Lucylililiwang create fix error handling and clean up client

Fix_error_handling_and_clean_up_client.py

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+# We are importing the require library
+import os
+import requests
+import json
+
+def generate_text(prompt, max_length=50):
+    try:
+        # Retrieve OpenAI API key from environment variable
+        openai_key = os.getenv('OPENAI_API_KEY')
+        if not openai_key:
+            raise EnvironmentError("OpenAI API key not found in environmnet variables")
+
+        url = "https://api/openai.com/v1/completions"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer{openai_key}"
+        }
+
+        # We are prepare JSON payload
+        data = {
+            "model": "text-davinci-003",
+            "prompt": prompt,
+            "max_tokens": max_length
+        }
+
+        # We are making a POST request to OpenAI API
+        response = requests.post(url, headers=headers)
+        # Raise HTTPError for non-2xx status codes
+        response.raise_for_status()
+
+        # We are parse response JSON
+        result = response.json()
+        generate_text = result['choices'][0]['text'].strip()
+        return generate_text
+
+    except Exception as e:
+        print(f"Error occurred: {str(e)}")
+
+
+# We are define the entry poing of the function
+if __name__ == "__main__":
+    prompt = "What is the meaning of life?"
+    max_length = 100
+
+    generate_text = generate_text(prompt, max_length)
+    if generate_text:
+        print("Generated text:", generate_text)
+    else:
+        print("Failed to generate text")

LoRa.py

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+# Import the require library
+from network import LoRa
+import socket
+import time
+
+# We are initialize LoRa module
+lora = LoRa(model=LoRa.LORA, frequency=915000000)
+
+# We are Create LoRa socket
+s = socket.socket(socket.AF_LORA, socket.SOCK_RAW)
+
+# We are set the LoR socket blocking to False
+s.setblocking(False)
+
+# We are starting an while loop
+while True:
+    # We are sending some data
+    s.send("Hello LoRa")
+
+    # We are wait for incoming messages
+    data = s.recv(64)
+    if data:
+        print("Received", data)
+
+    time.sleep(2)

evaluation_script.py

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+# We are import the require library
+import sys
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.utils.data import Dataset, DataLoader
+from tqdm import tqdm
+
+# We are define your custom dataset class if needed
+class CustomDataset(Dataset):
+    def __init__(self, data):
+        self.data = data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+# We are define the evaluation function
+def evaluate_models(model_names, test_datset, tokenizer, device="cuda" if torch.cuda.is_available() else "cpu"):
+    for model_name in model_names:
+        print(f"Evaluating model: {model_name}")
+
+        # We are Load the fine-tuned model
+        model = AutoModelForCausalLM.from_pretrained(f"fine_tuned_{model_name}").to(device)
+
+        # We are create data loader for evaluation
+        test_loader = DataLoader(test_datset, batch_size=8, shuffle=False)
+
+        # We are evaluation loop
+        total_loss = 0.0
+        total_samples = 0
+
+        model.eval()
+        with torch.no_grad():
+            for batch in tqdm(test_loader, desc="Evalatiing"):
+                input_ids = tokenizer(batch, return_tensors="pt", padding=True, truncation=True)["input_ids"].to(device)
+                labels = input_ids.clone()
+
+                outouts = model(input_ids, labels=labels)
+
+                loss = outouts.loss
+                total_loss += loss.item() * input_ids.size(0)
+                total_samples += input_ids.size(0)

fine_tuning_script.py.py

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+# We are importing the library
+import sys
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.utils.data import Dataset, DataLoader
+from tqdm import tqdm
+
+# We are define the custom dataset class if needed
+class CustomDataset(Dataset):
+    def __init__(self, data):
+        self.data = data
+    def __len__(self):
+        return len(self.data)
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+# We are define the fine-tuning function
+def fine_tune_models(model_names, train_dataset, tokenizer, num_epochs=3, batch_size=8,device="cuda" if torch.cuda.is_available() else "cpu"):
+    for model_name in model_names:
+        print(f"Fine-tuning model: {model_name}")
+
+        # We are load the pre-trained model
+        model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+        # We are define optimizer and scheduler
+        optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
+        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9)
+
+        # Creare Data Loader for training
+        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+        # We are Fine-tuning loop
+        for epoch in range(num_epochs):
+            model.train()
+            total_loss = 0.0
+
+            for batch in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{num_epochs}"):
+                input_ids = tokenizer(batch, return_tensors="pt", padding=True, truncation=True)["input_ids"].to(device)
+                labels = input_ids.clone()
+
+                optimizer.zero_grad()
+                outputs = model(input_ids, labels=labels)
+
+                loss = outputs.loss
+                total_loss += loss.item()
+
+                loss.backward()
+                optimizer.step()
+                scheduler.step()
+
+            # We are print the average loss for the epoch
+            print(f"Average Loss: {total_loss/ len(train_loader)}")
+
+        # We are save the fine-tuned model
+        model.save_pretrained(f"fine_tuned_{model_name}")
+
+if __name__ == "__main__":
+    # we are define the models to fine-tune
+    model_names = [
+        "facebook/opt-125m",
+        "facebook/opt-350m",
+    ]
+
+    # We are Load preprocessed data and tokenizer
+    train_dataset = CustomDataset(...)
+    tokenizer = AutoTokenizer.from_pretrained("tokenizer-name")
+
+    # We are fine-tune the models
+    fine_tune_models(model_names, train_dataset, tokenizer)

