Update app.py

app.py

@@ -1,80 +1,80 @@
-import os
-import streamlit as st
-import google.generativeai as genai
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-api_key = os.getenv("GEMINI_API_KEY")
-
-# Check if API key is set
-if not api_key:
-    st.error("API key not found. Please set GEMINI_API_KEY in your .env file.")
-    st.stop()
-
-# Configure the generative AI model
-genai.configure(api_key=api_key)
-generation_config = {
-    "temperature": 0.7,
-    "top_p": 0.9,
-    "top_k": 50,
-    "max_output_tokens": 1024,
-    "response_mime_type": "text/plain",
-}
-
-try:
-    model = genai.GenerativeModel(
-        model_name="gemini-1.5-flash",
-        generation_config=generation_config
-    )
-except Exception as e:
-    st.error(f"Failed to load model: {str(e)}")
-    st.stop()
-
-# Main function for Streamlit app
-def main():
-    st.title("Personalized Course Recommendation System")
-
-    # List of questions for the user
-    questions = [
-        "Write your career interests shortly.",
-        "What is Your Academic Background?",
-        "What are your specific skills?",
-        "How many hours per week can you dedicate to learning?",
-        "Do you prefer short-term or long-term courses?",
-        "Do you prefer online or in-person learning?",
-        "What is your preferred working environment (e.g., office, remote, hybrid)?",
-        "What are your long-term career goals?",
-        "How do you prefer to learn new concepts (e.g., reading, hands-on practice, videos)?",
-        "What are your hobbies and interests?",
-        "What is your preferred learning style (e.g., visual, auditory, kinesthetic)?",
-        "What motivates you to learn new things?",
-        "What are your strengths and weaknesses?",
-    ]
-
-    # Collect user responses
-    responses = {q: st.text_area(q, "") for q in questions}
-
-    # Button to get recommendations
-    if st.button("Get Career Path Recommendation"):
-        if all(responses.values()):
-            with st.spinner("Generating recommendations..."):
-                try:
-                    # Start chat session and send the message
-                    chat_session = model.start_chat(
-                        history=[{"role": "user", "parts": [{"text": f"{q}: {a}"} for q, a in responses.items()]}]
-                    )
-                    response = chat_session.send_message("Based on the answers provided, what career path should the user choose?")
-                    recommendation = response.text.strip()
-
-                    # Display the recommendation
-                    st.subheader("Career Path Recommendation:")
-                    st.write(recommendation)
-                except Exception as e:
-                    st.error(f"An error occurred while generating recommendations: {str(e)}")
-        else:
-            st.error("Please answer all the questions to get a recommendation.")
-
-# Run the app
-if __name__ == "__main__":
-    main()
+import os
+import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+api_key = os.getenv("GEMINI_API_KEY")
+
+# Check if API key is set
+if not api_key:
+    st.error("API key not found. Please set GEMINI_API_KEY in your .env file.")
+    st.stop()
+
+# Configure the generative AI model
+genai.configure(api_key=api_key)
+generation_config = {
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "top_k": 50,
+    "max_output_tokens": 1024,
+    "response_mime_type": "text/plain",
+}
+
+try:
+    model = genai.GenerativeModel(
+        model_name="gemini-1.5-flash",
+        generation_config=generation_config
+    )
+except Exception as e:
+    st.error(f"Failed to load model: {str(e)}")
+    st.stop()
+
+# Main function for Streamlit app
+def main():
+    st.title("Personalized Course Recommendation System")
+
+    # List of questions for the user
+    questions = [
+        "Write your career interests shortly.",
+        "What is Your Academic Background?",
+        "What are your specific skills?",
+        "How many hours per week can you dedicate to learning?",
+        "Do you prefer short-term or long-term courses?",
+        "Do you prefer online or in-person learning?",
+        "What is your preferred working environment (e.g., office, remote, hybrid)?",
+        "What are your long-term career goals?",
+        "How do you prefer to learn new concepts (e.g., reading, hands-on practice, videos)?",
+        "What are your hobbies and interests?",
+        "What is your preferred learning style (e.g., visual, auditory, kinesthetic)?",
+        "What motivates you to learn new things?",
+        "What are your strengths and weaknesses?",
+    ]
+
+    # Collect user responses
+    responses = {q: st.text_area(q, "") for q in questions}
+
+    # Button to get recommendations
+    if st.button("Get Career Path Recommendation"):
+        if all(responses.values()):
+            with st.spinner("Generating recommendations..."):
+                try:
+                    # Start chat session and send the message
+                    chat_session = model.start_chat(
+                        history=[{"role": "user", "parts": [{"text": f"{q}: {a}"} for q, a in responses.items()]}]
+                    )
+                    response = chat_session.send_message("Based on the answers provided, which major Subject of Department of Computer Science & Engineering should the user can take?")
+                    recommendation = response.text.strip()
+
+                    # Display the recommendation
+                    st.subheader("Career Path Recommendation:")
+                    st.write(recommendation)
+                except Exception as e:
+                    st.error(f"An error occurred while generating recommendations: {str(e)}")
+        else:
+            st.error("Please answer all the questions to get a recommendation.")
+
+# Run the app
+if __name__ == "__main__":
+    main()