modified frontend

src/backend/api_routes/chat_history_db_api.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter,HTTPException,status
+from fastapi import APIRouter,HTTPException,status,Query
 from services.schemas import ChatHistoryRequest
 from services import supabase_service
 from utils import logger
@@ -18,9 +18,9 @@ def store_chat_history(chat_history : ChatHistoryRequest):
     
 @router.get('/get-history')
 def get_chat_history(conversation_id: str):
+    """Retrieves chat history from Supabase for a given conversation ID."""
     try:
         chat_history = supabase_service.get_chat_history(conversation_id)
-        logger.info(f"Chat history retrieved successfully for conversation ID: {conversation_id}")
         return chat_history
     except Exception as e:
         logger.error(f"Error retrieving chat history for ID {conversation_id}: {e}")
@@ -29,7 +29,6 @@ def get_chat_history(conversation_id: str):
             detail="Failed to retrieve chat history. Please try again later."
         )
 
-
     
 
 

src/backend/api_routes/knowledge_base_api.py

@@ -1,7 +1,10 @@
 from fastapi import APIRouter,HTTPException
-from data import pinecone_db
-from models.schemas import UpsertRequest,DeleteRequest,MetadataRequest
+from services import pinecone_service,embedding_service
+from services.schemas import UpsertRequest,DeleteRequest,MetadataRequest
 import pandas as pd
+from utils import logger
+
+logger = logger.get_logger()
 
 router = APIRouter(prefix="/knowledge-base", tags=['Knowledge Base Operations'])
 
@@ -17,7 +20,7 @@ def upsert_data(request: UpsertRequest):
     """
     try:
         df = pd.DataFrame(request.data)
-        pinecone_db.upsert_vector_data(df)
+        pinecone_service.upsert_vector_data(df)
         return {"message": "Data upserted successfully."}
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to upsert data: {e}")
@@ -30,7 +33,7 @@ def delete_records(request: DeleteRequest):
 
     """
     try:
-        pinecone_db.delete_records_by_ids(request.ids_to_delete)
+        pinecone_service.delete_records_by_ids(request.ids_to_delete)
         return {"message": "Records deleted successfully."}
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to delete records: {e}")
@@ -45,7 +48,11 @@ def fetch_metadata(request: MetadataRequest):
         "score_threshold": 0.47}
     """
     try:
-        metadata = pinecone_db.retrieve_relevant_metadata(request.prompt, request.n_result, request.score_threshold)
+        prompt = request.prompt
+        logger.info(f"Given prompt : {prompt}")
+        # prompt = prompt[-1] if isinstance(prompt, list) else prompt
+        embedding = embedding_service.get_text_embedding(prompt)
+        metadata = pinecone_service.retrieve_relevant_metadata(embedding, prompt, request.n_result, request.score_threshold)
         return {"metadata": metadata}
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to fetch metadata: {e}")

src/backend/main.py

@@ -1,8 +1,8 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from api_routes.chat_api import router as chat_router
-# from api_routes.knowledge_base_api import router as knowledge_base_router
-# from api_routes.chat_history_db_api import router as chat_history_router
+from api_routes.knowledge_base_api import router as knowledge_base_router
+from api_routes.chat_history_db_api import router as chat_history_router
 
 app = FastAPI(
     title="Yuvabe Care Companion AI",
@@ -19,12 +19,12 @@ app.add_middleware(
 
 @app.get("/", tags=["Root"])
 def read_root():
-    return {"message": "Hello World"}
+    return {"message": "Yuvabe Care Companion AI"}
 
 # Register Routes
 app.include_router(chat_router)
-# app.include_router(knowledge_base_router)
-# app.include_router(chat_history_router)
+app.include_router(knowledge_base_router)
+app.include_router(chat_history_router)
 
 # Health Check Endpoint
 @app.get("/health", tags=["Health Check"])

src/backend/services/llm_model_service.py

@@ -19,11 +19,15 @@ client = Groq(api_key=GROQ_API_KEY)
 
