Create app.py

app.py

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+# ✅ Step 1: Install Required Libraries
+# !pip install transformers gradio
+
+# ✅ Step 1: Import Libraries
+import gradio as gr
+from transformers import pipeline
+import random
+
+# ✅ Step 2: Setup Behavioral Analysis Pipeline
+qa_classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+def analyze_behavioral_response(response):
+    result = qa_classifier(response)[0]
+    label = result['label']
+    score = result['score']
+    feedback = "✅ Good answer!" if label == "POSITIVE" and score > 0.8 else "⚠️ Try being more specific or structured."
+    return f"Sentiment: {label}, Confidence: {score:.2f}\n\nFeedback: {feedback}"
+
+# ✅ Step 3: Code Evaluation Logic
+def evaluate_code(code, expected_keywords_string):
+    expected_keywords = expected_keywords_string.split(',')
+    feedback = []
+
+    try:
+        local_env = {}
+        exec(code, {}, local_env)
+        feedback.append("✅ Code executed without errors.")
+    except Exception as e:
+        return f"❌ Code Error: {e}"
+
+    for keyword in expected_keywords:
+        keyword = keyword.strip()
+        if keyword and keyword not in code:
+            feedback.append(f"⚠️ Missing expected keyword or logic: `{keyword}`")
+
+    if len(feedback) == 1:
+        feedback.append("✅ Code looks good!")
+
+    return "\n".join(feedback)
+
+# ✅ Step 4: Question Banks
+technical_questions = {
+    "frontend": {
+        "beginner": [
+            ("Create a simple HTML page with a form input and a submit button.", "html,form,input,submit"),
+            ("Write CSS to center a div both vertically and horizontally.", "css,center,flex,justify-content,align-items"),
+            ("Add JavaScript to change a button color on hover.", "javascript,hover,style,backgroundColor"),
+            ("Create a dropdown menu using HTML and CSS.", "select,option,html,css"),
+            ("Implement a click counter using JavaScript.", "javascript,click,addEventListener,counter")
+        ],
+        "intermediate": [
+            ("Create a responsive navbar using CSS Flexbox.", "nav,flex,media query,css"),
+            ("Build a modal popup using HTML, CSS, and JavaScript.", "modal,html,css,display,show"),
+            ("Create a dark mode toggle using JavaScript.", "toggle,classList,dark,theme"),
+            ("Use JavaScript to validate a user registration form.", "validation,javascript,form,check"),
+            ("Dynamically render a list of items using JavaScript.", "list,forEach,appendChild,createElement")
+        ],
+        "advanced": [
+            ("Build a simple React component that fetches and displays API data.", "react,useEffect,fetch,setState"),
+            ("Implement a Redux-like state management system in vanilla JS.", "store,dispatch,subscribe,action"),
+            ("Build a React app with routing using React Router.", "react-router,Route,Link,BrowserRouter"),
+            ("Create a drag-and-drop component using plain JavaScript.", "dragstart,dragover,drop,addEventListener"),
+            ("Optimize performance of a React list using memoization.", "memo,useMemo,useCallback,react")
+        ]
+    },
+    "backend": {
+        "beginner": [
+            ("Write a Flask app with one route that returns 'Hello World'.", "flask,route,hello"),
+            ("Create a Python function to hash a password using bcrypt.", "bcrypt,hashpw,password"),
+            ("Set up a SQLite database connection in Flask.", "sqlite3,connect,flask"),
+            ("Return JSON data from a Flask endpoint.", "jsonify,flask,response"),
+            ("Create a POST endpoint in Flask to accept user data.", "POST,request,form,json")
+        ],
+        "intermediate": [
+            ("Implement JWT-based authentication in Flask.", "jwt,token,authorization,headers"),
+            ("Build a REST API with Flask that supports CRUD for a 'Book' model.", "GET,POST,PUT,DELETE,book"),
+            ("Set up input validation in Flask using Marshmallow.", "Schema,fields,validate,load"),
+            ("Write a middleware to log all incoming requests in Flask.", "before_request,logging"),
+            ("Implement pagination in a Flask REST API.", "limit,offset,page")
+        ],
+        "advanced": [
+            ("Build a Flask app with role-based user authentication.", "roles,login_required,admin"),
+            ("Integrate OAuth 2.0 login in a Flask app.", "oauth,redirect,authorize"),
+            ("Implement rate limiting for an API endpoint.", "rate limit,requests,time"),
+            ("Secure a Flask app against SQL injection and XSS.", "escape,parameterized,security"),
+            ("Build a background task queue using Celery with Flask.", "celery,task,delay")
+        ]
+    },
+    "fullstack": {
+        "beginner": [
+            ("Build a simple HTML form and process the data using Flask.", "form,action,flask,request"),
+            ("Create a frontend app that fetches data from a local Flask API.", "fetch,api,flask,json"),
+            ("Make a contact form that stores submissions to a backend database.", "form,submit,db,flask"),
+            ("Style a Flask app template using Bootstrap.", "bootstrap,template,link"),
+            ("Build a user login page (frontend + backend).", "login,form,flask,validation")
+        ],
+        "intermediate": [
+            ("Build a fullstack app with a frontend form and backend data processing.", "form,flask,submit,backend"),
+            ("Create a RESTful API in Flask and connect it with a React frontend.", "fetch,react,flask,route"),
+            ("Implement session management in Flask and use it on the frontend.", "session,cookies,login"),
