updated app.py file so improve the ui

app.py

@@ -1,21 +1,23 @@
-import uuid
 import os
-import gradio as gr
-import pandas as pd
+import uuid
+import re
+import json
+import sqlite3
+import random
 import torch
 import numpy as np
+import pandas as pd
+import gradio as gr
+from datetime import datetime
 from sentence_transformers import util
+from typing import Tuple
+
 import google.generativeai as genai
 import chromadb
 from langchain_chroma import Chroma
 import gspread
 from google.oauth2.service_account import Credentials
 from langgraph.checkpoint.sqlite import SqliteSaver
-import sqlite3
-import json
-from datetime import datetime
-import re
-from typing import Dict, List, Tuple
 
 # === Configuration ===
 genai.configure(api_key=os.environ["GEMINI_API_KEY"])
@@ -23,154 +25,102 @@ embedding_model = "models/embedding-001"
 llm_model_name = "models/gemma-3-4b-it"
 collection_name = "xeno_collection"
 
-# === Google Sheets Setup for Hugging Face ===
+# === Google Sheets Setup ===
 def get_google_sheets_credentials():
     credentials_json = os.environ.get("GOOGLE_SHEETS_CREDENTIALS")
     if not credentials_json:
         raise ValueError("GOOGLE_SHEETS_CREDENTIALS environment variable not set.")
     credentials_dict = json.loads(credentials_json)
-    scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
-    creds = Credentials.from_service_account_info(credentials_dict, scopes=scope)
-    return creds
+    scope = [
+        "https://spreadsheets.google.com/feeds",
+        "https://www.googleapis.com/auth/drive"
+    ]
+    return Credentials.from_service_account_info(credentials_dict, scopes=scope)
 
-# Authenticate with Google Sheets
 client_gspread = gspread.authorize(get_google_sheets_credentials())
-
-# Open the Google Sheet
 sheet = client_gspread.open("Response_Log").sheet1
 
 def log_response(question, answer, source_ids, knowledge_pairs, session_id):
-    """
-    Log a question, answer, source IDs, and knowledge base question-answer pairs to the Google Sheet.
-    
-    Args:
-        question (str): The question asked by the user.
-        answer (str): The answer provided by the model.
-        source_ids (str): Comma-separated list of source IDs used.
-        knowledge_pairs (list): List of tuples containing (question, answer) from the knowledge base.
-    """
     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    knowledge_question_1 = knowledge_pairs[0][0] if len(knowledge_pairs) > 0 else "N/A"
-    knowledge_answer_1 = knowledge_pairs[0][1] if len(knowledge_pairs) > 0 else "N/A"
-    knowledge_question_2 = knowledge_pairs[1][0] if len(knowledge_pairs) > 1 else "N/A"
-    knowledge_answer_2 = knowledge_pairs[1][1] if len(knowledge_pairs) > 1 else "N/A"
+    kq1, ka1 = knowledge_pairs[0] if len(knowledge_pairs) > 0 else ("N/A", "N/A")
+    kq2, ka2 = knowledge_pairs[1] if len(knowledge_pairs) > 1 else ("N/A", "N/A")
+
     row = [
-        timestamp,
-        session_id,
-        question,
-        answer,
-        source_ids,
-        knowledge_question_1,
-        knowledge_answer_1,
-        knowledge_question_2,
-        knowledge_answer_2
+        timestamp, session_id, question, answer, source_ids, kq1, ka1, kq2, ka2
     ]
     try:
         sheet.append_row(row)
-        print(f"Logged: {question} | Source IDs: {source_ids}")
     except Exception as e:
-        print(f"Failed to log to Google Sheet: {e}")
         with open("/tmp/response_log.txt", "a") as f:
-            f.write(f"{timestamp},{question},{answer},{source_ids},{knowledge_question_1},{knowledge_answer_1},{knowledge_question_2},{knowledge_answer_2}\n")
+            f.write(",".join(map(str, row)) + "\n")
 
