEY = st.secrets["GROQ_API_KEY"]  or  os.getenv("GROQ_API_KEY")
 
 
 def load_data_from_csv(csv_path):
@@ -43,7 +41,6 @@ def load_data_from_csv(csv_path):
 def load_groq_llm():
     try:
         api_key = st.secrets.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY")
-        api_key = st.secrets.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY")
     except KeyError:
         raise ValueError("GROQ_API_KEY not found in Streamlit secrets.")
     
@@ -52,13 +49,11 @@ def load_groq_llm():
 
 
 def load_embeddings():
-    return HuggingFaceEmbedding(model_name="all-MiniLM-L6-v2")   
-    return HuggingFaceEmbedding(model_name="all-MiniLM-L6-v2")   
+    return HuggingFaceEmbedding(model_name="all-MiniLM-L6-v2")    
 
 def build_index(data):
     """Builds the vector index from the provided assessment data."""
     return HuggingFaceEmbedding(model_name=EMBED_MODEL) 
-    return HuggingFaceEmbedding(model_name=EMBED_MODEL) 
     Settings.llm = load_groq_llm()
 
     documents = [Document(text=f"Name: {item['Assessment Name']}, URL: {item['URL']}, Remote Testing: {item['Remote Testing Support']}, Adaptive/IRT: {item['Adaptive/IRT Support']}, Duration: {item['Duration (min)']}, Type: {item['Test Type']}") for item in data]
@@ -144,16 +139,9 @@ def main():
             "role": "assistant",
             "content": "Hello! I'm your SHL assessment assistant. How can I help you?"
         }]
-        st.session_state.messages = [{
-            "role": "assistant",
-            "content": "Hello! I'm your SHL assessment assistant. How can I help you?"
-        }]
     if "index_built" not in st.session_state:
         st.session_state["index_built"] = False
 
-
-
-
     if not st.session_state["index_built"]:
         try:
             with st.spinner("Loading data and building index..."):