Upload new_app.py

new_app.py

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+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)