Temp

PadBot_api/.gitignore

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+.env
+
+venv/
+
+mod5-2.pdf

PadBot_api/app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import fitz  # PyMuPDF
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_together import TogetherEmbeddings, Together
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import PromptTemplate
+import google.generativeai as genai
+import requests
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+app = Flask(__name__)
+CORS(app, origins=["*"])
+
+
+
+# Load API Keys
+TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+
+# Gemini setup
+genai.configure(api_key=GEMINI_API_KEY)
+gemini_model = genai.GenerativeModel("gemini-2.5-flash")
+
+if not TOGETHER_API_KEY:
+    raise Exception("TOGETHER_AI_API_KEY not found in environment variables.")
+if not GEMINI_API_KEY:
+    raise Exception("GEMINI_API_KEY not found in environment variables.")
+
+VECTOR_STORE = None
+RETRIEVAL_CHAIN = None
+
+# -----------------------------------------
+# PDF Parsing and LangChain QA Chain Setup
+# -----------------------------------------
+def extract_pdf_text(file_bytes):
+    with fitz.open(stream=file_bytes, filetype="pdf") as doc:
+        return "".join(page.get_text() for page in doc)
+
+def process_and_store(pdf_text):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    chunks = splitter.split_text(pdf_text)
+
+    embeddings = TogetherEmbeddings(
+        model="togethercomputer/m2-bert-80M-32k-retrieval",
+        together_api_key=TOGETHER_API_KEY
+    )
+    vector_store = Chroma.from_texts(chunks, embeddings)
+
+    llm = Together(
+        model="meta-llama/Llama-3-8b-chat-hf",
+        temperature=0,
+        max_tokens=1024,
+        together_api_key=TOGETHER_API_KEY
+    )
+
+    retriever = vector_store.as_retriever(search_kwargs={"k": 5})
+    prompt_template = """
+    Answer the question based only on the following context in brief.
+    If you don't know the answer, just say that you don't know.
+
+    CONTEXT:
+    {context}
+
+    QUESTION:
+    {input}
+
+    ANSWER:
+    """
+    prompt = PromptTemplate.from_template(prompt_template)
+    qa_chain = create_stuff_documents_chain(llm, prompt)
+    retrieval_chain = create_retrieval_chain(retriever, qa_chain)
+
+    return vector_store, retrieval_chain
+
+# -------------------------------
+# Gemini Skill Analysis Functions
+# -------------------------------
+def fetch_github_summary(github_url):
+    try:
+        username = github_url.rstrip("/").split("/")[-1]
+        api_url = f"https://api.github.com/users/{username}/repos"
+        repos = requests.get(api_url).json()
+
+        summary = f"GitHub user: {username}\n"
+        for repo in repos[:5]:
+            summary += f"- {repo['name']}: {repo.get('description', 'No description')}\n"
+        return summary
+    except Exception as e:
+        return f"Error fetching GitHub info: {str(e)}"
+
+def analyze_with_gemini(resume_text, github_summary, desired_role):
+    prompt = f"""
+You are a career coach. A user wants to become a {desired_role}.
+Based on the resume and GitHub data, analyze:
+
+1. Skills they already have
+2. Skills they lack
+3. A short learning roadmap
+
+Resume:
+{resume_text}
+
+GitHub:
+{github_summary}
+"""
+    response = gemini_model.generate_content(prompt)
+    return response.text
+
+# -------------------------------
+# Routes
+# -------------------------------
+@app.route("/upload", methods=["POST"])
+def upload_pdf():
+    global VECTOR_STORE, RETRIEVAL_CHAIN
+
+    if 'file' not in request.files:
+        return jsonify({"error": "No file provided"}), 400
+
+    file = request.files['file']
+    if not file or file.filename == '':
+        return jsonify({"error": "Invalid file"}), 400
+
+    try:
+        pdf_text = extract_pdf_text(file.read())
+        VECTOR_STORE, RETRIEVAL_CHAIN = process_and_store(pdf_text)
+        return jsonify({"message": "File processed and stored"}), 200
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/ask", methods=["POST"])
+def ask_question():
+    global RETRIEVAL_CHAIN
+    if RETRIEVAL_CHAIN is None:
+        return jsonify({"error": "Please upload a PDF first"}), 400
+
+    data = request.get_json()
+    question = data.get("question", "").strip()
+    if not question:
+        return jsonify({"error": "No question provided"}), 400
+
+    try:
+        response = RETRIEVAL_CHAIN.invoke({"input": question})
+        return jsonify({"answer": response["answer"]}), 200
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/submit_preferences', methods=['POST'])
+def submit_preferences():
+    try:
+        role = request.form.get("role", "").strip()
+        company = request.form.get("company", "").strip()
+        github_link = request.form.get("githubLink", "").strip()
+        resume_file = request.files.get("resumeFile")
+
+        # ✅ Field validation
+        if not (role and company and github_link and resume_file):
+            print("❌ Missing fields:", role, company, github_link, resume_file)
+            return jsonify({"error": "Missing required fields"}), 400
+
+        # ✅ Debug info
+        print(f"\n📥 Received Submission:")
+        print(f"  - 📄 Resume: {resume_file.filename} ({resume_file.content_type})")
+        print(f"  - 🎯 Role: {role}")
+        print(f"  - 🏢 Company: {company}")
+        print(f"  - 🐙 GitHub: {github_link}\n")
+
+        # ✅ Process data
+        pdf_text = extract_pdf_text(resume_file.read())
+        github_summary = fetch_github_summary(github_link)
+        gemini_analysis = analyze_with_gemini(pdf_text, github_summary, role)
+
+        print(f"✅ Gemini Analysis:\n{gemini_analysis}\n")
+
+        return jsonify({
+            "message": "Analysis complete!",
+            "analysis": gemini_analysis
+        }), 200
+
+    except Exception as e:
+        return jsonify({"error": f"Internal server error: {str(e)}"}), 500
+
+if __name__ == "__main__":
+    app.run(debug=True)