 # System prompt structure s
 SYSTEM_PROMPT: List[Dict[str, str]] = [
-    {"role": "system", "content": "You are Yuvabe Care Companion AI, an advanced healthcare assistant..."},
-    {"role": "system", "content": "Your knowledge is up-to-date with the latest medical guidelines as of July 2024..."},
-    {"role": "system", "content": "Always provide accurate, empathetic, and responsible responses..."},
-    {"role": "system", "content": "If the user asks non-healthcare questions, politely decline..."},
-    {"role": "system", "content": "You were created by Velu R, an AI model developer."}
+    {
+        "role": "system",
+        "content": (
+            "You are Yuvabe Care Companion AI, an advanced healthcare assistant with up-to-date knowledge "
+            "based on the latest medical guidelines as of July 2024. Always provide accurate, empathetic, "
+            "and responsible responses. If the user asks non-healthcare questions, politely decline. "
+            "You were created by Velu R, an AI model developer."
+        )
+    }
 ]
 
 # Constants for token limits and configurations
@@ -70,15 +74,23 @@ def build_prompt(
     conversation_history = truncate_conversation_history(conversation_history)
 
     if db_response and "No relevant information found" not in db_response:
-        return SYSTEM_PROMPT + [
-            {"role": "system", "content": f"Relevant Context: {db_response}"},
-            {"role": "user", "content": user_query}
-        ] + conversation_history
-    else:
-        return SYSTEM_PROMPT + [
-            {"role": "system", "content": "Please respond using your internal medical knowledge."},
-            {"role": "user", "content": user_query}
-        ] + conversation_history
+        query_response = (
+            f"User query: {user_query}\n"
+            f"Database response: {db_response}\n"
+            "Please provide a detailed response based on the above information."
+        )
+        return SYSTEM_PROMPT + conversation_history + [
+            {"role": "user", "content": query_response}
+        ]
+
+    backup_response = (
+        "I'm unable to find relevant data from the database. "
+        "Please respond based on your expertise and available information."
+    )
+    return SYSTEM_PROMPT + conversation_history + [
+        {"role": "system", "content": backup_response},
+        {"role": "user", "content": user_query}
+    ]
 
 def get_health_advice(
     user_query: str,

src/backend/services/pinecone_service.py

@@ -92,6 +92,8 @@ def initialize_pinecone_index(pinecone, index_name, dimension=384, metric="cosin
     except Exception as e:
         logger.error(f"Error occurred while getting or creating the Pinecone index: {str(e)}", exc_info=True)
         return None
+
+index = initialize_pinecone_index(PINECONE, INDEX_NAME)
     
 def delete_records_by_ids(ids_to_delete):
     """
@@ -120,22 +122,17 @@ def delete_records_by_ids(ids_to_delete):
         - Deletion occurs within the specified `NAMESPACE`.
     """
     try:
-        index = initialize_pinecone_index(PINECONE,INDEX_NAME)
         index.delete(ids=ids_to_delete, namespace=NAMESPACE)
         logger.info("IDs deleted successfully.")
     except Exception as e:
         return f"Failed to delete the IDs: {e}"
+    
 
-def retrieve_relevant_metadata(prompt, n_result=3, score_threshold=0.47):
+def retrieve_relevant_metadata(embedding, prompt, n_result=3, score_threshold=0.47):
     """
     Retrieves and reranks relevant context data based on a given prompt.
     """
     try:
-        index = initialize_pinecone_index(PINECONE, INDEX_NAME)
-        prompt = prompt[-1] if isinstance(prompt, list) else prompt
-
-        # Generate embedding for the provided prompt
-        embedding = get_text_embedding(prompt)
         response = index.query(
             top_k=n_result,
             vector=embedding,
@@ -149,33 +146,31 @@ def retrieve_relevant_metadata(prompt, n_result=3, score_threshold=0.47):
                 "question": entry.get('metadata', {}).get('question', 'N/A'),
                 "answer": entry.get('metadata', {}).get('answer', 'N/A'),
                 "instruction": entry.get('metadata', {}).get('instruction', 'N/A'),
-                "score": f"{entry.get('score', 0)}",
-                "id": f"{entry.get('id', 'N/A')}"
+                "score": str(entry.get('score', 0)),
+                "id": str(entry.get('id', 'N/A'))
             }
             for entry in response.get('matches', [])
-            if entry.get('score', 0) >= score_threshold
+            if float(entry.get('score', 0)) >= score_threshold
         ]
 
-        # Rerank the filtered results using a reranker model
-        if filtered_results:
-            pairs = [(prompt, item["question"]) for item in filtered_results]
-            scores = reranker.predict(pairs)  # Predict relevance scores
+        logger.info(f"Retrieved filtered data: {filtered_results}")
+        return filtered_results if filtered_results else [{"response": "No relevant data found."}]
 
-            # Attach reranker scores and sort by relevance
-            for item, score in zip(filtered_results, scores):
-                item["reranker_score"] = score
+        # # Rerank the filtered results using a reranker model
+        # if filtered_results:
+        #     pairs = [(prompt, item["question"]) for item in filtered_results]
+        #     scores = reranker.predict(pairs)  # Predict relevance scores
 
-            filtered_results = sorted(
-                filtered_results, 
-                key=lambda x: x["reranker_score"], 
-                reverse=True
-            )
+        #     # Attach reranker scores and sort by relevance
+        #     for item, score in zip(filtered_results, scores):
+        #         item["reranker_score"] = score
 
-        # Return metadata or fallback message
-        return filtered_results if filtered_results else [{"response": "No relevant data found."}]
+        #     filtered_results.sort(key=lambda x: x["reranker_score"], reverse=True)
+
+        # return filtered_results if filtered_results else [{"response": "No relevant data found."}]
 
     except Exception as e:
-        logger.error(f"Failed to fetch context for '{prompt[:20]}'. Error: {e}")
+        logger.error(f"Failed to fetch context for prompt: '{prompt}'. Error: {e}")
         return [{"response": "Failed to fetch data due to an error."}]
 
 

src/backend/services/supabase_service.py

@@ -59,7 +59,6 @@ def get_chat_history(conversation_id):
         
         if existing_data:
             chat_data = json.loads(existing_data.decode('utf-8'))
-            logger.info("Chat history retrieved successfully!")
             return chat_data
         else:
             logger.warning("No chat history found for the given conversation ID.")

src/frontend/app/common_functions.py

@@ -5,6 +5,10 @@ from dotenv import load_dotenv
 from utils import logger
 import json
 import time
+from datetime import datetime
+from typing import Dict, Any
+import streamlit as st
+
 
 load_dotenv()
 logger = logger.get_logger()
@@ -16,21 +20,23 @@ ABOUT_US = "An AI-powered assistant for personalized healthcare guidance."
 
 API_URL = os.getenv("API_URL", "http://localhost:8000")
 
-def config_homepage(st, page_title=PAGE_TITLE):
-    st.set_page_config(
-    page_title=PAGE_TITLE,
-    page_icon= PAGE_ICON,
-    layout=PAGE_LAYOUT,
-    initial_sidebar_state="auto",
-    menu_items={"Get help":GITHUB_LINK,
+def config_homepage(page_title=PAGE_TITLE):
+    if not hasattr(st, "_page_config_set"):
+        st.set_page_config(
+            page_title=PAGE_TITLE,
+            page_icon=PAGE_ICON,
+            layout=PAGE_LAYOUT,
+            initial_sidebar_state="collapsed",
+            menu_items={
+                "Get help": GITHUB_LINK,
                 "Report a bug": GITHUB_LINK,
-                "About": ABOUT_US}
-    )
-    logger.info(f"Page successfully configured with title: {PAGE_TITLE}")
+                "About": ABOUT_US
+            }
+        )
 
-def set_page_title(st, page_title=PAGE_TITLE):
+def set_page_title(page_title=PAGE_TITLE):
     st.markdown(f"""
-        <h1 style="color: darkblue; text-align: left; font-size: 50px;">
+        <h1 style="color: white; text-align: left; font-size: 42px;">
         <i>{PAGE_TITLE} 🏥⚕️🤖</i>
         </h1>
         """, unsafe_allow_html=True
@@ -45,7 +51,7 @@ def img_to_base64(image_path):
         logger.error(f"Error converting image to base64: {str(e)}")
         return None
     
-def typewriter_effect(st, text, speed=0.01):
+def typewriter_effect(text, speed=0.01):
     """Displays text with a realistic typewriter effect (character by character)."""
     placeholder = st.empty()
     displayed_text = ""
@@ -142,30 +148,131 @@ def store_chat_history_in_db(conversation_id, messages):
     except Exception as e:
         logger.info(f"Failed to add the chat in db {e}")
 
