Update app.py

fix list error

app.py

@@ -6,6 +6,7 @@ import time
 import sqlite3
 import shutil
 import asyncio
+import re
 
 # ==========================================
 # 0. ASYNC FIX
@@ -16,39 +17,33 @@ except RuntimeError:
     asyncio.set_event_loop(asyncio.new_event_loop())
 
 # ==========================================
-# 1. PAGE CONFIG
+# 1. CONFIG & IMPORTS
 # ==========================================
 st.set_page_config(page_title="Bank Loan Agent", layout="wide")
 warnings.filterwarnings("ignore")
 
-# ==========================================
-# 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:
     from langchain_groq import ChatGroq
-    from langchain_google_genai import ChatGoogleGenerativeAI, HarmBlockThreshold, HarmCategory
+    from langchain_google_genai import ChatGoogleGenerativeAI
     import google.generativeai as genai
-    
     from langchain_huggingface import HuggingFaceEmbeddings
     from langchain_community.vectorstores import FAISS
-    from langchain_community.callbacks import StreamlitCallbackHandler
     from langchain_community.document_loaders import PyPDFLoader
     from langchain_text_splitters import CharacterTextSplitter
     from langchain_core.prompts import PromptTemplate
     from langchain_core.runnables import RunnablePassthrough
     from langchain_core.output_parsers import StrOutputParser
     from langchain_core.tools import tool
-    from langchain.agents import AgentExecutor, create_react_agent
 except ImportError as e:
     st.error(f"❌ Import Error: {e}")
     st.stop()
 
 # ==========================================
-# 3. DATABASE SETUP
+# 2. DATABASE & TOOLS SETUP
 # ==========================================
 def init_db():
     if os.path.exists(DB_FILE): return
@@ -73,28 +68,23 @@ def run_query(query, params=()):
             return cursor.fetchone()
     except Exception as e: return f"DB Error: {e}"
 
-# ==========================================
-# 4. TOOLS
-# ==========================================
-@tool
-def get_credit_score(user_id: str) -> str:
-    """Queries SQL DB for Credit Score. Input is ID string."""
+# --- TOOL FUNCTIONS (Pure Python) ---
+def tool_get_credit_score(user_id):
+    """Input: User ID. Returns Credit Score."""
     clean_id = ''.join(filter(str.isdigit, str(user_id)))
     row = run_query("SELECT Credit_Score FROM credit_score WHERE ID = ?", (clean_id,))
     return f"Credit Score: {row[0]}" if (row and not isinstance(row, str)) else "User ID not found."
 
-@tool
-def get_account_status(user_id: str) -> str:
-    """Queries SQL DB for Name, Nationality, Status, and Email. Input is ID string."""
+def tool_get_account_status(user_id):
+    """Input: User ID. Returns Name, Nationality, Status."""
     clean_id = ''.join(filter(str.isdigit, str(user_id)))
     row = run_query("SELECT Name, Nationality, Account_Status, Email FROM account_status WHERE ID = ?", (clean_id,))
     if row and not isinstance(row, str):
         return f"Customer Name: {row[0]}, Nationality: {row[1]}, Status: {row[2]}, Email: {row[3]}"
     return "User ID not found."
 
