Update app.py

app.py

@@ -1,23 +1,16 @@
 import streamlit as st
 import openai
 from langchain_google_genai import ChatGoogleGenerativeAI
-# Assuming you have a package or class for Groq integration. Check actual package/class.
-# from langchain_groq import ChatGroqGenerativeAI
 from datetime import datetime, timedelta
 import time
 
 # API keys
 GOOGLE_API_KEY = st.secrets["GOOGLE_API_KEY"]
 OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
-GROQ_API_KEY = st.secrets["GROQ_API_KEY"]  # Assume you have an API key for Groq
 
 # Initialize OpenAI
 openai.api_key = OPENAI_API_KEY
 
-# Initialize Groq
-# Uncomment and update if you find the correct class for Groq.
-# groq_llm = ChatGroqGenerativeAI(model="groq-pro", groq_api_key=GROQ_API_KEY)
-
 # In-memory storage for progress tracking
 progress_data = {
     "questions_solved": {
@@ -33,10 +26,6 @@ def get_llm(model_choice):
         return ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_API_KEY)
     elif model_choice == "OpenAI":
         return None
-    elif model_choice == "Groq":
-        # Replace with actual initialization if you find the correct class.
-        # return groq_llm
-        raise ValueError("Groq LLM is not initialized. Please check the correct class.")
     else:
         raise ValueError("Unsupported model choice.")
 
@@ -51,15 +40,11 @@ def generate_questions(model_choice, role, question_type, num_questions, difficu
             prompt=prompt,
             max_tokens=150
         )
-        return response.choices[0].text.strip().split('\n')
+        return response.choices[0].text.strip()
     elif model_choice == "Gemini":
         llm = get_llm(model_choice)
         response = llm.invoke(prompt)
-        return response.content.split('\n')
-    elif model_choice == "Groq":
-        llm = get_llm(model_choice)
-        response = llm.invoke(prompt)
-        return response.content.split('\n')
+        return response.content
     else:
         raise ValueError("Unsupported model choice.")
 
@@ -76,10 +61,6 @@ def provide_feedback(model_choice, answer):
         llm = get_llm(model_choice)
         response = llm.invoke(prompt)
         return response.content
-    elif model_choice == "Groq":
-        llm = get_llm(model_choice)
-        response = llm.invoke(prompt)
-        return response.content
     else:
         raise ValueError("Unsupported model choice.")
 
@@ -96,10 +77,6 @@ def get_tips(model_choice, role):
         llm = get_llm(model_choice)
         response = llm.invoke(prompt)
         return response.content
-    elif model_choice == "Groq":
-        llm = get_llm(model_choice)
-        response = llm.invoke(prompt)
-        return response.content
     else:
         raise ValueError("Unsupported model choice.")
 
@@ -293,56 +270,64 @@ with st.sidebar:
 
 st.title("💼 TechPrep")
 
