new_app.py

import os
import re
import time
from dotenv import load_dotenv
import streamlit as st
from gtts import gTTS
import PyPDF2
import google.generativeai as genai
import speech_recognition as sr

# Load API key
load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Initialize session state
if "generated_questions" not in st.session_state:
    st.session_state["generated_questions"] = []
if "current_question_index" not in st.session_state:
    st.session_state["current_question_index"] = 0
if "answers" not in st.session_state:
    st.session_state["answers"] = []
if "evaluation_feedback" not in st.session_state:
    st.session_state["evaluation_feedback"] = ""
if "overall_score" not in st.session_state:
    st.session_state["overall_score"] = 0
if "percentage_score" not in st.session_state:
    st.session_state["percentage_score"] = 0
if "is_recording" not in st.session_state:
    st.session_state["is_recording"] = False
if "question_played" not in st.session_state:
    st.session_state["question_played"] = False

# Extract text from PDF
def extract_pdf_text(uploaded_file):
    pdf_reader = PyPDF2.PdfReader(uploaded_file)
    text = ''.join(page.extract_text() or "" for page in pdf_reader.pages)
    return text.strip()

# Generate conceptual interview questions
def get_questions(prompt, input_text, num_questions=3):
    model = genai.GenerativeModel('gemini-1.5-pro-latest')
    response = model.generate_content([prompt, input_text])
    questions = [q.strip("* ") for q in response.text.strip().split("\n") if q.strip() and "question" not in q.lower()]
    return questions[:num_questions]

# Evaluate answers automatically
def evaluate_answers():
    model = genai.GenerativeModel('gemini-1.5-pro-latest')
    
    prompt = """
    You are an expert interview evaluator. Assess the responses based on:
    - Conceptual Understanding
    - Communication Skills
    - Clarity & Depth of Explanation
    - Use of Real-World Examples
    - Logical Flow
 
    Provide an evaluation summary with a score out of 10.

    Format:
    **Overall Score:** x/10
    **Evaluation Summary:**
    - Concept Understanding: ...
    - Communication: ...
    - Depth of Explanation: ...
    - Examples: ...
    - Logical Flow: ...
    """

    candidate_responses = "\n\n".join(
        [f"Q: {entry['question']}\nA: {entry['response']}" for entry in st.session_state["answers"]]
    )

    full_prompt = f"{prompt}\n\nCandidate Responses:\n{candidate_responses}"
    response = model.generate_content(full_prompt)

    st.session_state["evaluation_feedback"] = response.text.strip()
    
    score_match = re.search(r"\*\*Overall Score:\*\* (\d+)/10", response.text)
    if score_match:
        st.session_state["overall_score"] = int(score_match.group(1))
        st.session_state["percentage_score"] = st.session_state["overall_score"] * 10
    else:
        st.session_state["overall_score"] = 0
        st.session_state["percentage_score"] = 0

# Function to convert question to speech
def speak_question(question):
    if not st.session_state["question_played"]:
        tts = gTTS(text=question, lang="en")
        tts.save("question.mp3")
        st.audio("question.mp3", format="audio/mp3", autoplay=True)
        st.session_state["question_played"] = True

# Streamlit UI Enhancements
st.set_page_config(page_title="🔥🎯 GrillMaster", layout="wide")
st.sidebar.header("🔥🎯 Imarticus GrillMaster")

num_questions = st.sidebar.slider("Number of Questions:", 1, 10, 3)
difficulty_level = st.sidebar.selectbox("Select Difficulty Level:", ["Beginner", "Intermediate", "Advance"])
section_choice = st.sidebar.radio("Choose Input Type:", ("Resume", "Job Description", "Skills"))

input_text = ""
if section_choice == "Resume":
    uploaded_file = st.sidebar.file_uploader("Upload Resume:", type=["pdf", "txt"])
    if uploaded_file:
        input_text = extract_pdf_text(uploaded_file)
elif section_choice == "Job Description":
    input_text = st.sidebar.text_area("Paste Job Description:")
elif section_choice == "Skills":
    input_text = st.sidebar.selectbox("Select a Skill:", ["Python", "SQL", "Machine Learning", "Statistics", "Business Analytics"])

if st.sidebar.button("Generate Questions"):
    prompt =f"""
        You are a trainer conducting a beginner-level mock interview.  
        Ask {num_questions} direct and fundamental questions related to {input_text}.  
        Keep them concise and focused on key concepts, similar to:  
        - Difference between DDL and DML  
        - Types of Joins in SQL  
        - Primary Key vs Foreign Key  
        - Loops in Python  

        Ensure the questions are clear, to the point, and suitable for a {difficulty_level} level interview. 
    """
    
    st.session_state["generated_questions"] = get_questions(prompt, input_text, num_questions)
    st.session_state["current_question_index"] = 0
    st.session_state["answers"] = []
    st.session_state["evaluation_feedback"] = ""
    st.session_state["question_played"] = False
    st.rerun()

# Display Current Question
if st.session_state["generated_questions"]:
    q_index = st.session_state["current_question_index"]
    
    if q_index < len(st.session_state["generated_questions"]):
        question = st.session_state["generated_questions"][q_index]
        
        #st.subheader(f"Q{q_index + 1}:")
        st.write(f"**{question}**", unsafe_allow_html=True)
        
        speak_question(question)  
        
        recognizer = sr.Recognizer()
        
        if st.button("Start Recording"):
            st.session_state["is_recording"] = True
            with sr.Microphone() as source:
                st.write("🎤 Listening... Speak your answer.")
                try:
                    audio = recognizer.listen(source, timeout=60)
                    response_text = recognizer.recognize_google(audio)
                    st.session_state["answers"].append({"question": question, "response": response_text})
                    st.session_state["is_recording"] = False
                except sr.UnknownValueError:
                    st.write("Could not understand the audio. Try again.")
        
        if st.button("Stop Recording"):
            st.session_state["is_recording"] = False
        
        if st.button("➡️ Next Question"):
            st.session_state["current_question_index"] += 1
            st.session_state["question_played"] = False
            st.rerun()
    else:
        evaluate_answers()
        
        st.subheader("📊 Complete Mock Interview Summary")
        st.write(f"**Overall Score:** {st.session_state['overall_score']} / 10")
        st.write(f"**Percentage Score:** {st.session_state['percentage_score']:.2f}%")
        st.progress(st.session_state["percentage_score"] / 100)
        st.write(st.session_state["evaluation_feedback"], unsafe_allow_html=True)