+            ("Build a to-do app with persistent data storage (frontend + backend).", "todo,add,delete,db"),
+            ("Add error handling on both frontend and backend for a data submission app.", "try,catch,error,except")
+        ],
+        "advanced": [
+            ("Deploy a fullstack Flask + React app with Docker.", "dockerfile,compose,react,flask"),
+            ("Add real-time updates with WebSockets.", "websocket,emit,socketio"),
+            ("Secure your app using HTTPS and Content Security Policy headers.", "https,csp,headers"),
+            ("Build a multi-user app with different access levels.", "roles,auth,permissions"),
+            ("Optimize frontend-backend performance using caching and batching.", "cache,batch,api")
+        ]
+    },
+    "ai engineer": {
+        "beginner": [
+            ("Write a Python function to tokenize and clean a text string.", "tokenize,lower,strip"),
+            ("Use Hugging Face Transformers to load a sentiment analysis model.", "pipeline,sentiment-analysis,transformers"),
+            ("Load and process a CSV dataset using pandas.", "pandas,read_csv,head"),
+            ("Write a function to compute TF-IDF of a list of documents.", "TfidfVectorizer,fit_transform"),
+            ("Plot a confusion matrix from predicted and true labels.", "confusion_matrix,seaborn,heatmap")
+        ],
+        "intermediate": [
+            ("Build a sentiment classifier using logistic regression on text data.", "LogisticRegression,fit,predict"),
+            ("Fine-tune a pre-trained transformer model on a custom dataset.", "Trainer,training_args,model"),
+            ("Train and evaluate a simple neural network using PyTorch.", "nn.Module,forward,loss"),
+            ("Implement a spam classifier using scikit-learn.", "CountVectorizer,MultinomialNB"),
+            ("Visualize word embeddings using t-SNE or PCA.", "TSNE,PCA,scatter")
+        ],
+        "advanced": [
+            ("Create a custom Hugging Face dataset loader.", "datasets,load_dataset"),
+            ("Implement a transformer architecture from scratch.", "attention,layernorm,embedding"),
+            ("Use RLHF to fine-tune a language model for a specific task.", "reinforcement learning,human feedback"),
+            ("Build a QA system using Haystack or LangChain.", "haystack,pipeline,Retriever"),
+            ("Implement multi-GPU training in PyTorch.", "DataParallel,nn,model")
+        ]
+    },
+    "data scientist": {
+        "beginner": [
+            ("Load and summarize a CSV dataset using pandas.", "pandas,read_csv,describe"),
+            ("Plot a histogram and boxplot using seaborn.", "histplot,boxplot,seaborn"),
+            ("Clean missing values in a DataFrame.", "fillna,dropna"),
+            ("Create a correlation heatmap of numeric columns.", "corr,heatmap,seaborn"),
+            ("Group data and calculate mean using pandas groupby.", "groupby,mean")
+        ],
+        "intermediate": [
+            ("Build and evaluate a linear regression model.", "LinearRegression,fit,score"),
+            ("Apply feature scaling and normalization using scikit-learn.", "StandardScaler,transform"),
+            ("Train a decision tree and plot its structure.", "DecisionTreeClassifier,plot_tree"),
+            ("Perform K-means clustering on a dataset.", "KMeans,fit,predict"),
+            ("Use cross-validation to evaluate a model.", "cross_val_score,KFold")
+        ],
+        "advanced": [
+            ("Build an end-to-end pipeline with feature engineering and model selection.", "Pipeline,FeatureUnion,GridSearchCV"),
+            ("Apply grid search for hyperparameter tuning.", "GridSearchCV,param_grid"),
+            ("Detect and handle outliers in a dataset.", "zscore,IQR"),
+            ("Use XGBoost for a classification task.", "XGBClassifier,fit,predict"),
+            ("Deploy a model using Flask and expose it as an API.", "Flask,route,predict")
+        ]
+    },
+    "devops": {
+        "beginner": [
+            ("Write a Dockerfile for a simple Python app.", "FROM,WORKDIR,COPY,CMD"),
+            ("Create a GitHub Actions workflow to run tests.", "yaml,actions,run"),
+            ("Set up a local development environment using Docker Compose.", "docker-compose,services"),
+            ("Write a shell script to install dependencies.", "bash,apt-get,yum"),
+            ("Configure environment variables securely.", ".env,os.environ")
+        ],
+        "intermediate": [
+            ("Deploy a Python web app using Docker and NGINX.", "nginx,docker,proxy"),
+            ("Set up CI/CD pipeline using GitHub Actions.", "jobs,workflow,deploy"),
+            ("Monitor server logs and system metrics.", "logging,metrics,monitoring"),
+            ("Write an Ansible playbook to install and start a service.", "ansible,playbook,task"),
+            ("Implement blue-green deployment in a staging environment.", "blue,green,switch,rollback")
+        ],
+        "advanced": [
+            ("Configure autoscaling for a cloud-based service.", "autoscale,cloudwatch,asg"),
+            ("Secure infrastructure using IAM roles and policies.", "iam,policy,role"),
+            ("Use Terraform to provision cloud infrastructure.", "terraform,resource,provider"),
+            ("Set up Prometheus and Grafana for monitoring.", "prometheus,grafana,metrics"),
+            ("Implement centralized logging using ELK stack.", "elasticsearch,logstash,kibana")
+        ]
+    }
+}
+
+behavioral_questions = [
+    "Tell me about a time you had a conflict in a team.",
+    "Describe a situation where you missed a deadline.",
+    "How do you handle receiving negative feedback?",
+    "Share a moment you led a team under pressure.",
+    "Tell me about a mistake you made and what you learned."