kubernetes_support

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+# We are importing the require library
+from kubernetes import client, config
+
+def main():
+    # We are Load kubeconfig file or in-cluster config
+    config.load_kube_config()
+
+    # We are create Kubernetes API client
+    api_instance = client.CoreV1Api()
+
+    # We are list pods in the default namespace
+    print("Listing pods with their IPs:")
+    ret = api_instance.list_pod_for_all_namespaces(watch=False)
+    for i in ret.items:
+        print(f"{i.metadata.name} : {i.status.pod_ip}")
+
+
+# We are define the entry point of the function
+if __name__ == "__main__":
+    main()

preprocessing_script.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from transformers import AutoTokenizer
+
+# We are define an function to preprocess data
+def preprocess_data(csv_file, tokenizer_name, max_length=512, test_size=0.1, random_state=42):
+    # We first load the csv file into a DataFrame
+    df = pd.read_csv(csv_file)
+
+    # We are Initialize tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+
+    # We are tokenize text and add tokenized sequences to DataFrame
+    tokenized_sequences = []
+    # adjust text_columns according to your dataset
+    for text in df['text_columns']:
+        tokenized_text = tokenizer.encode(text, max_length=max_length, truncation=True)
+        tokenized_sequences.append(tokenized_text)
+
+    df['tokenzied_text'] = tokenized_sequences
+
+    # We are split the data into training and testing sets
+    train_df, test_df = train_test_split(df, test_size=test_size, random_state=random_state)
+
+    return train_df, test_df
+
+# We are define the entrypoint of the function
+if __name__ == "__main__":
+    # We are setting the parameters
+    csv_file = "path/to/your/dataset.csv"
+    tokenizer_name = "tokenizer-name"
+    max_length = 512
+    test_size = 0.1
+    random_state = 42
+
+    # We preprocess data
+    train_df, test_df = preprocess_data(csv_file, tokenizer_name, max_length=max_length, test_size=test_size)
+
+    # We are saving the preprocessed data if needed
+    train_df.to_csv("train.csv", index=False)
+    test_df.to_csv("test.csv", index=False)
+
+    # waiting for future improvement  

push_pypi.yml

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+name: Publish to PyPI
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.x'
+
+      - name: Install dependencies
+        run: pip install -r requirements.txt
+
+      - name: Build package
+        run: python setup.py sdist bdist_wheel
+
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.PyPI_API_TOKEN}}
+          distributions: "sdist bdist_wheel"
+          repository_url: https://upload.pypi.org/legacy/

signup_and_Multi-Factor.py

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+# We are importing the require library
+from flask import Flask, render_template, request, redirect, url_for, session
+from flask_wtf.csrf import CSRFProtect
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import boto3
+
+app = Flask(__name__)
+csrf = CSRFProtect(app)
+
+# We are intialized AWS variable
+AWS_REGION = 'Global'
+USER_POOL_ID = ''
+APP_CLIENT_ID = ''
+IDENTITY_POOL_ID = ''
+
+# We are Load the pre-trained GPT-2 model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+# We are initialize Congito client
+cognito_client = boto3.client('cognito-idp', region_name=AWS_REGION)
+
+# We are define the route
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/signup", methods=["GET","POST"])
+def signup():
+    if request.method == "POST":
+        username = request.form["username"]
+        password = request.form["password"]
+        email = request.form["email"]
+
+        # Step 1: We are create  a new user in Cognito User Pool
+        try:
+            response = cognito_client.sign_up(
+                ClientId=APP_CLIENT_ID
+                Username = username,
+                Password=password,
+                UserAttributes=[
+                    {'Name': 'email', 'Value': email},
+                ],
+            )
+            user_sub = response['UserSub']
+            session['user_sub'] = user_sub
+            return redirect(url_for('mfa_verification'))
+        except cognito_client.exceptions.UsernameExistsException:
+            return render_template("signup.html", error="Username already exists. Choose a different one")
+        except cognito_client.exceptions.UserLambdaValidationException as e:
+            return render_template("signup.html", error=f"Error: {e}")
+        except Exception as e:
+            return render_template("signup.html", error=f"Error: {e}")
+
+    return render_template("signup.html")
+
+@app.route("/mfa_verification", methods=["GET", "POST"])
+def mfa_verification():
+    if 'user_sub' not in session:
+        return redirect(url_for('signup'))
+    if request.method =="POST":
+        mfa_code = request.form["mfa_code"]
+
+        # Step 2: Verify MFA code
+        try:
+            cognito_client.verify_software_token(
+                AccessToken=session['user_sub'],
+                UserCode = mfa_code,
+            )
+
+            return render_template("index.html", prompt="", generated_text="MFA verification successful.")
+        except cognito_client.exceptions.CodeMismatchException:
+            return render_template("mfa_verification.html", error="Incorrect MFA code. Please try again.")
+
+        except Exception as e:
+            return render_template("mfa_verification.html", error=f"Error:{e}")
+
+    return render_template("mfa_verification.html")
+
+if __name__ == "__main__":
+    app.secret_key = 'supersecretkey'
+    app.run(debug=True)
+