-def main():
-    st.title("Interview Preparation Assistant")
-
-    st.sidebar.header("Menu")
-    selection = st.sidebar.radio("Choose an option", ["Generate Questions", "Provide Feedback", "Get Tips", "Schedule Mock Interview", "Track Progress", "Connect with Resources"])
-
-    if selection == "Generate Questions":
-        st.subheader("Generate Interview Questions")
-        model_choice = st.selectbox("Select Model", ["OpenAI", "Gemini", "Groq"])
-        role = st.text_input("Enter Role", "Software Engineer")
-        question_type = st.selectbox("Select Question Type", ["Behavioral", "Technical", "Situational", "Case Study", "Problem Solving"])
-        num_questions = st.slider("Number of Questions", 1, 10, 5)
-        difficulty = st.selectbox("Select Difficulty", ["Easy", "Medium", "Hard"])
-
-        if st.button("Generate Questions"):
-            with st.spinner("Generating questions..."):
-                questions = generate_questions(model_choice, role, question_type, num_questions, difficulty)
-                if isinstance(questions, list):
-                    for question in questions:
-                        st.write(question)
-                        time.sleep(1)  # Display questions one by one
-
-    elif selection == "Provide Feedback":
-        st.subheader("Provide Feedback on an Answer")
-        model_choice = st.selectbox("Select Model", ["OpenAI", "Gemini", "Groq"])
-        answer = st.text_area("Enter Interview Answer")
-
-        if st.button("Get Feedback"):
+model_choice = st.selectbox("Select AI Model", ["Gemini", "OpenAI"], key="select_model")
+
+# Updated role dropdown with 20 roles
+roles = [
+    "Software Developer", "Data Analyst", "Marketing Manager", "Project Manager", "UX Designer",
+    "Machine Learning Engineer", "Data Scientist", "AI Researcher", "Product Manager", "Business Analyst",
+    "Web Developer", "Mobile App Developer", "Cloud Engineer", "DevOps Engineer", "System Analyst",
+    "Database Administrator", "Cybersecurity Specialist", "QA Engineer", "IT Support Specialist", "Network Engineer",
+    "Graphic Designer"
+]
+role = st.selectbox("Select Role", roles, key="select_role")
+
+question_type = st.selectbox("Select Question Type", ["Behavioral", "Technical", "Situational", "Case Study", "Problem Solving"], key="select_question_type")
+num_questions = st.slider("Number of Questions", 1, 10, key="slider_num_questions")
+difficulty = st.selectbox("Select Difficulty Level", ["Easy", "Medium", "Hard"], key="select_difficulty")
+
+st.header("📝 Generate Interview Questions")
+if st.button("Generate Questions", key="generate_questions"):
+    with st.spinner("Generating questions..."):
+        questions = generate_questions(model_choice, role, question_type, num_questions, difficulty)
+        st.markdown(style_output("Questions Generated:", "#4CAF50"), unsafe_allow_html=True)
+        st.write(questions)
+        progress_data["questions_solved"][question_type] += num_questions
+
+st.header("🗣️ Provide Feedback")
+answer = st.text_area("Submit Your Answer", key="text_area_answer")
+if st.button("Submit Answer", key="submit_answer"):
+    if not answer:
+        st.error("Please enter an answer to receive feedback.")
+    else:
+        with st.spinner("Providing feedback..."):
             feedback = provide_feedback(model_choice, answer)
-            st.write(style_output(feedback, "blue"))
+            st.markdown(style_output("Feedback Received:", "#FF5722"), unsafe_allow_html=True)
+            st.write(feedback)
             progress_data["feedback_provided"] += 1
 
-    elif selection == "Get Tips":
-        st.subheader("Get Interview Tips")
-        model_choice = st.selectbox("Select Model", ["OpenAI", "Gemini", "Groq"])
-        role = st.text_input("Enter Role", "Software Engineer")
-
-        if st.button("Get Tips"):
-            tips = get_tips(model_choice, role)
-            st.write(style_output(tips, "green"))
-            progress_data["tips_retrieved"] += 1
-
-    elif selection == "Schedule Mock Interview":
-        schedule_mock_interview()
-
-    elif selection == "Track Progress":
-        track_progress()
-
-    elif selection == "Connect with Resources":
-        connect_resources()
+st.header("💡 Interview Tips")
+if st.button("Get Tips", key="get_tips"):
+    with st.spinner("Fetching tips..."):
+        tips = get_tips(model_choice, role)
+        st.markdown(style_output("Tips Received:", "#2196F3"), unsafe_allow_html=True)
+        st.write(tips)
+        progress_data["tips_retrieved"] += 1
+
+st.header("📅 Schedule Mock Interview")
+schedule_mock_interview()
+
+st.header("📈 Progress and Resources")
+col1, col2 = st.columns(2)
+with col1:
+    track_progress()
+with col2:
+    connect_resources()
+
+# Footer
+st.markdown("""
+    <div class="footer">
+        <p>&copy; 2024 TechPrep. All rights reserved.</p>
+    </div>
+    """, unsafe_allow_html=True)
 
-if __name__ == "__main__":
-    main()