-def get_chat_history_from_db(conversation_id):
-    try:
-        API_URL = f"http://127.0.0.1:8000/chat-db/get-history"
-        response = requests.post(API_URL,params={"conversation_id": conversation_id})
-        response.raise_for_status()
-        logger.info(f"Successfully retrieved chat history for conversation ID: {conversation_id}")
-        return response.json()
-    except Exception as e:
-        logger.info(f"Failed to get the chat history")
-    return {"error": "Failed to retrieve chat history. Please try again later."}
+def get_chat_history_from_db(conversation_id: str, retries=3, delay=5):
+    API_URL = "http://127.0.0.1:8000/chat-db/get-history"
+    for attempt in range(retries):
+        try:
+            response = requests.get(API_URL, params={"conversation_id": conversation_id}, timeout=30)
+            response.raise_for_status()
+            return response.json()
+        except ConnectionError:
+            logger.warning(f"Retrying... Attempt {attempt + 1}")
+            time.sleep(delay)
+    raise Exception("Failed to connect after multiple attempts")
 
-def display_chat_history(st):
-    conversation_id = st.session_state.get("conversation_id")
-    if not conversation_id:
-        st.warning("Conversation ID is missing. Please provide a valid ID.")
-        return
+def display_chat_history(st, conversation_id):
+    """
+    Displays the chat history for a given conversation ID in the Streamlit app.
 
+    Args:
+        st (streamlit): Streamlit object for UI rendering.
+        conversation_id (str): Unique identifier for the conversation.
+    """
     try:
         chat_history = get_chat_history_from_db(conversation_id)
-        if chat_history and isinstance(chat_history, dict) and 'error' not in chat_history:
-            st.success("Chat history loaded successfully!")
-            if st.sidebar.button(f"Show History for {conversation_id}",key="show_history_button"):
-                st.json(chat_history)
-        else:
-            st.info("No chat history found for this conversation ID or the data format is incorrect.")
+        button_text = chat_history["messages"][0]['content'].strip()[:20]
+        if st.sidebar.button(f"Show History for {button_text}", key=f"show_history_{button_text}"):
+            st.subheader(f"Chat History for Conversation ID: {conversation_id}")
+            with st.spinner("fetching chat history"):
+                for message in chat_history["messages"]:
+                    st.write(message['role'])
+                    st.write(message['content'])
     except Exception as e:
-        st.error(f"Error retrieving chat history: {e}")
+        logger.error(f"Error retrieving chat history: {e}")
+        st.error("An unexpected error occurred while retrieving chat history.")
+
+    
+def set_bg_image(file_path, opacity=0.5):
+    encoded_img = img_to_base64(file_path)
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background: linear-gradient(rgba(0, 0, 0, {opacity}), rgba(0, 0, 0, {opacity})),
+                        url("data:image/png;base64,{encoded_img}") center/cover fixed no-repeat;
+            min-height: 100vh;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+def custom_navbar():
+    st.markdown(
+        """
+        <style>
+        .navbar {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            background-color: #F0F2F6; 
+            padding: 4px 24px;
+            margin-top: -30px; 
+            width: 100%; 
+            border-radius: 32px;
+            box-shadow: 0 6px 20px rgba(0, 0, 0, 0.15);
+            border: 1px solid #D1D5DB;
+        }
+
+        .logo {
+            font-family: 'Arial', sans-serif;
+            font-size: 30px; /* Slightly larger for better visibility */
+            font-weight: bold;
+            color: #1E293B; /* Darker tone for professional appeal */
+        }
+
+        .nav-links {
+            display: flex;
+            gap: 32px; /* Wider spacing for clarity */
+            align-items: center;
+        }
+
+        .nav-link {
+            color: #1E293B !important; /* Darker color for consistency */
+            background-color: transparent;
+            text-decoration: none;
+            font-weight: 600;
+            font-size: 18px; /* Improved readability */
+            padding: 6px 16px; /* Balanced padding */
+            border-radius: 8px; /* Rounded edges for clickable elements */
+            transition: background-color 0.3s ease, color 0.3s ease; /* Smooth hover effects */
+        }
+
+        .nav-link:hover {
+            background-color: #2E5D5B; /* Distinctive hover effect */
+            color: #FFFFFF; /* White text for contrast */
+        }
+
+        </style>
+
+        <div class="navbar">
+            <div class="logo">Yuvabe Care Companion AI</div>
+            <div class="nav-links">
+                <a href="/" class="nav-link">Home</a>
+                <a href="/Admin_Portal" class="nav-link">Admin Portal</a>
+                <a href="/Knowledge_Base_Explorer" class="nav-link">Knowledge Base Explorer</a>
+                <a href="/chatbot" class="nav-link">Chat With Us</a>
+            </div>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+def type_text(container, text, delay=0.03):
+    """Simulates a typing effect for text with only text highlighted."""
+    displayed_text = ""
+    for char in text:
+        displayed_text += char
+        container.markdown(f"""
+            <h2 style="
+                color: #3D6D6B;
+                text-align: center;
+                background: linear-gradient(90deg, #3D6D6B, #6EA8A5);
+                -webkit-background-clip: text;
+                color: white;
+                font-weight: bold;
+                font-size: 28px;">
+                {displayed_text}
+            </h2>
+        """, unsafe_allow_html=True)
+        time.sleep(delay)