+]
+
+# ✅ Step 6: App Logic for Question Navigation
+session = {
+    "role": None,
+    "tech_index": 0,
+    "behavior_index": 0,
+    "tech_qs": [],
+}
+
+def start_session(selected_role):
+    session["role"] = selected_role
+    session["tech_index"] = 0
+    session["behavior_index"] = 0
+
+    # Get exactly 5 beginner, 5 intermediate, 5 advanced in order
+    beginner_qs = technical_questions[selected_role]["beginner"][:5]
+    intermediate_qs = technical_questions[selected_role]["intermediate"][:5]
+    advanced_qs = technical_questions[selected_role]["advanced"][:5]
+
+    session["tech_qs"] = beginner_qs + intermediate_qs + advanced_qs
+
+    first_q, hints = session["tech_qs"][0]
+    session["tech_index"] = 1
+    return f"Ready for your {selected_role.upper()} interview!\nStarting with Beginner Level Questions.", first_q, hints, "", ""
+
+def next_tech_question():
+    if session["tech_index"] < 15:
+        q, hints = session["tech_qs"][session["tech_index"]]
+        session["tech_index"] += 1
+        return q, hints, "", ""
+    else:
+        return "🎉 All technical questions completed!", "", "", ""
+
+def next_behavioral_question():
+    if session["behavior_index"] < 5:
+        q = behavioral_questions[session["behavior_index"]]
+        session["behavior_index"] += 1
+        return q, ""
+    else:
+        return "🎉 All behavioral questions completed!", ""
+
+# ✅ Step 6: Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 👩‍💻 Tech Interview Practice")
+    gr.Markdown("Choose a developer role and get 15 technical + 5 behavioral interview questions with instant AI feedback.")
+
+    role = gr.Dropdown(choices=list(technical_questions.keys()), label="Select Developer Role")
+    start_btn = gr.Button("Start Interview")
+    start_msg = gr.Textbox(label="Session Status")
+
+    with gr.Tab("💻 Technical Interview"):
+        tech_q = gr.Textbox(label="Technical Question", interactive=False)
+        tech_hint = gr.Textbox(label="Expected Keywords (For Feedback)", interactive=False)
+        tech_code = gr.Code(label="Your Code")
+        tech_submit = gr.Button("Submit Code")
+        tech_feedback = gr.Textbox(label="AI Feedback")
+        tech_next = gr.Button("Next Question")
+
+    with gr.Tab("🧠 Behavioral Interview"):
+        beh_q = gr.Textbox(label="Behavioral Question", interactive=False)
+        beh_answer = gr.Textbox(label="Your Answer", lines=5)
+        beh_submit = gr.Button("Submit Answer")
+        beh_feedback = gr.Textbox(label="AI Feedback")
+        beh_next = gr.Button("Next Question")
+
+    # Bind Events
+    start_btn.click(fn=start_session, inputs=role, outputs=[start_msg, tech_q, tech_hint, tech_code, tech_feedback])
+    tech_next.click(fn=next_tech_question, outputs=[tech_q, tech_hint, tech_code, tech_feedback])
+    tech_submit.click(fn=evaluate_code, inputs=[tech_code, tech_hint], outputs=tech_feedback)
+    beh_next.click(fn=next_behavioral_question, outputs=[beh_q, beh_feedback])
+    beh_submit.click(fn=analyze_behavioral_response, inputs=beh_answer, outputs=beh_feedback)
+
+demo.launch(share=True)