-# === LangGraph Memory Setup ===
+# === Memory Setup ===
 conn = sqlite3.connect("xeno_memory.db", check_same_thread=False)
 memory = SqliteSaver(conn=conn)
 
 def update_memory(config, user_message, assistant_message):
-    full_checkpoint = memory.get(config) or {}
-    messages = full_checkpoint.get("channel_values", {}).get("messages", [])
-    
+    checkpoint = memory.get(config) or {}
+    messages = checkpoint.get("channel_values", {}).get("messages", [])
     messages.append({"role": "user", "content": user_message})
     messages.append({"role": "assistant", "content": assistant_message})
-    
-    checkpoint_to_save = {
+    memory.put(config, {
         "v": 1,
         "id": str(uuid.uuid4()),
         "ts": datetime.now().isoformat(),
         "channel_values": {"messages": messages},
         "channel_versions": {},
-        "versions_seen": {},   
-    }
-    
-    memory.put(config, checkpoint_to_save, {}, {})
-    
-# === Intent Classification System ===
+        "versions_seen": {}
+    }, {}, {})
+
+# === Intent Classifier ===
 class IntentClassifier:
     def __init__(self):
-        # Define intent patterns and responses
         self.intent_patterns = {
-            'greeting': {
-                'patterns': [
-                    r'\b(hi|hello|hey|good morning|good afternoon|good evening|greetings)\b',
-                    r'^(hi|hello|hey)[\s!.]*$',
-                    r'\b(how are you|how do you do)\b'
-                ],
-                'responses': [
-                    "Hello! I'm XENO Assistant. How can I help you with XENO financial services today?",
-                    "Hi there! I'm here to assist you with any questions about XENO services. What can I help you with?",
-                    "Good day! Welcome to XENO Support. How may I assist you today?"
+            "greeting": {
+                "patterns": [r"\b(hi|hello|hey|good morning|good afternoon|good evening)\b"],
+                "responses": [
+                    "Hello! I'm XENO Assistant. How can I help you today?",
+                    "Hi there! What can I assist you with?",
+                    "Good day! How may I assist you?"
                 ]
             },
-            'thanks': {
-                'patterns': [
-                    r'\b(thank you|thanks|thank u|thx|appreciate|grateful)\b',
-                    r'^(thanks|thank you)[\s!.]*$',
-                    r'\b(much appreciated|thanks a lot|thank you so much)\b'
-                ],
-                'responses': [
-                    "You're welcome! Is there anything else I can help you with regarding XENO services?",
-                    "Happy to help! Feel free to ask if you have any other questions about XENO.",
-                    "Glad I could assist you! Let me know if you need help with anything else."
+            "thanks": {
+                "patterns": [r"\b(thank you|thanks|appreciate)\b"],
+                "responses": [
+                    "You're welcome! Anything else I can help you with?",
+                    "Happy to help!",
+                    "Glad I could assist!"
                 ]
             },
-            'goodbye': {
-                'patterns': [
-                    r'\b(bye|goodbye|see you|farewell|take care|have a good day)\b',
-                    r'^(bye|goodbye)[\s!.]*$',
-                    r'\b(talk to you later|see you later|until next time)\b'
-                ],
-                'responses': [
-                    "Goodbye! Thank you for using XENO services. Have a great day!",
-                    "Take care! Feel free to return anytime you need help with XENO services.",
-                    "Have a wonderful day! Don't hesitate to reach out if you need assistance with XENO."
+            "goodbye": {
+                "patterns": [r"\b(bye|goodbye|see you)\b"],
+                "responses": [
+                    "Goodbye! Have a great day!",
+                    "Take care! Come back anytime.",
+                    "See you later!"
                 ]
             }
         }
-    
+
     def classify_intent(self, message: str) -> Tuple[str, str]:
-        """
-        Classify the intent of a message and return appropriate response if it's a simple intent.
-        Returns: (intent_name, response) - response is empty string if intent requires RAG
-        """
         message_lower = message.lower().strip()
-        
         for intent_name, intent_data in self.intent_patterns.items():
-            for pattern in intent_data['patterns']:
-                if re.search(pattern, message_lower, re.IGNORECASE):
-                    import random
-                    response = random.choice(intent_data['responses'])
-                    return intent_name, response
-        
-        return 'query', ''
-    
-    def is_simple_intent(self, intent: str) -> bool:
-        """Check if intent can be handled without RAG"""
-        simple_intents = ['greeting', 'thanks']
-        return intent in simple_intents
+            for pattern in intent_data["patterns"]:
+                if re.search(pattern, message_lower):
+                    return intent_name, random.choice(intent_data["responses"])
+        return "query", ""
 
