Update src/streamlit_app.py

src/streamlit_app.py

@@ -45,33 +45,51 @@ class RoutePrediction(BaseModel):
 # --- 3. CACHED INITIALIZATION ---
 @st.cache_resource(show_spinner=False)
 def initialize_resources():
-    print("🔌 Initializing Strict-Boundary Agent...")
+    print("🔌 Initializing Agent...")
     
-    # Setup LlamaIndex Settings
     Settings.llm = OpenAI(model="gpt-4o-mini", temperature=0)
     Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
 
-    # Load CSV
-    try:
-        script_dir = os.path.dirname(os.path.abspath(__file__))
-        # Combine with filename
-        csv_path = os.path.join(script_dir, 'src/nasdaq-listed.csv')
-        # Debug print to logs
-        print(f"📂 Looking for CSV at: {csv_path}")
-        nasdaq_df = pd.read_csv('nasdaq-listed.csv')
-        nasdaq_df.columns = [c.strip() for c in nasdaq_df.columns]
-    except Exception as e:
-        st.error(f"CSV Load Error: {e}")
+    # --- CSV PATH FINDER ---
+    # We check ALL possible locations
+    possible_paths = [
+        "nasdaq-listed.csv",                                      # Root directory
+        "src/nasdaq-listed.csv",                                  # Src folder
+        os.path.join(os.getcwd(), "nasdaq-listed.csv"),           # Current Working Directory
+        os.path.join(os.path.dirname(__file__), "nasdaq-listed.csv"), # Same folder as script
+        "../nasdaq-listed.csv"                                    # One level up
+    ]
+    
+    csv_path = None
+    for path in possible_paths:
+        if os.path.exists(path):
+            csv_path = path
+            print(f"✅ Found CSV at: {path}")
+            break
+            
+    if csv_path:
+        try:
+            nasdaq_df = pd.read_csv(csv_path)
+            nasdaq_df.columns = [c.strip() for c in nasdaq_df.columns]
+        except Exception as e:
+            st.error(f"CSV Corrupt: {e}")
+            nasdaq_df = pd.DataFrame()
+    else:
+        st.error(f"❌ CRITICAL: 'nasdaq-listed.csv' not found. I looked in: {possible_paths}")
         nasdaq_df = pd.DataFrame()
 
-    # Connect to Pinecone
-    api_key = os.environ.get("PINECONE_API_KEY")
-    if not api_key: raise ValueError("Pinecone Key Missing")
-    
-    pc = Pinecone(api_key=api_key)
-    index = VectorStoreIndex.from_vector_store(
-        vector_store=PineconeVectorStore(pinecone_index=pc.Index("financial-rag-agent"))
-    )
+    # --- Connect to Pinecone ---
+    try:
+        api_key = os.environ.get("PINECONE_API_KEY")
+        if not api_key: raise ValueError("Pinecone Key Missing")
+        
+        pc = Pinecone(api_key=api_key)
+        index = VectorStoreIndex.from_vector_store(
+            vector_store=PineconeVectorStore(pinecone_index=pc.Index("financial-rag-agent"))
+        )
+    except Exception as e:
+        st.error(f"Pinecone Error: {e}")
+        return nasdaq_df, None
     
     return nasdaq_df, index