Rithish

.gitignore

@@ -0,0 +1,8 @@
+flask/.env
+node/.env
+node/node_modules
+.env
+__pycache__
+flask_backend/__pycache__
+flask_backend/src/__pycache__
+flask_backend/src/data

README.md

@@ -1,53 +1,52 @@
-
 # Define 3.0
 The official template repository for Define 3.0
 
 ![DefineHack 2025 Logo](https://github.com/user-attachments/assets/8173bc16-418e-4912-b500-c6427e4ba4b6)
 
 
 
-# < Project Name >
+# FlowFi
  Cover Image  If applicable
 
 ### Team Information
-- **Team Name**: <Team Name> 
-- **Track**: < Track >
+- Team Name: Strike4 
+- Track: Fintech
 
 ### Team Members
 | Name | Role | GitHub | LinkedIn |
 |------|------|--------|----------|
-| [Full Name] | [Role] | [@username](https://github.com/username) | [Profile](https://linkedin.com/in/username) |
-| [Full Name] | [Role] | [@username](https://github.com/username) | [Profile](https://linkedin.com/in/username) |
-| [Full Name] | [Role] | [@username](https://github.com/username) | [Profile](https://linkedin.com/in/username) |
-| [Full Name] | [Role] | [@username](https://github.com/username) | [Profile](https://linkedin.com/in/username) |
+| [Gokul G] | [Frontend Developer] | [@GOKULGSECE](https://github.com/GOKULGSECE) | [Profile](https://linkedin.com/in/gokul-g-760a23259) |
+| [Rithish S] | [Backend Developer] | [@rithish5513u](https://github.com/rithish5513u) | [Profile](https://linkedin.com/in/rithish-s) |
+| [Rishab S] | [Backend Developer] | [@rishabtr](https://github.com/rishabtr) | [Profile](https://linkedin.com/in/rishab-s-0aa28b21a) |
+| [Tamilkumaran N] | [Front End Developer] | [@tamilkumaran17](https://github.com/tamilkumaran17) | [Profile](https://linkedin.com/in/tamilkumarann) |
 
 ## Project Details
 
 ### Overview
-_A concise summary of your project (2-3 sentences)_
+We're building a fintech platform with an AI-powered chatbot to provide financial insights and customer support. Our platform pulls data from publicly available APIs to deliver personalized financial news. With a user-friendly dashboard, you can track your finances, analyze trends, and manage a virtual investment portfolio. Plus, you can upload your bank statements for automatic income and expense analysis. Our goal is to make financial management easier, helping users make smarter money decisions.
 
 ### Problem Statement
-_Describe the problem your project aims to solve_
+Develop a basic prototype of a fintech website with an AI-powered chatbot that provides users with financial insights and customer support. The website should have a clean, user-friendly interface with a simple dashboard displaying sample financial data and a chatbot capable of handling basic FAQs related to fintech services.
 
 ### Solution
-_Explain your approach to solving the problem_
+We're building a fintech platform with an AI-powered chatbot to provide financial insights and customer support. Our platform pulls data from publicly available APIs to deliver personalized financial news. With a user-friendly dashboard, you can track your finances, analyze trends, and manage a virtual investment portfolio. Plus, you can upload your bank statements for automatic income and expense analysis. Our goal is to make financial management easier, helping users make smarter money decisions.
 
 ### Demo
 [![Project Demo](https://img.youtube.com/vi/VIDEO_ID/0.jpg)](https://www.youtube.com/watch?v=VIDEO_ID)
-_Replace VIDEO_ID with your YouTube video ID or provide an alternative demo link_
+Replace VIDEO_ID with your YouTube video ID or provide an alternative demo link
 
 ### Live Project
-[Project Name](https://your-project-url.com)
+[Project Name]FlowFii
 
 ## Technical Implementation
 
 ### Technologies Used
-- **Frontend**: [Technologies]
-- **Backend**: [Technologies]
-- **Database**: [Technologies]
-- **APIs**: [Technologies]
-- **DevOps**: [Technologies]
-- **Other Tools**: [Technologies]
+- Frontend: ReactJS
+- Backend: NodeJS/Flask
+- Database: MongoDB
+- APIs: [Technologies]
+- DevOps: [Technologies]
+- Other Tools: [Technologies]
 