-# Initialize intent classifier
 intent_classifier = IntentClassifier()
 
-# === Load and Clean Knowledge Base ===
+# === Load Knowledge Base ===
 df_kb = pd.read_json("XENO_Uganda_KnowledgeBase_Advisory.json")
-df_kb.dropna(subset=['Content'], inplace=True)
+df_kb.dropna(subset=["Content"], inplace=True)
 
 def prepare_documents(data):
-    documents, metadatas, ids = [], [], []
+    docs, metas, ids = [], [], []
     for item in data:
-        documents.append(f"Question: {item['Question']}\nAnswer: {item['Content']}")
-        metadatas.append({
+        docs.append(f"Question: {item['Question']}\nAnswer: {item['Content']}")
+        metas.append({
             "question": item["Question"],
             "content": item["Content"],
             "section": item.get("Section", ""),
@@ -180,150 +130,85 @@ def prepare_documents(data):
             "id": item["ID"]
         })
         ids.append(item["ID"])
-    return documents, metadatas, ids
+    return docs, metas, ids
 
-xeno_data_list = df_kb.to_dict('records')
-documents, metadatas, ids = prepare_documents(xeno_data_list)
+docs, metas, ids = prepare_documents(df_kb.to_dict("records"))
 
-# === Setup ChromaDB ===
+# === ChromaDB ===
+client = chromadb.PersistentClient(path="/tmp/xeno_db")
 try:
-    client = chromadb.PersistentClient(path="/tmp/xeno_db")
-    try:
-        collection = client.get_collection(name=collection_name)
-        print(f"Loaded existing ChromaDB collection: {collection_name}")
-    except:
-        print(f"Creating new ChromaDB collection: {collection_name}")
-        collection = client.create_collection(name=collection_name)
-        collection.add(documents=documents, metadatas=metadatas, ids=ids)
-except Exception as e:
-    print(f"Failed to initialize ChromaDB: {e}")
-    raise
+    collection = client.get_collection(name=collection_name)
+except:
+    collection = client.create_collection(name=collection_name)
+    collection.add(documents=docs, metadatas=metas, ids=ids)
 
-vector_store = Chroma(client=client, collection_name=collection_name)
-retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 4})
+retriever = Chroma(client=client, collection_name=collection_name) \
+    .as_retriever(search_type="similarity", search_kwargs={"k": 4})
 
 # === Prompt System ===
-SYSTEM_PROMPT = """You are a friendly XENO Support Assistant, an AI-powered helpful and professional customer service representative.
-Use only the information provided in the knowledge base context to answer user queries.
-Do not hallucinate. If context doesn't contain relevant info, say so in a calm polite manner by saying I'm sorry, I can't assist with that.
-Only use context that is clearly relevant to the user's question.
-For greetings like “hi” or “hello”, respond politely without using the context.
-remember previous conversations."""
+SYSTEM_PROMPT = """You are a friendly XENO Support Assistant.
+Use only the knowledge base context to answer questions.
+Do not hallucinate. If no relevant info, politely decline.
+Remember previous conversations."""
 
