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app.py

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+import streamlit as st
+import google.generativeai as genai
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# Configure the API using the key from environment variables
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Function to get response from the Gemini model
+def get_gemini_response(input_text):
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(input_text)
+    return response.text
+
+# Streamlit UI setup
+st.title("CVD Prediction AI BOT")
+st.markdown("Enhancing Cardiovascular Disease Prediction with LLM: <span style='color:#009933;'>**A Prototype Using Human Vital Signs**</span><br>by <span style='color:#ff6600;'>***Chidozie Louis Uzoegwu***</span>", unsafe_allow_html=True)
+
+# Patient Information Form
+st.write ("""
+## Normal Vital Signs in Adults
+| Vital Sign        | Normal Range            |
+|--------------------|-------------------------|
+| Body Temperature   | 98.6°F (37°C)           |
+| Heart Rate         | 60-100 beats per minute |
+| Respiratory Rate   | 12-18 breaths per minute|
+| Blood Oxygen       | 95-100%                 |
+| Blood Pressure     | 120/80 mm Hg            |
+""")
+("Patient Information:")
+age = st.slider("Age", min_value=1, max_value=100, value=30)
+gender = st.radio("Gender", ["Male", "Female"])
+heart_rate = st.slider("Heart Rate (beats per minute)", min_value=30, max_value=200, value=75)
+respiration_rate = st.slider("Respiration Rate (breaths per minute)", min_value=5, max_value=40, value=15)
+body_temperature = st.slider("Body Temperature (°C)", min_value=30.0, max_value=45.0, value=37.0, step=0.1)
+blood_pressure_systolic = st.slider("Blood Pressure - Systolic (mm Hg)", min_value=50, max_value=250, value=120)
+blood_pressure_diastolic = st.slider("Blood Pressure - Diastolic (mm Hg)", min_value=30, max_value=150, value=80)
+oxygen_saturation = st.slider("Blood Oxygen Saturation (%)", min_value=50, max_value=100, value=98)
+
+submit = st.button('Predict CVD')
+
+if submit:
+    # Prepare the input prompt using the patient information
+    input_prompt = f"""
+    Given the patient's information, assess the risk of cardiovascular disease (CVD). Patient details:
+    - Age: {age}
+    - Gender: {gender}
+    - Heart Rate: {heart_rate} bpm
+    - Respiration Rate: {respiration_rate} breaths per minute
+    - Body Temperature: {body_temperature} °C
+    - Blood Pressure - Systolic: {blood_pressure_systolic} mm Hg
+    - Blood Pressure - Diastolic: {blood_pressure_diastolic} mm Hg
+    - Blood Oxygen Saturation: {oxygen_saturation}%
+    
+ Provide an analysis in a simple table based on vital signs and physiological characteristics, focusing on measures such as heart rate, respiration rate, body temperature, blood pressure, and oxygen saturation. 
+ Consider the normal ranges and their significance in assessing CVD risk. 
+ Also, explore the use of machine learning algorithms in predicting vital signs for CVD monitoring and intervention. 
+ Include insights on the role of these vital signs in evaluating the basic operations of the human body and their importance in clinical examinations and long-term CVD risk assessment. 
+ As part of the conclusion make massive point about enhancing the development and implementation of an innovative Internet of Medical Things (IoMT) -based remote patient monitoring system. 
+ Lastly include advice, such as see doctor, eat healthy, exercise, etc, depending on the level of critical assessment.
+    """
+    response = get_gemini_response(input_prompt)
+    st.write(response)

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv