Update app.py

app.py

@@ -4,9 +4,10 @@ from groq import Groq
 from streamlit.components.v1 import html
 
 # Load Groq API key from environment variable
-groq_api_key = os.getenv('api_key')
+groq_api_key = os.getenv('GROQ_API_KEY')
 if not groq_api_key:
-    raise ValueError("GROQ_API_KEY environment variable is not set.")
+    st.error("GROQ_API_KEY environment variable is not set. Please set it and restart the app.")
+    st.stop()
 
 # Initialize Groq client
 groq_client = Groq(api_key=groq_api_key)
@@ -14,49 +15,7 @@ groq_client = Groq(api_key=groq_api_key)
 # Few-shot examples for inspiration
 few_shot_examples = """
 🎢 **Data Scaling Showdown: Normalization vs Standardization!** 📊
-
-Ever felt like your data is on a rollercoaster? 🎡 Some values reaching for the stars, others barely off the ground? Time to level the playing field with the dynamic duo of data scaling: **Normalization** and **Standardization!** 💪
-
-🌈 **Normalization: The Range Ranger**
-🔸 What: Squeezes your data into a cozy 0-1 range.
-🔸 Why: Perfect for when your features are as different as apples and skyscrapers! 🍎🏙️
-🔸 Best for: k-NN, neural networks, and data that's living its best non-normal life.
-🔸 Superpower: Taming wild data ranges without breaking a sweat!
-
-📊 **Standardization: The Gaussian Don**
-🔷 What: Centers your data around 0, with a standard deviation of 1.
-🔷 Why: Ideal when your data follows the bell curve or your algorithm is a normal distribution fan.
-🔷 Best for: Logistic regression, SVM, and PCA - the cool kids of the ML world.
-🔷 Superpower: Handling outliers like a boss!
-
-🥊 **The Face-Off**  
-**Normalization:** "Let's all fit in this box!" 📦  
-**Standardization:** "Let's dance around the mean!" 💃
-
-Use Normalization for diverse data like a candy store 🍭, and Standardization when your data fits the normal crowd 🎭.
-
-#DataScience #NormalizationVsStandardization #AITricks
-
----
-🎯 **Binary Classification or Multiclass? Here's How to Choose!** 🎯
-
-Choosing between **binary** and **multiclass classification** can feel like picking the perfect dessert—do you want the classic **chocolate cake** or an **exotic fruit tart**?
-
-**Binary Classification:**
-🔸 Focuses on **two classes**: cat or dog, pass or fail, true or false.
-🔸 **Simpler** to model, quick to train, and widely used when the task is a straightforward **this or that** decision.
-
-**Multiclass Classification:**
-🔷 Handles **more than two classes**: apple, orange, banana, or even dragon fruit!
-🔷 Complex but powerful when you need **multiple predictions** from a single model, often used in tasks like image recognition.
-
----
-
-🧠 **How to Ace Hyperparameter Tuning!**
-
-Tuning hyperparameters can feel like adjusting the knobs on an old radio—get it just right and the music plays beautifully. 🎶 
-
-#AI #DataScienceTips #MLHacks
+...
 """
 
 # Function to get Groq response with few-shot examples
@@ -101,7 +60,10 @@ if st.button("Generate Post"):
     if user_input:
         with st.spinner("Generating post..."):
             response = get_groq_response_with_few_shot(post_type, user_input)
-            st.success("Post generated successfully!")
+            if "An error occurred" in response:
+                st.error(response)
+            else:
+                st.success("Post generated successfully!")
     else:
         st.error("Please enter a topic or idea!")