-@tool
-def check_pr_status(user_id: str) -> str:
-    """Queries SQL DB for PR Status. Input is ID string."""
+def tool_check_pr_status(user_id):
+    """Input: User ID. Returns PR Status."""
     clean_id = ''.join(filter(str.isdigit, str(user_id)))
     row = run_query("SELECT PR_Status FROM pr_status WHERE ID = ?", (clean_id,))
     if not row or (isinstance(row, str) and "no such column" in row.lower()):
@@ -102,170 +92,156 @@ 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
+# 3. MANUAL AGENT ENGINE (The Fix)
+# ==========================================
+class ManualReActAgent:
+    def __init__(self, llm, tools_map, rag_chain):
+        self.llm = llm
+        self.tools = tools_map
+        self.rag_chain = rag_chain
+        self.max_steps = 6
+
+    def run(self, query):
+        """Runs the ReAct loop manually to avoid Library Parsing Errors."""
+        
+        # 1. DEFINE PROMPT
+        tool_desc = "\n".join([f"- {name}: {func.__doc__}" for name, func in self.tools.items()])
+        system_prompt = f"""You are a Loan Risk Officer. Answer the question using the tools below.
+
+TOOLS:
+{tool_desc}
+- consult_policy_doc: Consult policy PDF for risk rules. Input: a question string.
+
+FORMAT:
+Thought: <reasoning>
+Action: <tool_name>
+Action Input: <input>
+Observation: <result>
+... (repeat)
+Final Answer: <answer>
+
+Begin!
+Question: {query}
+"""
+        history = system_prompt
+        logs = []
+
+        # 2. LOOP
+        for i in range(self.max_steps):
+            # A. Call LLM
+            response = self.llm.invoke(history).content
+            history += response + "\n"
+            
+            # B. Parse "Action"
+            action_match = re.search(r"Action:\s*(.+)", response)
+            input_match = re.search(r"Action Input:\s*(.+)", response)
+            
+            # C. Check for Final Answer (Stop Condition)
+            if "Final Answer:" in response:
+                final_ans = response.split("Final Answer:")[-1].strip()
+                return final_ans, logs
+
+            # D. Execute Tool
+            if action_match and input_match:
+                tool_name = action_match.group(1).strip()
+                tool_input = input_match.group(1).strip()
+                
+                # Strip quotes if present
+                tool_input = tool_input.strip('"').strip("'")
+                
+                logs.append((tool_name, tool_input))
+                
+                # Execute
+                observation = f"Error: Tool {tool_name} not found."
+                if tool_name in self.tools:
+                    try:
+                        observation = self.tools[tool_name](tool_input)
+                    except Exception as e:
+                        observation = f"Tool Error: {e}"
+                elif tool_name == "consult_policy_doc":
+                    try:
+                        observation = self.rag_chain.invoke(tool_input)
+                    except Exception as e:
+                        observation = f"RAG Error: {e}"
+                
+                obs_str = f"Observation: {observation}\n"
+                history += obs_str
+            else:
+                # If LLM didn't output an action but didn't finish, force it
+                if i == self.max_steps - 1:
+                    return response, logs
+                history += "Observation: Please continue. If you have the answer, say 'Final Answer:'.\n"
+
+        return "Agent timed out.", logs
+
+# ==========================================
+# 4. UI & SETUP
 # ==========================================
 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:
-        col1, col2 = placeholder.columns(2)
-        col1.metric("Processing Time", f"{st.session_state.execution_time:.2f}s")
-        col2.metric("Status", "Success")
-
 with st.sidebar:
     st.header("🔐 Authentication")
-    provider_option = st.radio("Select Model:", ["Groq (Llama-3)", "Google (Gemini)"])
+    provider = st.radio("Model:", ["Groq (Llama-3)", "Google (Gemini)"])
     
-    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
-
-    if st.session_state.get('provider') != provider_option:
-        st.session_state['auth_status'] = False
-        st.session_state['api_key'] = None
-        st.session_state['provider'] = provider_option
-
-    if not st.session_state['auth_status']:
-        api_key_input = st.text_input(f"Enter {provider_option} API Key", type="password")
-        if st.button("Validate Key"):
-            if not api_key_input:
-                st.error("⚠️ Enter a key.")
-            else:
-                try:
-                    with st.spinner(f"Verifying {provider_option}..."):
-                        if "Groq" in provider_option:
-                            ChatGroq(api_key=api_key_input).invoke("Hi")
-                        else:
-                            genai.configure(api_key=api_key_input)
-                            list(genai.list_models()) 
-                        
-                        st.session_state['auth_status'] = True
-                        st.session_state['api_key'] = api_key_input
-                        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']} Active")
-        if st.button("🔴 Logout"):
-            st.session_state['auth_status'] = False
-            st.rerun()
+    if 'api_key' not in st.session_state: st.session_state.api_key = None
+    
+    key_input = st.text_input("API Key", type="password")
+    if st.button("Set Key"):
+        st.session_state.api_key = key_input
+        st.success("Key Set!")
+        st.rerun()
 
