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import gradio as gr
import torch
from transformers import LLaMAForCausalLM, LLaMATokenizer
# Load model and tokenizer
model_name = "llama-3.1"
tokenizer = LLaMATokenizer.from_pretrained(model_name)
model = LLaMAForCausalLM.from_pretrained(model_name)
# Define code analysis function
def analyze_code(code):
inputs = tokenizer.encode_plus(code, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=512)
return outputs.last_hidden_state
# Define GitHub API function
def get_repo_code(repo_link):
GITHUB_TOKEN = "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"
GITHUB_API_URL = "https://api.github.com"
owner, repo = repo_link.split("/")[-2:]
repo_url = f"{GITHUB_API_URL}/repos/{owner}/{repo}/contents"
headers = {"Authorization": f"Bearer {GITHUB_TOKEN}"}
response = requests.get(repo_url, headers=headers)
return response.json()
# Create Gradio interface
demo = gr.Interface(
fn=analyze_code,
inputs=gr.Textbox(label="Enter code to analyze"),
outputs=gr.JSON(label="Analyzed code representation"),
title="Code Analysis with LLaMA 3.1",
description="Enter code to analyze and get its representation using LLaMA 3.1 model."
)
# Create Gradio interface for GitHub repository link
github_demo = gr.Interface(
fn=get_repo_code,
inputs=gr.Textbox(label="Enter GitHub repository link"),
outputs=gr.JSON(label="Repository code"),
title="Get GitHub Repository Code",
description="Enter GitHub repository link to get its code."
)
# Launch Gradio app
gr.TabbedInterface([demo, github_demo], ["Analyze Code", "Get Repository Code"]).launch()
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