Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,61 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+from groq import Groq
+from dotenv import load_dotenv
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the API key from environment variable
+api_key = os.getenv("GROQ_API_KEY")
+
+# Initialize Groq client with the API key
+client = Groq(api_key=api_key)
+
+# Define your chatbot logic for student exam preparation assistant
+def chatbot():
+    st.title("Student Exam Preparation Assistant 🎓")
+    st.write("Welcome to your personal exam preparation assistant! Whether you're preparing for a high school exam, college exams, or any professional tests, I'm here to help. What would you like assistance with today?")
+
+    # Add an attractive header with an emoji
+    st.markdown("**Ask me anything about exam preparation!**")
+    st.markdown("I can help you with study tips, time management strategies, practice questions, and more. Let’s get started! 😄")
+
+    # Input field for the user to type a message
+    user_input = st.text_input("Type your exam preparation question here:")
+
+    # Add a submit button
+    if st.button("Submit"):
+        if user_input:
+            # Display user's input
+            st.write(f"You: {user_input}")
+
+            # Sending user's input to Groq API for completion
+            try:
+                completion = client.chat.completions.create(
+                    model="deepseek-r1-distill-llama-70b",  # You can change this model based on your preference
+                    messages=[{"role": "user", "content": user_input}],
+                    temperature=0.6,
+                    max_completion_tokens=4096,
+                    top_p=0.95,
+                    stream=True,
+                    stop=None,
+                )
+
+                # Collect the response chunk by chunk
+                response = ""
+                for chunk in completion:
+                    # Get the assistant's response from each chunk
+                    response += chunk.choices[0].delta.content or ""
+
+                # Display assistant's response
+                st.write(f"Assistant: {response}")
+            
+            except Exception as e:
+                st.write(f"Error occurred: {e}")
+        else:
+            st.write("Please type a question before submitting. 😊")
+
+# Run the chatbot with dynamic user input
+if __name__ == "__main__":
+    chatbot()