Update app.py

app.py

@@ -6,23 +6,32 @@ import seaborn as sns
 import requests
 from googleapiclient.discovery import build
 from google.oauth2.credentials import Credentials
+from groq import Groq
 
-# News API Key
-news_api_key = "fe1e6bcbbf384b3e9220a7a1138805e0"  # Replace with your News API key
+# Groq API Key (replace with your actual API key)
+groq_api_key = "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"
+
+# Initialize Groq client
+client = Groq(api_key=groq_api_key)
 
 @st.cache_data
 def load_data(file):
     return pd.read_csv(file)
 
-def fetch_health_articles(query):
-    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={news_api_key}"
-    response = requests.get(url)
-    if response.status_code == 200:
-        articles = response.json().get('articles', [])
-        return articles[:5]
-    else:
-        st.error("Failed to fetch news articles. Please check your API key or try again later.")
-        return []
+def get_personalized_advice(user_data):
+    """Fetch personalized advice using the Groq API based on user data"""
+    # Construct the query for the Groq API
+    query = f"Based on the user's data: anxiety level: {user_data['anxiety_level']}, self esteem: {user_data['self_esteem']}, mental health history: {user_data['mental_health_history']}, stress level: {user_data['stress_level']}, provide personalized mental health advice."
+    
+    try:
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": query}],
+            model="llama-3.3-70b-versatile",
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        st.error(f"Error fetching advice from Groq: {str(e)}")
+        return None
 
 def stress_level_to_string(stress_level):
     """Convert numerical stress level (0, 1, 2) to a string representation."""
@@ -33,28 +42,6 @@ def stress_level_to_string(stress_level):
     else:
         return "High"
 
-def provide_advice_from_articles(data):
-    advice = []
-    stress_level = stress_level_to_string(data['stress_level'])
-    
-    if stress_level == "High":
-        advice.append("Searching for articles related to high stress...")
-        articles = fetch_health_articles("high stress")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    elif stress_level == "Moderate":
-        advice.append("Searching for articles related to moderate stress...")
-        articles = fetch_health_articles("moderate stress")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    else:
-        advice.append("Searching for articles related to low stress...")
-        articles = fetch_health_articles("low stress")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    
-    return advice
-
 def plot_graphs(data):
     """Create subplots for visualization."""
     st.markdown("### 📊 Data Visualizations")
@@ -132,11 +119,10 @@ def main():
                 st.write("### Selected User Details:")
                 st.json(row_data)
 
-                st.subheader("🔔 Health Advice Based on Articles")
-                advice = provide_advice_from_articles(row_data)
+                st.subheader("🔔 Health Advice Based on Groq")
+                advice = get_personalized_advice(row_data)
                 if advice:
-                    for i, tip in enumerate(advice, 1):
-                        st.write(f"📌 **{i}.** {tip}")
+                    st.write(advice)
                 else:
                     st.warning("No specific advice available based on this user's data.")