-# === Context Processing ===
+# === Context Processor ===
 def process_context(results, cosine_scores, max_results=2):
-    sorted_indices = np.argsort(cosine_scores)[::-1][:max_results]
-    formatted_context = ""
-    source_ids = []
-    knowledge_pairs = []
-    for i, idx in enumerate(sorted_indices, 1):
-        result = results[idx]
-        score = cosine_scores[idx]
-        question = result.metadata.get('question', 'N/A')
-        answer = result.metadata.get('content', 'N/A')
-        formatted_context += f"Knowledge Entry {i}:\n"
-        formatted_context += f"Q: {question}\n"
-        formatted_context += f"A: {answer}\n"
-        formatted_context += "-" * 40 + "\n"
-        source_ids.append(result.metadata.get('id', 'N/A'))
-        knowledge_pairs.append((question, answer))
-    return formatted_context, source_ids, knowledge_pairs
-
-# === LLM Generation (Refactored) ===
-def generate_xeno_response(context, question, chat_history):
-    """Generates a response but does NOT handle memory."""
+    sorted_idx = np.argsort(cosine_scores)[::-1][:max_results]
+    ctx, src_ids, kpairs = "", [], []
+    for i, idx in enumerate(sorted_idx, 1):
+        q = results[idx].metadata.get("question", "N/A")
+        a = results[idx].metadata.get("content", "N/A")
+        ctx += f"Knowledge Entry {i}:\nQ: {q}\nA: {a}\n" + "-" * 40 + "\n"
+        src_ids.append(results[idx].metadata.get("id", "N/A"))
+        kpairs.append((q, a))
+    return ctx, src_ids, kpairs
+
+# === LLM Generation ===
+def generate_xeno_response(context, question, history):
     model = genai.GenerativeModel(llm_model_name)
-    formatted_history = "\n".join(
-        [f"{msg['role'].capitalize()}: {msg['content']}" for msg in chat_history]
-    ) if chat_history else "None"
-    
-    prompt = f"{SYSTEM_PROMPT}\n### HISTORY ###\n{formatted_history}\n### CONTEXT ###\n{context}\n### QUESTION ###\n{question}"
-    
-    response = model.generate_content(prompt)
-    return response.text.strip()
-
-
-# === Main Interface Logic (Refactored) ===
-def get_context_and_answer(message, history, session_id="default"):
-    """
-    Handles intent classification, RAG, and memory updates in one place.
-    """
-    config = {"configurable": {"thread_id": str(session_id), "checkpoint_ns": ""}}
-    
-    full_checkpoint = memory.get(config) or {}
-    chat_history = full_checkpoint.get("channel_values", {}).get("messages", [])
-    intent, direct_response = intent_classifier.classify_intent(message)
-    
-    answer = ""
-    source_ids = "N/A"
-    knowledge_pairs = []
-
-    if intent != 'query':
-        answer = direct_response
-    else: 
-        if len(message.strip()) < 3:
-            answer = "I'd be happy to help! Could you please provide more details about what you'd like to know?"
-        else:
-            try:
-                queried_results = retriever.invoke(message)
-                query_embedding = genai.embed_content(model=embedding_model, content=message, task_type="retrieval_query")['embedding']
-                
-                doc_embeddings = [genai.embed_content(model=embedding_model, content=doc.page_content, task_type="retrieval_document")['embedding'] for doc in queried_results]
-                
-                cosine_scores = util.cos_sim(torch.tensor(query_embedding).float(), torch.tensor(doc_embeddings).float())[0].tolist()
-
-                if max(cosine_scores) < 0.4:
-                    answer = "I'm sorry, I couldn't find specific information for your question. Could you try rephrasing it, or contact XENO support directly?"
-                else:
-                    context, source_ids_list, knowledge_pairs = process_context(queried_results, cosine_scores)
-                    answer = generate_xeno_response(context, message, chat_history)
-                    source_ids = ", ".join(source_ids_list)
-
-            except Exception as e:
-                print(f"Error during RAG processing: {e}")
-                answer = "I apologize, but I'm having a technical issue. Please try again shortly or contact XENO support."
+    hist_text = "\n".join([f"{m['role'].capitalize()}: {m['content']}" for m in history]) if history else "None"
+    prompt = f"{SYSTEM_PROMPT}\n### HISTORY ###\n{hist_text}\n### CONTEXT ###\n{context}\n### QUESTION ###\n{question}"
+    return model.generate_content(prompt).text.strip()
+
+# === Chat Handler ===
+def chat_handler(message, history):
+    session_id = "default"  # could be made dynamic if needed
+    config = {"configurable": {"thread_id": session_id, "checkpoint_ns": ""}}
+    checkpoint = memory.get(config) or {}
+    chat_history = checkpoint.get("channel_values", {}).get("messages", [])
+
+    intent, quick_reply = intent_classifier.classify_intent(message)
+    answer, src_ids, kpairs = "", "N/A", []
+
+    if intent != "query":
+        answer = quick_reply
+    else:
+        try:
+            results = retriever.invoke(message)
+            query_emb = genai.embed_content(model=embedding_model, content=message, task_type="retrieval_query")['embedding']
+            doc_embs = [genai.embed_content(model=embedding_model, content=doc.page_content, task_type="retrieval_document")['embedding'] for doc in results]
+            cos_scores = util.cos_sim(torch.tensor(query_emb).float(), torch.tensor(doc_embs).float())[0].tolist()
+
+            if max(cos_scores) < 0.4:
+                answer = "I'm sorry, I couldn't find specific information for your question."
+            else:
+                ctx, src_ids_list, kpairs = process_context(results, cos_scores)
+                answer = generate_xeno_response(ctx, message, chat_history)
+                src_ids = ", ".join(src_ids_list)
+        except Exception as e:
+            answer = "I’m having a technical issue. Please try again later."
 