 ### Key Features
 - Feature 1
@@ -62,25 +61,25 @@ _Replace VIDEO_ID with your YouTube video ID or provide an alternative demo link
 - Requirement 3
 
 ### Installation 
-```bash
+bash
+
 
-```
 
 ### Running the Project
-```bash
+bash
+
 
-```
 
 ## Additional Resources
 
 ### Project Timeline
-_Brief overview of your development process and milestones_
+Brief overview of your development process and milestones
 
 ### Challenges Faced
-_Discuss technical challenges and how you overcame them_
+Discuss technical challenges and how you overcame them
 
 ### Future Enhancements
-_Share your vision for future development_
+Share your vision for future development
 
 ### References (if any)
 - [Reference 1](link)

flask_backend/main.py

@@ -0,0 +1,40 @@
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from src.news import News
+from src.faq_chatbot import FAQChatBot
+from src.excelhandler import ExcelHandler
+
+app = Flask(__name__)
+CORS(app)
+
+@app.route('/')
+def home():
+    return "Welcome to FlowFi!"
+
+@app.route('/financialInsights', methods = ['POST'])
+def extract_news():
+    # used to extract the news based on user preferences
+    data = request.json
+    preferences = data['preferences']
+    news = News()
+    news_data = news.get_everything(preferences)
+    return jsonify(news_data)
+
+@app.route('/faqHandler', methods = ['POST'])
+def faq_handler():
+    # used to access the FAQ chatbot
+    data = request.json
+    user_query = data['user_query']
+    chatbot = FAQChatBot()
+    response = chatbot.get_financial_insight(user_query)
+    return jsonify({"response": response})
+
+@app.route('/triggerExcel', methods = ['POST'])
+def trigger_excel():
+    # used to convert the excel file to json
+    excel = ExcelHandler()
+    data = excel.get_json()
+    return jsonify(data)
+
+if __name__ == '__main__':
+    app.run(debug = True, port = 8080)

flask_backend/requirements.txt

@@ -0,0 +1,9 @@
+flask
+flask-cors
+openai
+numpy
+pandas
+newsapi-python
+google-generativeai
+xlrd
+yfinance

flask_backend/src/excelhandler.py

@@ -0,0 +1,90 @@
+import pandas as pd
+import os
+
+class ExcelHandler:
+    def __init__(self):
+        self.folder_path = "flask_backend/src/data/"
+        self.required_columns = ['date', 'description', 'withdrawals', 'deposits', 'balance']
+
+    def getFiles(self):
+        """
+        Function to get the files from the folder
+        Inputs:
+            None
+        Outputs:
+            List of files
+        """
+        files = os.listdir(self.folder_path)
+        files = [
+            os.path.join(self.folder_path, f) for f in files 
+            if (f.endswith(".xlsx") or f.endswith(".xls")) and os.path.isfile(os.path.join(self.folder_path, f))
+        ]
+        return files
+
+    def validateExcel(self, file):
+        """
+        Function to validate the excel file
+        Inputs:
+            file -> str
+        Outputs:
+            boolean value
+        """
+        data = pd.read_excel(file)
+        columns = data.columns
+        columns = [column.lower() for column in columns]
+        for column in self.required_columns:
+            if column not in columns:
+                return False
+        return True
+
+    def getData(self, file):
+        """
+        Function to get the data from the excel file
+        Inputs:
+            file -> str
+        Outputs:
+            data -> dict
+        """
+        data = pd.read_excel(file)
+        data.columns = [column.lower() for column in data.columns]
+        data = data[self.required_columns]
+        return data
+
+    def delete_files(self, folder_path):
+        """
+        Function to delete the files from the folder
+        Inputs:
+            folder_path -> str
+        Outputs:
+            None
+        """
+        files = os.listdir(folder_path)
+        for file in files:
+            os.remove(os.path.join(folder_path, file))
+
+    def get_json(self):
+        """
+        Function to convert the excel file to json
+        Inputs:
+            None
+        Outputs:
+            json data
+        """
+        files = self.getFiles()
+        data = pd.DataFrame()
+        incorrect = 0
+        for file in files:
+            if self.validateExcel(file):
+                data = pd.concat([data, self.getData(file)])
+            else:
+                incorrect += 1
+        if incorrect > 0:
+            print(f"{incorrect} files are incorrect")
+
+        data.drop_duplicates(inplace = True)                   # checking for multiple same entries
+        data.fillna(0, inplace = True)                         # filling the null values with 0
+        data = data.to_dict(orient = 'records')
+        self.delete_files(self.folder_path)                    # deleting the files after reading
+
+        return data
+

