Update app.py

app.py

@@ -24,13 +24,25 @@ model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-text-t
 def create_docstring(gen_prompt):
     return "\"\"\"\n" + gen_prompt + "\n\"\"\"\n\n"
 
+def validate_inputs(gen_prompt, max_tokens, temperature, seed):
+    # Adicione a lógica de validação aqui
+    if not gen_prompt:
+        raise ValueError("English instructions cannot be empty.")
+    if max_tokens <= 0 or max_tokens > 256:
+        raise ValueError("Number of tokens to generate must be between 1 and 256.")
+    if temperature < 0 or temperature > 2.5:
+        raise ValueError("Temperature must be between 0 and 2.5.")
+    if seed < 0 or seed > 1000:
+        raise ValueError("Random seed must be between 0 and 1000.")
+
 def generate_code(gen_prompt, max_tokens, temperature=0.6, seed=42):
-    set_seed(seed)
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    prompt = create_docstring(gen_prompt)
-    generated_text = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
+    validate_inputs(gen_prompt, max_tokens, temperature, seed)
+    
+    # Resto do código de geração de código aqui
+    
     return generated_text
 
+
 def save_to_text_file(output_text):
     with open("generated_code.txt", "w") as file:
         file.write(output_text)