     update_memory(config, message, answer)
-    log_response(message, answer, source_ids, knowledge_pairs, session_id)
-    
+    log_response(message, answer, src_ids, kpairs, session_id)
     return answer
 
-# === Enhanced Gradio UI ===
-def respond(message, history, session_id):
-    """Gradio's main response function."""
-    if not session_id:
-        session_id = str(uuid.uuid4())
-    
-    response = get_context_and_answer(message, history, session_id)
-    
-    config = {"configurable": {"thread_id": str(session_id), "checkpoint_ns": ""}}
-    updated_messages = (memory.get(config) or {}).get("messages", [])
-    
-    history.append({"role": "user", "content": message})
-    history.append({"role": "assistant", "content": response})
-    
-    return "", history
-def create_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("""ASKXENO 
-                    
-            **Welcome to XENO AI Support!** 
-            I can help you with questions about XENO financial services including:
-            • Account management and setup
-            • Transaction processes and fees  
-            • Platform features and troubleshooting
-            • General service information
-            *Simply type your question below to get started!*
-                    """)
-        
-        session_id_box = gr.Textbox(label="Session ID", value=str(uuid.uuid4()), interactive=True)
-        
-        chatbot = gr.Chatbot(label="XENO Assistant", bubble_full_width=False, height=500, type="messages")
-        msg = gr.Textbox(label="Your Message", placeholder="Type your question here...")
-        
-        msg.submit(respond, [msg, chatbot, session_id_box], [msg, chatbot])
-    return demo
+# === Clean ChatInterface UI ===
+iface = gr.ChatInterface(
+    fn=chat_handler,
+    title="ASKXENO",
+    description="Ask anything about XENO's financial services.",
+    theme="soft"
+)
 
 if __name__ == "__main__":
-    iface = create_interface()
-    iface.launch(share=False, server_name="0.0.0.0", server_port=7860)
+    iface.launch(share=False, server_name="0.0.0.0", server_port=7860)