this is the updated code

app.py

@@ -1,12 +1,13 @@
 import streamlit as st
-from langchain_google_genai import ChatGoogleGenerativeAI
+from code.DiseaseModel import DiseaseModel
+from code.helper import prepare_symptoms_array
 
-# Set up AI model
-llm = ChatGoogleGenerativeAI(
-    model="gemini-1.5-flash",  # Free model
-    google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE",
-    temperature=0.5
-)
+# Create disease class and load ML model
+disease_model = DiseaseModel()
+disease_model.load_xgboost('model/xgboost_model.json')
+
+# Set page width to wide
+st.set_page_config(layout='wide')
 
 # Custom CSS for background color and text color
 st.markdown(
@@ -16,91 +17,42 @@ st.markdown(
             background-color: #efefef !important;
             color: black !important;
         }
-        iframe {
-            border: none !important;
-            box-shadow: none !important;
-            outline: none !important;
-        }
         h1, h2, h3, h4, h5, h6, p, div, span, label {
             color: black !important;
         }
         button, .stButton>button {
             color: black !important;
-            background-color: grey !important;
         }
-        /* Hide Streamlit's top colored bar and options icon */
         header {display: none !important;}
-        .stDeployButton {display: none !important;}
     </style>
     """,
     unsafe_allow_html=True
 )
 
-# Streamlit UI
-st.title("AI-Driven Health Assistant")
-st.write("Welcome to the AI-Driven Health Assistant! Simply enter your symptoms or disease name, and get accurate medicine suggestions instantly. Stay informed, stay healthy!")
-
-# User Input
-user_question = st.text_input("Type your symptoms or disease name, and let CureBot unlock the right cure for you—fast, smart, and AI-powered")
-
-# Function to filter AI disclaimers
-def is_valid_response(response_text):
-    disclaimers = [
-        "I am an AI and cannot give medical advice",
-        "Seek medical attention",
-        "Consult a doctor",
-        "Contact your doctor",
-        "Go to an emergency room",
-    ]
-    return not any(phrase.lower() in response_text.lower() for phrase in disclaimers)
+# Create sidebar
+st.sidebar.markdown('# The Health AI ')
+st.sidebar.markdown("This web app uses a machine learning model to predict diseases based on a set of symptoms using Scikit-learn, Python and Streamlit.")
+st.sidebar.markdown("Author: S N V S KOMAL")
 
-# Process User Query
-if st.button("Get Recommendation"):
-    if user_question.strip():
-        formatted_question = (
-            f"Without any disclaimer, recommend medicine for {user_question}. "
-            f"5 medicine names "
-            f"Also, provide alternative treatments such as home remedies, lifestyle changes, exercises, or dietary suggestions. "
-            f"Only for learning purposes, not for treatment."
-        )
+# Title
+st.write('# Symptoms to Disease Prediction')
 
-        with st.spinner("Analyzing..."):
-            response = llm.invoke(formatted_question)
+symptoms = st.multiselect('What are your symptoms?', options=disease_model.all_symptoms)
 
-        response_text = response.content if hasattr(response, "content") else str(response)
+X = prepare_symptoms_array(symptoms)
 
-        if is_valid_response(response_text):
-            st.success("✨ Analysis complete! Here are the best medicine recommendations for you: 🔽")
-            st.write(response_text)
-        else:
-            st.warning("⚠️ Oops! It looks like the input is unclear or incorrect. Please enter a valid disease name or symptoms to get accurate recommendations")
-            better_prompt = (
-                f"Strictly provide a detailed answer including:\n"
-                f"1. Medicine names\n"
-                f"2. Home remedies\n"
-                f"3. Lifestyle changes\n"
-                f"4. Exercises\n"
-                f"5. Diet recommendations\n"
-                f"Do not include any disclaimers. The response should be clear and structured."
-            )
-            retry_response = llm.invoke(better_prompt)
-            retry_response_text = retry_response.content if hasattr(retry_response, "content") else str(retry_response)
+# Trigger XGBoost model
+if st.button('Predict'):
+    # Run the model with the python script
+    prediction, prob = disease_model.predict(X)
+    st.write(f'## Disease: {prediction} with {prob*100:.2f}% probability')
 
-            if is_valid_response(retry_response_text):
-                st.success("Here is the refined information:")
-                st.write(retry_response_text)
-            else:
-                st.error("Unable to get a useful response. Try rephrasing your question.")
-    else:
-        st.warning("Please enter a question!")
+    tab1, tab2= st.tabs(["Description", "Precautions"])
 
-# Emergency Contact Button
-if st.button("Emergency Contact"):
-    st.subheader("📞 Emergency Contacts")
-    st.write("- 🚑 *Ambulance:* 102")
-    st.write("- 🏥 *National Health Helpline:* 108")
-    st.write("- ☎ *COVID-19 Helpline:* 1075")
-    st.write("- 🚓 *Police:* 100")
+    with tab1:
+        st.write(disease_model.describe_predicted_disease())
 
-# Footer
-st.markdown("---")
+    with tab2:
+        precautions = disease_model.predicted_disease_precautions()
+        for i in range(4):
+            st.write(f'{i+1}. {precautions[i]}')