-    st.divider()
-    if st.button("♻️ Rebuild Database"):
+    if st.button("♻️ Reset"):
         if os.path.exists(INDEX_PATH): shutil.rmtree(INDEX_PATH)
         st.cache_resource.clear()
         st.rerun()
 
-    if os.path.exists(DB_FILE) and not pdfs_missing:
-        st.success("✅ System Ready")
-    else:
-        st.warning(f"⚠️ Missing: {pdfs_missing}")
-    update_metrics(st.empty())
-
-# ==========================================
-# 6. MAIN LOGIC
-# ==========================================
-if st.session_state.get('auth_status', False):
-    
-    current_key = st.session_state['api_key']
-    current_provider = st.session_state['provider']
-
+if st.session_state.api_key:
+    # --- RAG SETUP ---
     @st.cache_resource
-    def setup_rag(_provider, _key):
-        if pdfs_missing: st.stop()
+    def setup_rag():
+        if pdfs_missing: return None
         embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-
         if os.path.exists(INDEX_PATH):
             return FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True).as_retriever()
-        else:
-            documents = []
-            for pdf_file in REQUIRED_PDFS:
-                documents.extend(PyPDFLoader(pdf_file).load())
-            splits = CharacterTextSplitter(chunk_size=600, chunk_overlap=50).split_documents(documents)
-            vectorstore = FAISS.from_documents(splits, embeddings)
-            vectorstore.save_local(INDEX_PATH)
-            return vectorstore.as_retriever()
+        documents = []
+        for f in REQUIRED_PDFS: documents.extend(PyPDFLoader(f).load())
+        splits = CharacterTextSplitter(chunk_size=600, chunk_overlap=50).split_documents(documents)
+        vectorstore = FAISS.from_documents(splits, embeddings)
+        vectorstore.save_local(INDEX_PATH)
+        return vectorstore.as_retriever()
 
     with st.spinner("Loading AI..."):
-        retriever = setup_rag(current_provider, current_key)
+        retriever = setup_rag()
 
-    # --- LLM SELECTION ---
-    if "Groq" in current_provider:
-        llm = ChatGroq(
-            api_key=current_key, 
-            temperature=0, 
-            model_name="llama-3.3-70b-versatile"
-        )
+    # --- LLM SETUP ---
+    if "Groq" in provider:
+        llm = ChatGroq(api_key=st.session_state.api_key, temperature=0, model_name="llama-3.3-70b-versatile")
     else:
-        # CRITICAL FIX: DISABLE SAFETY FILTERS
-        # This prevents Gemini from returning an empty list or error when it gets confused
-        safety = {
-            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
-        }
-        
+        # Using Gemini 1.5 Flash with REST transport
         llm = ChatGoogleGenerativeAI(
-            google_api_key=current_key, 
+            google_api_key=st.session_state.api_key, 
             temperature=0, 
             model="gemini-1.5-flash",
-            transport="rest",
-            safety_settings=safety # <--- APPLY FIX
+            transport="rest"
         )
 
     # --- RAG CHAIN ---
     rag_chain = (
         {"context": retriever | (lambda d: "\n".join([x.page_content for x in d])), "question": RunnablePassthrough()}
-        | PromptTemplate.from_template("Answer based on context:\n{context}\nQuestion: {question}") 
+        | PromptTemplate.from_template("Info: {context}\nQ: {question}\nA:") 
         | llm 
         | StrOutputParser()
     )
-    
-    @tool
-    def consult_policy_doc(query: str) -> str:
-        """Consults Policy Documents. Input should be a question string."""
-        return rag_chain.invoke(query)
-
-    tools = [get_credit_score, get_account_status, check_pr_status, consult_policy_doc]
-    
-    # --- REACT PROMPT ---
-    template = '''Answer the following questions as best you can. You have access to the following tools:
-
-{tools}
-
-Use the following format:
 