src/frontend/home.py

@@ -1,50 +1,58 @@
 import streamlit as st
 from app import common_functions
-from PIL import Image  
+from pages import Admin_Portal as admin_page
+from pages import Knowledge_Base_Explorer as knowledge_base_explorer_page
+import time
+
+# # Page Configuration
+common_functions.config_homepage()
+common_functions.set_bg_image("src/frontend/images/health_care_baner.png")
+common_functions.custom_navbar()
+
+# def render_homepage():
+#     """Renders the Yuvabe Care Companion AI homepage."""
+
+    # # Welcome Section with Visual
+    # st.image("src/frontend/images/health_care_baner.png", 
+    #          use_container_width=True, 
+    #          caption="Your AI-Powered Health Companion")
+
+    # # Navigation Tabs
+    # Home, Admin_Portal, Knowledge_Base_Explorer = st.tabs(
+    #     ["🏠 Home", "🔒 Admin Portal", "📚 Knowledge Base Explorer"]
+    # )
+
+    # with Home:
+    # st.markdown("""
+    # ### 👋 Welcome to the Yuvabe Care Companion AI!
+    # This platform offers comprehensive tools to support your healthcare journey. Use the tabs above to navigate:
+    # """)
+
+    # # Feature Overview Section
+    # st.markdown("""
+    # ### 🔹 Key Features
+    # - **Admin Portal** — Manage records, data, and configurations efficiently.
+    # - **Knowledge Base Explorer** — Leverage advanced vector search to find relevant knowledge entries with precision.
+    # - **Patient Assistance** — Personalized guidance to help patients describe their concerns.
+
+    # > 💡 *Explore each section for detailed functionality.*
+    # """)
+
+    # with Admin_Portal:
+    #     admin_page.render_admin_portal()
+    #     # if st.button("Go to Admin Portal"):
+    #     #     st.switch_page("pages/admin_portal.py")
+
+    # with Knowledge_Base_Explorer:
+    #     knowledge_base_explorer_page.render_knowledge_base_explorer()
+        # if st.button("Go to Knowledge Base Explorer"):
+        #     st.switch_page("pages/knowledge_base_explorer.py")
 
-def render_homepage():
-    """Renders the Yuvabe Care Companion AI homepage."""
-
-    # Page Configuration
-    common_functions.config_homepage(st)
-
-    common_functions.set_page_title(st)
-
-
-    # Welcome Section with Visual
-    st.image("src/frontend/images/health_care_baner.png", 
-             use_container_width=True, 
-             caption="Your AI-Powered Health Companion")
-
-    # Navigation Tabs
-    Home, Admin_Portal, Knowledge_Base_Explorer = st.tabs(
-        ["🏠 Home", "🔒 Admin Portal", "📚 Knowledge Base Explorer"]
-    )
-
-    with Home:
-        st.markdown("""
-        ### 👋 Welcome to the Yuvabe Care Companion AI!
-        This platform offers comprehensive tools to support your healthcare journey. Use the tabs above to navigate:
-        """)
-
-        # Feature Overview Section
-        st.markdown("""
-        ### 🔹 Key Features
-        - **Admin Portal** — Manage records, data, and configurations efficiently.
-        - **Knowledge Base Explorer** — Leverage advanced vector search to find relevant knowledge entries with precision.
-        - **Patient Assistance** — Personalized guidance to help patients describe their concerns.
-
-        > 💡 *Explore each section for detailed functionality.*
-        """)
-
-    with Admin_Portal:
-        if st.button("Go to Admin Portal"):
-            st.switch_page("pages/admin_portal.py")
+# Render the Homepage
+# render_homepage()
 