flask_backend/src/faq_chatbot.py

@@ -0,0 +1,27 @@
+from dotenv import load_dotenv
+import os
+import google.generativeai as genai
+
+load_dotenv()
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+class FAQChatBot:
+    def __init__(self):
+        self.model = genai.GenerativeModel("gemini-1.5-flash")
+        self.chat = self.model.start_chat(history=[])
+        self.system_prompt = (
+            "You are a financial assistant. You will only answer questions related to finance, "
+            "including investment strategies, market trends, personal finance, budgeting, and "
+            "other financial topics. If a user asks about a non-financial topic, politely decline "
+            "to answer and redirect them to financial inquiries."
+            "The answer should be brief and concise enough and should be to the point."
+            "The response should give sustainable fintech solutions"
+        )
+
+    def get_financial_insight(self, user_query):
+        response = self.chat.send_message(user_query)
+
+        if hasattr(response, 'text'):
+            return response.text.strip()
+        return "Sorry, I couldn't understand your query. Please try again."

flask_backend/src/news.py

@@ -0,0 +1,77 @@
+from newsapi import NewsApiClient
+import os
+from dotenv import load_dotenv
+from typing import List
+
+load_dotenv()
+key = os.getenv("NEWS_API")
+
+class News:
+    def __init__(self):
+        self.key = key
+        self.newsapi = NewsApiClient(api_key=key)
+
+    # for getting the top headlines based on user preferences
+    def get_top_headlines(self, preferences: List[str]) -> List[dict]:
+        """
+        Inputs:
+            List of preferences -> str
+        Outputs:
+            List of dictionaries containing the news data
+        """
+        threshold = 2
+        if len(preferences) <= 2:
+            threshold = 5
+
+        required_data = []
+        for preference in preferences:
+            top_headlines = self.newsapi.get_top_headlines(
+                q=preference, language="en"
+            )
+            result = top_headlines["articles"]
+            cnt = 0
+            for each in result:
+                if cnt == threshold:
+                    break
+                cnt += 1
+                required_data.append(
+                    {
+                        "author": each["author"],
+                        "title": each["title"],
+                        "description": each["description"],
+                        "url": each["url"],
+                        "urlToImage": each["urlToImage"],
+                    }
+                )
+        return required_data
+
+    # for getting all the news based on user preferences
+    def get_everything(self, preferences: List[str]) -> List[dict]:
+        """
+        Inputs:
+            List of preferences -> str
+        Outputs:
+            List of dictionaries containing the news data
+        """
+        result = []
+        threshold = 5
+        for preference in preferences:
+            everything = self.newsapi.get_everything(language="en", q=preference)
+            required = everything["articles"]
+            cnt = 0
+
+            for each in required:
+                if cnt == threshold:
+                    break
+                cnt += 1
+                result.append(
+                    {
+                        "author": each["author"],
+                        "publishedAt": each["publishedAt"],
+                        "title": each["title"],
+                        "description": each["description"],
+                        "url": each["url"],
+                        "urlToImage": each["urlToImage"],
+                    }
+                )
+        return result

node/controller/faqController.js

@@ -0,0 +1,18 @@
+const faqService = require("../service/faqService");
+
+exports.handleFAQRequest = async(req, res) =>{
+    try {
+        const { user_query } = req.body;
+        // {{console.log(req.body)}}
+        if (!user_query) {
+            return res.status(400).json({ error: "User query is required" });
+        }
+
+        const response = await faqService.faqService(user_query);
+        res.status(200).json({ response });
+    } catch (error) {
+        res.status(500).json({ error: error.message });
+    }
+}
+
+