Update app.py

fix connecting issue with google

app.py

@@ -8,9 +8,8 @@ import shutil
 import asyncio
 
 # ==========================================
-# 0. ASYNC FIX (CRITICAL FOR STREAMLIT)
+# 0. ASYNC FIX (CRITICAL)
 # ==========================================
-# Fixes "No event loop" errors
 try:
     asyncio.get_running_loop()
 except RuntimeError:
@@ -23,15 +22,19 @@ st.set_page_config(page_title="Bank Loan Agent", layout="wide")
 warnings.filterwarnings("ignore")
 
 # ==========================================
-# 2. CONSTANTS & IMPORTS
+# 2. IMPORTS & CONSTANTS
 # ==========================================
 DB_FILE = "bank.db"
 INDEX_PATH = "faiss_index"
 REQUIRED_PDFS = ["Bank Loan Overall Risk Policy.pdf", "Bank Loan Interest Rate Policy.pdf"]
 
 try:
+    # PROVIDERS
     from langchain_groq import ChatGroq
     from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+    import google.generativeai as genai  # <--- DIRECT IMPORT FOR VALIDATION
+    
+    # SHARED
     from langchain_huggingface import HuggingFaceEmbeddings
     from langchain_community.vectorstores import FAISS
     from langchain_community.callbacks import StreamlitCallbackHandler
@@ -43,7 +46,7 @@ try:
     from langchain_core.tools import tool
     from langchain.agents import AgentExecutor, create_tool_calling_agent
 except ImportError as e:
-    st.error(f"❌ Critical Import Error: {e}")
+    st.error(f"❌ Import Error: {e}")
     st.stop()
 
 # ==========================================
@@ -101,32 +104,31 @@ def check_pr_status(user_id: str) -> str:
     return f"PR Status: {row[0]}" if (row and not isinstance(row, str)) else "PR Status: False."
 
 # ==========================================
-# 5. UI & AUTH
+# 5. UI & AUTHENTICATION
 # ==========================================
 st.title("🤖 Multi-Model Loan Assessor")
 pdfs_missing = [f for f in REQUIRED_PDFS if not os.path.exists(f)]
 
 def update_metrics(placeholder):
     if 'execution_time' in st.session_state:
-        ai_time = st.session_state.execution_time
         col1, col2 = placeholder.columns(2)
-        col1.metric("Processing Time", f"{ai_time:.2f}s")
-        col2.metric("Efficiency", "High")
+        col1.metric("Processing Time", f"{st.session_state.execution_time:.2f}s")
+        col2.metric("Status", "Success")
 
 # --- SIDEBAR ---
 with st.sidebar:
     st.header("🔐 Authentication")
     
-    # Provider Selection
+    # Provider Select
     provider_option = st.radio("Select Model:", ["Groq (Llama-3)", "Google (Gemini)"])
     
-    # Init State
+    # State Init
     if 'auth_status' not in st.session_state:
         st.session_state['auth_status'] = False
         st.session_state['api_key'] = None
         st.session_state['provider'] = None
 
-    # Reset on Switch
+    # Reset on Change
     if st.session_state.get('provider') != provider_option:
         st.session_state['auth_status'] = False
         st.session_state['api_key'] = None
@@ -141,30 +143,26 @@ with st.sidebar:
                 st.error("⚠️ Enter a key.")
             else:
                 try:
-                    with st.spinner(f"Connecting to {provider_option}..."):
-                        # --- VALIDATION LOGIC ---
+                    with st.spinner(f"Verifying {provider_option}..."):
+                        
+                        # --- LIGHTWEIGHT VALIDATION (NO SPINNING) ---
                         if "Groq" in provider_option:
-                            test_llm = ChatGroq(api_key=api_key_input, model_name="llama-3.3-70b-versatile")
+                            ChatGroq(api_key=api_key_input).invoke("Hi")
                         else:
-                            # FORCE REST TRANSPORT TO FIX SPINNING
-                            test_llm = ChatGoogleGenerativeAI(
-                                google_api_key=api_key_input, 
-                                model="gemini-1.5-flash",
-                                transport="rest" # <--- THIS IS THE FIX
-                            )
-                        
-                        # Simple Invoke to test connection
-                        test_llm.invoke("Hello")
+                            # Use Direct SDK to avoid LangChain/Streamlit Thread Lock
+                            genai.configure(api_key=api_key_input)
+                            list(genai.list_models()) # Quick check that doesn't hang
                         