-    with Knowledge_Base_Explorer:
-        if st.button("Go to Knowledge Base Explorer"):
-            st.switch_page("pages/knowledge_base_explorer.py")
 
-# Render the Homepage
-if __name__ == "__main__":
-    render_homepage()
+# Display the animated text
+welcome_text = "👋 Welcome to the Yuvabe Care Companion AI!"
+container = st.empty()
+common_functions.type_text(container, welcome_text)

src/frontend/pages/Admin_Portal.py

@@ -1,39 +1,51 @@
 import streamlit as st
 from app import pinecone_data_handler
+from app import common_functions
+
+# # Page Configuration
+common_functions.config_homepage()
+common_functions.custom_navbar()
 
-# Page Configuration
-# st.set_page_config(page_title="Admin Portal", page_icon="🔒", layout="wide")
 def render_admin_portal():
-    """Renders the Admin Portal page with improved UI and navigation."""
-    
+    """Renders the enhanced Admin Portal page with improved UI and navigation."""
+
     # Header Section
-    st.title("🔒 Admin Portal")
+    st.markdown("<h1 class='header-text'>🛠️ Admin Portal</h1>", unsafe_allow_html=True)
     st.markdown("""
-    ### Welcome to the Admin Portal!
-    Manage your Pinecone data efficiently with the options below.
+        Welcome to the **Admin Portal**.  
+        Manage your Pinecone database with secure and efficient tools.
     """)
+    st.divider()
 
     # Data Manager Tabs
     DataManager = st.tabs(["📂 Pinecone Data Manager"])[0]
 
     with DataManager:
-        Upsert, Delete = st.tabs(["📥 Upsert Data", "🗑️ Delete Records"])
+        Upsert, Delete = st.tabs(["Upsert Data", "Delete Records"])
 
+        # Upsert Section
         with Upsert:
+            st.markdown("<div class='card'>", unsafe_allow_html=True)
             st.markdown("### 📥 Upsert Data")
-            st.info("Use this section to insert or update data in Pinecone.")
+            st.info(
+                "Use this section to **insert** or **update** records in Pinecone."
+                "\n\n✅ Ensure your data is correctly formatted before uploading."
+            )
+            st.markdown("---")
             pinecone_data_handler.upsert_data(st)
+            st.markdown("</div>", unsafe_allow_html=True)
 
+        # Delete Section
         with Delete:
-            st.markdown("### 🗑️ Delete Records")
-            st.warning("Use this section to delete records from Pinecone. Be cautious when performing deletions.")
+            st.markdown("<div class='card'>", unsafe_allow_html=True)
+            st.markdown("### ⚠️ Delete Records")
+            st.error(
+                "❗ **Warning:** Deleting data is irreversible.\n"
+                "Proceed with caution."
+            )
+            # Confirmation Dialog for Safety
             pinecone_data_handler.delete_records(st)
 
-    # Sidebar for Navigation
-    st.sidebar.title("🔀 Navigation")
-    st.sidebar.markdown("[🏠 Home](http://localhost:8501)", unsafe_allow_html=True)
-    st.sidebar.markdown("[🔒 Knowledge Base](http://localhost:8501/Knowledge_Base_Explorer)", unsafe_allow_html=True)
-
 # Call the function to render the Admin Portal
 if __name__ == "__main__":
     render_admin_portal()

src/frontend/pages/Knowledge_Base_Explorer.py

@@ -1,27 +1,29 @@
 import streamlit as st
 from app import pinecone_data_handler
+from app import common_functions
+
+# Page Configuration
+# st.set_page_config(page_title="Knowledge Base Explorer", page_icon="📚", layout="wide")
 
 def render_knowledge_base_explorer():
     """Renders the Knowledge Base Explorer page with improved UI and navigation."""
     
-    # Page Configuration
-    st.set_page_config(page_title="Knowledge Base Explorer", page_icon="📚", layout="wide")
-
     # Header Section
-    st.title("📚 Knowledge Base Explorer")
-    st.markdown("""
-    ### Discover Helpful Information!
-    Enter your concerns below to receive insights and solutions tailored to your needs.
-    """)
+    # st.title("📚 Knowledge Base Explorer")
+    # st.markdown("""
+    # ### Discover Helpful Information!
+    # Enter your concerns below to receive insights and solutions tailored to your needs.
+    # """)
 
     # Knowledge Base Section
     with st.expander("🔍 Explore the Knowledge Base"):
         pinecone_data_handler.render_metadata_fetch_form(st)
 
-    # Sidebar for Navigation
-    st.sidebar.title("🔀 Navigation")
-    st.sidebar.markdown("[🏠 Home](http://localhost:8501)", unsafe_allow_html=True)
-    st.sidebar.markdown("[🔒 Admin Portal](http://localhost:8501/Admin_Portal)", unsafe_allow_html=True)
+
+    # # Sidebar for Navigation
+    # st.sidebar.title("🔀 Navigation")
+    # st.sidebar.markdown("[🏠 Home](http://localhost:8501)", unsafe_allow_html=True)
+    # st.sidebar.markdown("[🔒 Admin Portal](http://localhost:8501/Admin_Portal)", unsafe_allow_html=True)
 
 # Call the function to render the Knowledge Base Explorer
 if __name__ == "__main__":

src/frontend/pages/chatbot.py

@@ -1,17 +1,20 @@
 import streamlit as st
 import requests
 from app import common_functions
+from datetime import datetime
 
 API_URL = "http://localhost:8000/chat/get-health-advice/"
 NUMBER_OF_MESSAGES_TO_DISPLAY = 20
-common_functions.config_homepage(st)
-common_functions.set_page_title(st)
+common_functions.config_homepage()
+common_functions.set_page_title()
 # Initialize conversation history
 def initialize_conversation():
     assistant_message = ("Hello! I am your Yuvabe Care Companion AI, here to assist you with general medicine queries. " 
                          "How can I help you today?")
     
     return [{"role": "assistant", "content": assistant_message}]
+system_message = ("Hello! I am your Yuvabe Care Companion AI, here to assist you with general medicine queries. " 
+                         "How can I help you today?")
 
 # Function to fetch advice from the API
 def fetch_health_advice(conversation_history):
@@ -23,11 +26,11 @@ def fetch_health_advice(conversation_history):
         response.raise_for_status()
         return response.json().get("reply", "I couldn't process your request at the moment.")
     except requests.exceptions.RequestException as e:
-        st.error(f"❗ API Connection Error: {e}")
+        st.error(f"API Connection Error: {e}")
         return "I'm currently unable to respond. Please try again later."
 
 if "conversation_history" not in st.session_state:
-    st.session_state.conversation_history = initialize_conversation()
+    st.session_state.conversation_history = []
 
 # Display chat history
 for message in st.session_state.conversation_history [-NUMBER_OF_MESSAGES_TO_DISPLAY:]:
@@ -38,31 +41,29 @@ for message in st.session_state.conversation_history [-NUMBER_OF_MESSAGES_TO_DIS
 
 # User Input
 user_input = st.chat_input("Ask your health-related question:")
-
+with st.chat_message('ai'):
+    common_functions.typewriter_effect(system_message)
 
 if user_input:
 
     if 'conversation_id' not in st.session_state:
-        st.session_state.conversation_id = user_input.strip()[:50]
+        st.session_state.conversation_id = datetime.now().strftime("%Y-%m-%d")
 
     # Display user's input
     with st.chat_message('user'):
-        common_functions.typewriter_effect(st,user_input)
+        common_functions.typewriter_effect(user_input)
         
-
     # Append user input to session history
     st.session_state.conversation_history.append({"role": "user", "content": user_input})
     
-
     # Fetch assistant response
     assistant_reply = fetch_health_advice(st.session_state.conversation_history)
 
     # Append assistant's reply to conversation history first
     st.session_state.conversation_history.append({"role": "assistant", "content": assistant_reply})
     common_functions.store_chat_history_in_db(st.session_state.conversation_id,st.session_state.conversation_history)
-    common_functions.display_chat_history(st)
-
+    common_functions.display_chat_history(st,st.session_state.conversation_id)
 
     # Display only the assistant's latest response
     with st.chat_message('assistant'):
-        common_functions.typewriter_effect(st,assistant_reply)
+        common_functions.typewriter_effect(assistant_reply)