Datasets:

erdikent
/

PythonProjectt

+import pandas as pd
+from sklearn.ensemble import IsolationForest
+from smolagents import CodeAgent
+class FraudDetectionAgent:
+    pass
+CodeAgent = FraudDetectionAgent()
+class TransactionModel:
+    def __init__(self, transaction_id: int, amount: float, timestamp: str, location_lat: float, location_long: float):
+        self.transaction_id = transaction_id
+        self.amount = amount
+        self.timestamp = timestamp
+        self.location_lat = location_lat
+        self.location_long = location_long
+    def to_dict(self):
+        return {
+            "transaction_id": self.transaction_id,
+            "amount": self.amount,
+            "timestamp": self.timestamp,
+            "location_lat": self.location_lat,
+            "location_long": self.location_long
+        }
+class FraudResult:
+    def __init__(self, transaction_id: int, amount: float, timestamp: str, anomaly_score: int):
+        self.transaction_id = transaction_id
+        self.amount = amount
+        self.timestamp = timestamp
+        self.anomaly_score = anomaly_score
+    def to_dict(self):
+        return {
+            "transaction_id": self.transaction_id,
+            "amount": self.amount,
+            "timestamp": self.timestamp,
+            "anomaly_score": self.anomaly_score
+        }
+class FraudDetectionAgent(FraudDetectionAgent):
+    def __init__(self, data_path: str):
+        super().__init__()
+        self.data_path = data_path
+        self.df = None
+        self.X = None
+        self.model = IsolationForest(contamination=0.01, random_state=42)
+    def load_data(self):
+        self.df = pd.read_csv(self.data_path)
+        print(f"Loaded {len(self.df)} transactions.")
+    def preprocess(self):
+        self.df['transaction_hour'] = pd.to_datetime(self.df['timestamp']).dt.hour
+        features = ['amount', 'transaction_hour', 'location_lat', 'location_long']
+        self.df = self.df.dropna(subset=features)
+        self.X = self.df[features]
+    def detect_fraud(self):
+        self.df['anomaly_score'] = self.model.fit_predict(self.X)
+        frauds = self.df[self.df['anomaly_score'] == -1]
+        print(f"Detected {len(frauds)} potential fraudulent transactions.")
+        return [
+            FraudResult(
+                row['transaction_id'],
+                row['amount'],
+                row['timestamp'],
+                row['anomaly_score']
+            ).to_dict()
+            for _, row in frauds.iterrows()
+        ]
+    def run(self):
+        self.load_data()
+        self.preprocess()
+        return self.detect_fraud()
+if __name__ == "__main__":
+    agent = FraudDetectionAgent(data_path="transactions.csv")
+    agent.run()
+    print("\nFraud detection completed.")

flagged_transactions.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ transaction_id,amount,timestamp,anomaly_score
2	+ 7,8900.0,2023-05-01 01:45:00,-1

