Delete app.py

app.py

@@ -1,147 +0,0 @@
-import streamlit as st
-import pandas as pd
-import requests
-import os
-from google.cloud import language_v1
-from google.oauth2 import service_account
-
-
-# Set the API key for Google AI API (if not set in the environment variable)
-api_key = "AIzaSyAlvoXLqzqcZgVjhQeCNUsQgk6_SGHQNr8"  # Ensure your credentials are set up
-
-# Initialize Google AI Client
-client = language_v1.LanguageServiceClient(credentials=service_account.Credentials.from_service_account_file("path_to_your_service_account_json"))
-
-# Function to load and preprocess data
-@st.cache_data
-def load_data(file):
-    df = pd.read_csv(file)
-    return df
-
-# Function to fetch and analyze text using Google AI's Natural Language API
-def analyze_text_with_google_ai(text):
-    document = language_v1.Document(content=text, type_=language_v1.Document.Type.PLAIN_TEXT)
-    response = client.analyze_sentiment(document=document)
-    sentiment_score = response.document_sentiment.score
-    sentiment_magnitude = response.document_sentiment.magnitude
-    
-    # Example: Based on sentiment, provide advice
-    if sentiment_score < -0.5:
-        return "You may want to focus on activities that improve your mood, such as physical exercise, talking with a counselor, or engaging in mindfulness practices."
-    elif sentiment_score > 0.5:
-        return "It seems you're in a positive emotional state. Keep nurturing these positive habits, such as engaging in social activities and continuing to practice stress-relief strategies."
-    else:
-        return "You are in a neutral emotional state. Consider exploring activities that help enhance your mood, such as engaging in hobbies or relaxation exercises."
-
-# Function to provide health advice based on user data and Google AI analysis
-def provide_google_ai_advice(data):
-    advice = []
-
-    # Example of analysis based on Google AI's sentiment analysis
-    if data['depression'] > 7 or data['anxiety'] > 7:
-        advice.append("It seems you're experiencing high levels of depression or anxiety. It might be helpful to talk to a professional or consider engaging in activities that can reduce stress, like mindfulness or physical exercise.")
-    
-    # Call Google AI for sentiment-based advice
-    user_data_summary = f"User's depression: {data['depression']}, anxiety: {data['anxiety']}, isolation: {data['isolation']}, future insecurity: {data['future_insecurity']}, stress-relief activities: {data['stress_relief_activities']}"
-    google_ai_advice = analyze_text_with_google_ai(user_data_summary)
-    advice.append(google_ai_advice)
-
-    return advice
-
-# Function to fetch related health articles from GROC API (optional, for RAG-style application)
-def get_health_articles(query):
-    url = f"https://api.groc.com/search?q={query}"
-    headers = {"Authorization": f"Bearer {api_key}"}  # Replace with actual Google API key if required
-
-    try:
-        response = requests.get(url, headers=headers)
-        response.raise_for_status()
-        data = response.json()
-        if 'results' in data:
-            articles = [{"title": item["title"], "url": item["url"]} for item in data['results']]
-        else:
-            articles = []
-        return articles
-    except requests.exceptions.RequestException as err:
-        st.error(f"Error fetching articles: {err}. Please check your internet connection.")
-        return []
-
-# Streamlit app layout
-def main():
-    # Set a background color and style
-    st.markdown(
-        """
-        <style>
-        .stApp {
-            background-color: #F4F4F9;
-        }
-        .stButton>button {
-            background-color: #6200EE;
-            color: white;
-            font-size: 18px;
-        }
-        .stSlider>div>div>span {
-            color: #6200EE;
-        }
-        .stTextInput>div>div>input {
-            background-color: #E0E0E0;
-        }
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-    # Title and header
-    st.title("🌟 **Student Health Advisory Assistant** 🌟")
-    st.markdown("### **Analyze your well-being and get personalized advice**")
-
-    # File upload
-    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
-    if uploaded_file:
-        df = load_data(uploaded_file)
-        st.write("### Dataset Preview:")
-        st.dataframe(df.head())
-
-        # User input for analysis
-        st.markdown("### **Input Your Details**")
-        gender = st.selectbox("🔹 Gender", ["Male", "Female"], help="Select your gender.")
-        age = st.slider("🔹 Age", 18, 35, step=1)
-        depression = st.slider("🔹 Depression Level (1-10)", 1, 10)
-        anxiety = st.slider("🔹 Anxiety Level (1-10)", 1, 10)
-        isolation = st.slider("🔹 Isolation Level (1-10)", 1, 10)
-        future_insecurity = st.slider("🔹 Future Insecurity Level (1-10)", 1, 10)
-        stress_relief_activities = st.slider("🔹 Stress Relief Activities Level (1-10)", 1, 10)
-
-        # Data dictionary for advice
-        user_data = {
-            "gender": gender,
-            "age": age,
-            "depression": depression,
-            "anxiety": anxiety,
-            "isolation": isolation,
-            "future_insecurity": future_insecurity,
-            "stress_relief_activities": stress_relief_activities,
-        }
-
-        # Provide advice based on user inputs
-        if st.button("🔍 Get Observed Advice", key="advice_btn"):
-            st.subheader("🔔 **Health Advice Based on Observations** 🔔")
-            advice = provide_google_ai_advice(user_data)
-            if advice:
-                for i, tip in enumerate(advice, 1):
-                    st.write(f"📌 {i}. {tip}")
-            else:
-                st.warning("No advice available based on your inputs.")
-
-            # Fetch related health articles based on user input
-            st.subheader("📰 **Related Health Articles** 📰")
-            query = "mental health anxiety depression isolation stress relief"
-            articles = get_health_articles(query)
-            if articles:
-                for article in articles:
-                    st.write(f"🌐 [{article['title']}]({article['url']})")
-            else:
-                st.write("No articles found. Please check your API key or internet connection.")
-
-if __name__ == "__main__":
-    main()