                         st.session_state['auth_status'] = True
                         st.session_state['api_key'] = api_key_input
-                        st.success("✅ Connected!")
+                        st.success("✅ Valid!")
                         time.sleep(0.5)
                         st.rerun()
+                        
                 except Exception as e:
                     st.error(f"❌ Error: {e}")
     else:
-        st.success(f"✅ {st.session_state['provider']} Ready")
+        st.success(f"✅ {st.session_state['provider']} Active")
         if st.button("🔴 Logout"):
             st.session_state['auth_status'] = False
             st.rerun()
@@ -173,22 +171,19 @@ with st.sidebar:
     if st.button("♻️ Rebuild Database"):
         if os.path.exists(INDEX_PATH): shutil.rmtree(INDEX_PATH)
         st.cache_resource.clear()
-        st.success("Reset Complete.")
-        time.sleep(1)
         st.rerun()
 
-    st.divider()
     if os.path.exists(DB_FILE) and not pdfs_missing:
         st.success("✅ System Ready")
     else:
         st.warning(f"⚠️ Missing: {pdfs_missing}")
-    
+
     st.header("Metrics")
     metrics_placeholder = st.empty()
     update_metrics(metrics_placeholder)
 
 # ==========================================
-# 6. MAIN APP LOGIC
+# 6. MAIN LOGIC
 # ==========================================
 if st.session_state.get('auth_status', False):
     
@@ -200,7 +195,7 @@ if st.session_state.get('auth_status', False):
     def setup_rag(_provider, _key):
         if pdfs_missing: st.stop()
         
-        # Use HuggingFace embeddings for stability across both models
+        # Always use HuggingFace Embeddings for compatibility
         embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
         if os.path.exists(INDEX_PATH):
@@ -214,18 +209,18 @@ if st.session_state.get('auth_status', False):
             vectorstore.save_local(INDEX_PATH)
             return vectorstore.as_retriever()
 
-    with st.spinner("Loading Knowledge Base..."):
+    with st.spinner("Loading AI..."):
         retriever = setup_rag(current_provider, current_key)
 
     # --- LLM SETUP ---
     if "Groq" in current_provider:
         llm = ChatGroq(api_key=current_key, temperature=0, model_name="llama-3.3-70b-versatile")
     else:
-        # FORCE REST TRANSPORT HERE TOO
+        # Use 'gemini-pro' and force 'rest' transport
         llm = ChatGoogleGenerativeAI(
             google_api_key=current_key, 
             temperature=0, 
-            model="gemini-pro",
+            model="gemini-pro", 
             transport="rest"
         )
 
@@ -262,7 +257,7 @@ if st.session_state.get('auth_status', False):
     with col2:
         if btn:
             query = f"Process Loan {uid}. "
-            if use_sim: query += f"SIMULATION: Use Score {sim_score}, Status {sim_status}. Only query Name/Nationality from DB."
+            if use_sim: query += f"SIMULATION: Use Score {sim_score}, Status {sim_status}. Only query Name from DB."
             else: query += "Query SQL for all data."
             query += " Check Policies. Output Final Report."
             
@@ -292,4 +287,4 @@ if st.session_state.get('auth_status', False):
                     st.text_area("Draft", value=email, height=200)
 
 elif not st.session_state.get('auth_status', False):
-    st.info("👈 Please Select Provider & Validate Key in Sidebar")
+    st.info("👈 Select Provider & Validate Key in Sidebar")