index.html ADDED Viewed

	@@ -0,0 +1,195 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <title>Fraud Detection Chatbot</title>
+  <style>
+    body {
+      font-family: 'Segoe UI', sans-serif;
+      margin: 0;
+      padding: 0;
+      background: #f1f2f7;
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      height: 100vh;
+    }
+    h2 {
+      margin: 20px;
+      color: #333;
+    }
+    #chat-box {
+      width: 90%;
+      max-width: 600px;
+      background: white;
+      border-radius: 10px;
+      box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+      display: flex;
+      flex-direction: column;
+      overflow: hidden;
+      flex-grow: 1;
+    }
+    #messages {
+      flex: 1;
+      overflow-y: auto;
+      padding: 20px;
+    }
+    .msg {
+      margin-bottom: 10px;
+      padding: 10px 14px;
+      border-radius: 20px;
+      max-width: 80%;
+      word-wrap: break-word;
+      line-height: 1.4;
+    }
+    .bot {
+      background-color: #e9ecef;
+      align-self: flex-start;
+      color: #333;
+    }
+    .user {
+      background-color: #0d6efd;
+      color: white;
+      align-self: flex-end;
+    }
+    #input-bar {
+      display: flex;
+      padding: 10px;
+      border-top: 1px solid #ddd;
+      background: #f9f9f9;
+    }
+    #input {
+      flex: 1;
+      padding: 10px;
+      border: 1px solid #ccc;
+      border-radius: 20px;
+      outline: none;
+      margin-right: 10px;
+    }
+    #send-btn, #upload-btn {
+      background-color: #0d6efd;
+      color: white;
+      border: none;
+      padding: 10px 14px;
+      border-radius: 20px;
+      cursor: pointer;
+    }
+    #fileInput {
+      margin: 10px 0;
+    }
+    @media (max-width: 600px) {
+      #chat-box {
+        width: 95%;
+      }
+    }
+  </style>
+</head>
+<body>
+  <h2>💬 Fraud Detection Chatbot</h2>
+  <div id="chat-box">
+    <div id="messages"></div>
+    <div style="padding: 10px; text-align: center;">
+      <input type="file" id="fileInput">
+      <button id="upload-btn" onclick="uploadFile()">Upload CSV</button>
+    </div>
+    <div id="input-bar">
+      <input id="input" type="text" placeholder="Type your message...">
+      <button id="send-btn" onclick="sendMessage()">Send</button>
+    </div>
+  </div>
+  <script>
+    const messages = document.getElementById("messages");
+    function addMessage(text, sender) {
+      const msg = document.createElement("div");
+      msg.className = `msg ${sender}`;
+      msg.textContent = text;
+      messages.appendChild(msg);
+      messages.scrollTop = messages.scrollHeight;
+    }
+    async function sendMessage() {
+      const input = document.getElementById("input");
+      const text = input.value.trim();
+      if (!text) return;
+      addMessage(text, "user");
+      input.value = "";
+      const res = await fetch("http://127.0.0.1:8000/chat/", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ message: text })
+      });
+      const data = await res.json();
+      addMessage(data.response, "bot");
+    }
+    async function uploadFile() {
+      const file = document.getElementById("fileInput").files[0];
+      if (!file) {
+        alert("Please select a CSV file first.");
+        return;
+      }
+      const formData = new FormData();
+      formData.append("file", file);
+      addMessage("🔄 Analyzing your file...", "bot");
+      const res = await fetch("http://127.0.0.1:8000/upload/", {
+        method: "POST",
+        body: formData
+      });
+      const data = await res.json();
+      addMessage(data.response, "bot");
+    }
+async function uploadFile() {
+  const file = document.getElementById("fileInput").files[0];
+  if (!file) {
+    alert("Please select a CSV file first.");
+    return;
+  }
+  const formData = new FormData();
+  formData.append("file", file);
+  addMessage("🔄 Analyzing your file...", "bot");
+  try {
+    const res = await fetch("http://127.0.0.1:8000/upload/", {
+      method: "POST",
+      body: formData
+    });
+    if (!res.ok) {
+      throw new Error(`Server error: ${res.status}`);
+    }
+    const data = await res.json();
+    addMessage(data.response || "No response from server.", "bot");
+  } catch (err) {
+    addMessage(`❌ Error: ${err.message}`, "bot");
+  }
+}
+  </script>
+</body>
+</html>