-Question: the input question you must answer
-Thought: you should always think about what to do
-Action: the action to take, should be one of [{tool_names}]
-Action Input: the input to the action
-Observation: the result of the action
-... (this Thought/Action/Action Input/Observation can repeat N times)
-Thought: I now know the final answer
-Final Answer: the final answer to the original input question
-
-Begin!
-
-Question: {input}
-Thought:{agent_scratchpad}'''
-
-    prompt = PromptTemplate.from_template(template)
-    
-    # --- AGENT CREATION ---
-    agent = create_react_agent(llm, tools, prompt)
-    
-    agent_executor = AgentExecutor(
-        agent=agent,
-        tools=tools, 
-        verbose=True, 
-        return_intermediate_steps=True,
-        handle_parsing_errors=True # Auto-fix formatting errors
-    )
+    # --- AGENT INSTANCE ---
+    tools_map = {
+        "get_credit_score": tool_get_credit_score,
+        "get_account_status": tool_get_account_status,
+        "check_pr_status": tool_check_pr_status
+    }
+    agent = ManualReActAgent(llm, tools_map, rag_chain)
 
     # --- UI ---
     col1, col2 = st.columns([1, 2])
@@ -278,43 +254,32 @@ Thought:{agent_scratchpad}'''
 
     with col2:
         if btn:
-            query = f"Process Loan for Customer ID: {uid}. "
-            if use_sim: 
-                query += f"SIMULATION ACTIVE. Use Score {sim_score} and Status '{sim_status}'. Do NOT query credit/status tools. Only query Name."
-            else: 
-                query += "Query SQL tools for Name, Email, Nationality, Status, Score."
-            
-            query += " Check Policies using 'consult_policy_doc'. Output a Final Report Table."
-            
-            with st.status(f"🤖 Agent ({current_provider}) Working...", expanded=True) as status:
-                st_callback = StreamlitCallbackHandler(st.container())
+            query = f"Process Loan for ID {uid}. "
+            if use_sim: query += f"SIMULATION: Score {sim_score}, Status '{sim_status}'. Do NOT query DB for score/status."
+            else: query += "Query DB for all info."
+            query += " Check policies. Report Risk, Rate, and Decision."
+
+            with st.status(f"🤖 {provider} Agent Running...", expanded=True):
+                st.write("Thinking...")
                 try:
-                    start_time = time.time()
-                    res = agent_executor.invoke({"input": query}, {"callbacks": [st_callback]})
-                    st.session_state.execution_time = time.time() - start_time
-                    update_metrics(metrics_placeholder)
-                    status.update(label="✅ Done", state="complete", expanded=False)
+                    # Run Manual Loop
+                    final_res, logs = agent.run(query)
+                    st.write("✅ Done!")
                 except Exception as e:
-                    st.error(f"Agent Error: {e}")
-                    st.stop()
+                    st.error(f"Error: {e}")
+                    final_res = "Failed."
+                    logs = []
 
-            st.success("### 📋 Final Report")
-            final_output = res.get('output', "Error generating report.")
-            st.markdown(final_output)
+            st.success("### 📋 Report")
+            st.markdown(final_res)
             
             with st.expander("Trace"):
-                steps = res.get("intermediate_steps", [])
-                for action, obs in steps:
-                    st.markdown(f"**Tool:** `{action.tool}`\n**Result:** `{obs}`")
-
+                for tool_name, tool_in in logs:
+                    st.markdown(f"**Tool:** `{tool_name}` | **Input:** `{tool_in}`")
+            
             if not use_sim:
                 st.divider()
-                with st.expander("✉️ Email Draft"):
-                    try:
-                        email = llm.invoke(f"Draft email for: {final_output}").content
-                        st.text_area("Draft", value=email, height=200)
-                    except:
-                        pass
+                st.text_area("✉️ Email Draft", value=llm.invoke(f"Draft email for: {final_res}").content)
 
-elif not st.session_state.get('auth_status', False):
-    st.info("👈 Select Provider & Validate Key in Sidebar")
+else:
+    st.info("👈 Enter API Key")