the updated ones

app.py

@@ -1,10 +1,12 @@
 import streamlit as st
-from code.DiseaseModel import DiseaseModel
-from code.helper import prepare_symptoms_array
+from langchain_google_genai import ChatGoogleGenerativeAI
 
-# Create disease class and load ML model
-disease_model = DiseaseModel()
-disease_model.load_xgboost('model/xgboost_model.json')
+# Set up AI model
+llm = ChatGoogleGenerativeAI(
+    model="gemini-1.5-flash",  # Free model
+    google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE",
+    temperature=0.5
+)
 
 # Set page width to wide
 st.set_page_config(layout='wide')
@@ -12,7 +14,7 @@ st.set_page_config(layout='wide')
 # Custom CSS for background color and text color
 st.markdown(
     """
-     <style>
+    <style>
         .stApp {
             background-color: #efefef !important;
             color: black !important;
@@ -23,36 +25,85 @@ st.markdown(
         button, .stButton>button {
             color: black !important;
         }
-        header {display: none !important;}
+        header, iframe {display: none !important;}
     </style>
     """,
     unsafe_allow_html=True
 )
 
-# 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")
+# Streamlit UI
+st.title("AI-Driven Health Assistant")
+st.write("Welcome 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)
+
+# Process User Query
+if st.button("Get Recommendation"):
+    if user_question.strip():
+        # Ensure the AI provides both medicine and alternative treatments
+        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."
+        )
+
+        with st.spinner("Analyzing..."):
+            response = llm.invoke(formatted_question)
 
-# Title
-st.write('# Symptoms to Disease Prediction')
+        # Extract text content
+        response_text = response.content if hasattr(response, "content") else str(response)
 
-symptoms = st.multiselect('What are your symptoms?', options=disease_model.all_symptoms)
+        # Check if response is valid
+        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")
+            # Retry with a refined prompt
+            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)
 
-X = prepare_symptoms_array(symptoms)
+            # Extract text from retry response
+            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')
+            # Display the retried response if valid
+            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.")
 
-    tab1, tab2= st.tabs(["Description", "Precautions"])
+    else:
+        st.warning("Please enter a question!")
 
-    with tab1:
-        st.write(disease_model.describe_predicted_disease())
+# 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 tab2:
-        precautions = disease_model.predicted_disease_precautions()
-        for i in range(4):
-            st.write(f'{i+1}. {precautions[i]}')
+# Footer
+st.markdown("---")