PadBot_api/requirements.txt

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+Flask
+PyMuPDF
+langchain
+langchain-community
+langchain-together
+chromadb
+python-dotenv  

README.md

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 # CODERECET
 
 ## Project Repository
-*Commit and save your changes here*
+https://github.com/devananda6200/CODERECET-ByteWizards
 
 ### Team Name :
+ByteWizards
+
 ### Team Members :
+- Deva Nanda Nair  
+- Swathi H Nair 
+- Shreya R S 
+
 ### Project Description
-.
-.
-.
+**Project Padhik (പഥികൻ)** is a dual-purpose AI-powered student ecosystem designed specifically for the educational and employment challenges faced by students in Kerala. It consists of two integrated pillars:
+
+1. **Academic Accelerator (EXAM AI Wing)** – helps students boost CGPA and ace university exams using AI-driven PYQ solving, smart note analysis, and curated YouTube learning playlists.
+2. **Career Navigator (Original Padhik)** – analyzes resumes and GitHub profiles to identify skill gaps and suggests personalized learning paths and hyper-local job opportunities.
+
+It empowers students from their **first semester to their first job**—making Padhik the ultimate Kerala student companion.
+
+---
 
 ## Technical Details
 
 ### Technologies/Components Used
 
 ## For Software:
 
-[Languages used]
-[Frameworks used]
-[Libraries used]
-[Tools used]
-
-## For Hardware:
-
-[List main components]
-[List specifications]
-[List tools required]
+**Languages used:**
+- Python  
+- JavaScript  
+- HTML/CSS  
+
+**Frameworks used:**
+- Flask (Backend)  
+- Svelte + Astro + Tailwind CSS (Frontend)  
+
+**Libraries used:**
+- LangChain  
+- Together.ai  
+- Google Generative AI (Gemini)  
+- PyMuPDF (fitz)  
+- Chroma (Vector Store)  
+- SymPy (Math solver)  
+- dotenv  
+- Flask-CORS  
+- Requests  
+
+**Tools used:**
+- GitHub  
+- VS Code  
+- Postman  
+- Gamma for Deck  
+- Figma (UI mockups)  
+- YouTube Data API  
+- LinkedIn API (planned)
+
+---
+
+[List specifications]  
+- Runs on any machine with Python 3.10+  
+- Tested on Google Colab, local Ubuntu 22.04, and Windows 10  
+
+[List tools required]  
+- Python + pip  
+- .env file for API keys  
+
+---
 
 ## Implementation
 
 ## For Software:
 
 ### Installation
-[commands]
+```bash
+git clone https://github.com/devananda6200/CODERECET-ByteWizards
+cd CODERECET-ByteWizards
+python -m venv env
+source env/bin/activate  # for Linux/macOS
+# or
+env\Scripts\activate  # for Windows
+
+pip install -r requirements.txt
+# Add your .env file with GEMINI_API_KEY and TOGETHER_API_KEY
+python app.py
+# Then open the frontend (Svelte project)
+cd frontend
+npm install
+npm run dev
+```
 
-### Run
-[commands]
+### Screenshots (Add at least 3)
 
-### Project Documentation
+![Dashboard](dashboard.png)  
+*Main dashboard allowing users to choose between "Boost My Grades" (XMAI) or "Build My Career" (Navigator).*
 
-### Screenshots (Add at least 3)
+![PYQ Solver](pyq_solver.png)
+![PYQ Solver1](pyq2.png)
+![PYQ Solver](padbot.png)  
+*User uploads a PDF of previous year questions and receives step-by-step AI-generated solutions.*
+
+![Career Analysis Result](career.png)  
+   *Caption: Gemini AI analyzing resume + GitHub and generating skill gap and career roadmap.*
+
+---
 
 ### Diagrams
-Workflow(Add your workflow/architecture diagram here) Add caption explaining your workflow
 
-## For Hardware:
+#### Workflow (Architecture Diagram)
+
+![Workflow Architecture](https://via.placeholder.com/800x450.png?text=Workflow+Diagram+Coming+Soon)  
 
-### Schematic & Circuit
-Circuit(Add your circuit diagram here) Add caption explaining connections
-Schematic(Add your schematic diagram here) Add caption explaining the schematic
 
-### Build Photos
-Components(Add photo of your components here) List out all components shown
-Build(Add photos of build process here) Explain the build steps
-Final(Add photo of final product here) Explain the final build
 
 ### Project Demo
 
 ### Video
-[Add your demo video link here] Explain what the video demonstrates
 
-## Additional Demos
-[Add any extra demo materials/links]
+[https://youtu.be/sample-demo-link](https://youtube.com/shorts/78PsVvvoW5Y?feature=share)  
+
 
 ## Team Contributions
-[Name 1]: [Specific contributions]
-[Name 2]: [Specific contributions]
-[Name 3]: [Specific contributions]
+
+**Deva Nanda Nair:**    
+- Core backend logic and integration with Together AI
+- Flask endpoints and Gemini integration
+
+**Shreya R S:**  
+- Career Navigator logic 
+- EXAM AI  Wing Setup
+
+**Swathi H Nair:**  
+- Frontend design and implementation (Svelte, Astro, Tailwind)  
+
+
+