Delete app.py

app.py

@@ -1,176 +0,0 @@
-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
-for key, default in {
-    "generated_questions": [],
-    "current_question_index": 0,
-    "answers": [],
-    "evaluation_feedback": "",
-    "overall_score": 0,
-    "percentage_score": 0,
-    "is_recording": False,
-    "question_played": False,
-    "selected_domain": ""
-}.items():
-    if key not in st.session_state:
-        st.session_state[key] = default
-
-# Extract text from PDF
-def extract_pdf_text(uploaded_file):
-    pdf_reader = PyPDF2.PdfReader(uploaded_file)
-    return "".join(page.extract_text() or "" for page in pdf_reader.pages).strip()
-
-# Generate 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 candidate answers
-def evaluate_answers():
-    model = genai.GenerativeModel('gemini-1.5-pro-latest')
-    
-    prompt = """
-    You are an expert interview evaluator. Assess responses based on:
-    - Conceptual Understanding
-    - Communication Skills
-    - Clarity & Depth of Explanation
-    - Use of Real-World Examples
-    - Logical Flow
-
-    Provide a score (out of 10) and an evaluation summary.
-    
-    **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)
-    st.session_state["overall_score"] = int(score_match.group(1)) if score_match else 0
-    st.session_state["percentage_score"] = st.session_state["overall_score"] * 10
-
-# Convert question to speech and allow manual playback
-def generate_question_audio(question):
-    tts = gTTS(text=question, lang="en")
-    tts.save("question.mp3")
-
-# Streamlit UI
-st.set_page_config(page_title="GrillMaster", layout="wide")
-st.sidebar.header("Imarticus GrillMaster")
-
-# Domain selection
-st.sidebar.subheader("Select Interview Domain:")
-col1, col2 = st.sidebar.columns(2)
-
-if col1.button("Analytics"):
-    st.session_state["selected_domain"] = "Analytics"
-if col2.button("Finance"):
-    st.session_state["selected_domain"] = "Finance"
-
-if not st.session_state["selected_domain"]:
-    st.warning("Please select a domain to proceed.")
-    st.stop()
-
-# Sidebar options
-section_choice = st.sidebar.radio("Choose Input Type:", ("Resume", "Job Description", "Skills"))
-
-# Skill options based on domain
-if st.session_state["selected_domain"] == "Analytics":
-    skill_options = ["Python", "SQL", "Machine Learning", "Statistics", "Business Analytics"]
-elif st.session_state["selected_domain"] == "Finance":
-    skill_options = ["Fund Accounting", "Investment Banking", "Derivatives"]
-
-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:", skill_options)
-
-difficulty_level = st.sidebar.selectbox("Select Difficulty Level:", ["Beginner", "Intermediate", "Advanced"])
-num_questions = st.sidebar.slider("Number of Questions:", 1, 10, 3)
-
-
-
-# Generate Questions
-if st.sidebar.button("Generate Questions"):
-    prompt = f"""
-        You are an expert interviewer conducting a {difficulty_level} level {st.session_state['selected_domain']} interview.  
-        Ask {num_questions} clear and direct {st.session_state['selected_domain']}-related questions.
-    """
-
-    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]
-        generate_question_audio(question)
-        st.audio("question.mp3", format="audio/mp3")
-        st.write(f"**{question}**", unsafe_allow_html=True)
-
-        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)