main.py ADDED Viewed

	@@ -0,0 +1,120 @@

+import os
+from fastapi import FastAPI, UploadFile, File
+from FraudDetectionAgent import CodeAgent
+app = FastAPI()
+@app.post("/detect-fraud/")
+async def detect_fraud(file: UploadFile = File(...)):
+    contents = await file.read()
+    temp_path = "temp_transactions.csv"
+    with open(temp_path, "wb") as f:
+        f.write(contents)
+    agent = CodeAgent(data_path=temp_path)
+    frauds = agent.run()
+    os.remove(temp_path)
+    return {"flagged_transactions": frauds}
+from fastapi import FastAPI, UploadFile, File
+from pydantic import BaseModel
+from FraudDetectionAgent import FraudDetectionAgent
+import pandas as pd
+import os
+app = FastAPI()
+# Basic chat message schema
+class ChatMessage(BaseModel):
+    message: str
+@app.post("/chat/")
+def chat_with_agent(msg: ChatMessage):
+    user_input = msg.message.lower()
+    if "fraud" in user_input:
+        return {"response": "You can upload a CSV file at /detect-fraud/ to check for fraudulent transactions."}
+    elif "hello" in user_input or "hi" in user_input:
+        return {"response": "Hello! I can help you detect transaction frauds. Ask me how."}
+    else:
+        return {"response": "I'm still learning! Try asking about fraud detection or uploading a CSV."}
+@app.post("/detect-fraud/")
+async def detect_fraud(file: UploadFile = File(...)):
+    contents = await file.read()
+    temp_path = "temp_transactions.csv"
+    with open(temp_path, "wb") as f:
+        f.write(contents)
+    agent = FraudDetectionAgent(data_path=temp_path)
+    frauds = agent.run()
+    os.remove(temp_path)
+    return {"flagged_transactions": [f.to_dict() for f in frauds]}
+from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from FraudDetectionAgent import CodeAgent
+import pandas as pd
+import os
+app = FastAPI()
+# Enable CORS for browser frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+class ChatMessage(BaseModel):
+    message: str
+@app.post("/chat/")
+def chat_with_agent(msg: ChatMessage):
+    text = msg.message.lower()
+    if "fraud" in text:
+        return {"response": "You can upload a CSV of transactions below and I'll tell you which ones look suspicious."}
+    elif "hello" in text:
+        return {"response": "Hi there! I'm your fraud detection assistant. Upload a CSV to get started."}
+    else:
+        return {
+            "response": "I'm here to help with transaction fraud detection. Try asking about fraud or upload your data."}
+@app.post("/upload/")
+async def upload_file(file: UploadFile = File(...)):
+    try:
+        temp_file = "temp_data.csv"
+        with open(temp_file, "wb") as f:
+            f.write(await file.read())
+        agent = FraudDetectionAgent(data_path=temp_file)
+        frauds = agent.run()
+        os.remove(temp_file)
+        if not frauds:
+            return {"response": "✅ No fraud detected!"}
+        summary = "\n".join([
+            f"- ID: {f.get('transaction_id')}, Amount: ${f.get('amount')}"
+            for f in frauds[:5]
+        ])
+        return {"response": f"⚠️ Detected {len(frauds)} suspicious transactions:\n{summary}"}
+    except Exception as e:
+        return {"response": f"❌ Error processing file: {str(e)}"}

requirements.txt ADDED Viewed

	@@ -0,0 +1,9 @@

+pip~=25.1.1
+pillow~=11.2.1
+filelock~=3.18.0
+pandas~=2.2.3
+fastapi~=0.115.12
+pydantic~=2.11.4
+scikit-learn~=1.6.1
+smolagents~=1.16.0
+uvicorn

transactions.csv ADDED Viewed

	@@ -0,0 +1,9 @@

+transaction_id,amount,timestamp,location_lat,location_long
+1,120.50,2023-05-01 08:45:00,37.7749,-122.4194
+2,9999.99,2023-05-01 02:13:00,40.7128,-74.0060
+3,10.00,2023-05-01 14:32:00,37.7749,-122.4194
+4,5000.00,2023-05-01 03:00:00,35.6895,139.6917
+5,75.20,2023-05-01 17:15:00,34.0522,-118.2437
+6,250.00,2023-05-01 13:22:00,37.7749,-122.4194
+7,8900.00,2023-05-01 01:45:00,55.7558,37.6173
+8,8.99,2023-05-01 11:15:00,37.7749,-122.4194