Create app.py

app.py

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+pip install transformers huggingface_hub gradio torch
+from huggingface_hub import login
+
+# Login using your Hugging Face token (replace "your_token" with your actual token)
+login("your_token")
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi")
+# Input text for code generation
+text = "def bubble_sort(list_elements):"
+
+# Tokenize the input text
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+# Generate code based on the input text
+generated_ids = model.generate(
+    input_ids,
+    max_length=200,  # Adjust as needed
+    num_return_sequences=1,  # Number of generated sequences to return
+    pad_token_id=tokenizer.eos_token_id  # Handle padding tokens
+)
+
+# Decode the generated tokens to text
+generated_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+print(generated_code)
+from huggingface_hub import HfApi, Repository
+
+# Replace with your Hugging Face username and repo name
+repo_name = "your-username/codegen-350M-multi-bubble-sort"
+
+# Initialize the repository to push your model and tokenizer
+api = HfApi()
+
+# Create a new repo (if you haven't already)
+api.create_repo(repo_name, exist_ok=True)
+
+# Push the tokenizer and model to Hugging Face Hub
+model.push_to_hub(repo_name)
+tokenizer.push_to_hub(repo_name)
+
+print(f"Model and tokenizer pushed to Hugging Face Hub under: {repo_name}")