Update app.py

app.py

@@ -1,23 +1,4 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-# # Specify the directory containing the tokenizer's configuration file (config.json)
-# model_name = "pytorch_model-00001-of-00002.bin"
-
-# # Initialize the tokenizer
-# # tokenizer = AutoTokenizer.from_pretrained(model_name, local_files_only=True)
-# tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-# tokenizer.pad_token = tokenizer.eos_token
-# tokenizer.padding_side = "right"
-
-
-# # Initialize the GPT4All model
-# model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# def generate_text(input_text):
-#     pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-#     result = pipe(f"<s>[INST] {input_text} [/INST]")
-#     return result[0]['generated_text']
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Specify the path to your fine-tuned model and tokenizer
@@ -28,16 +9,14 @@ model_name = "pytorch_model-00001-of-00002.bin"  # Replace with your model name
 model = AutoModelForCausalLM.from_pretrained(model_path)
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-# Example usage
-# input_text = "Once upon a time"
-def generated_text(input_text):
+# Define the function for text generation
+def generate_text(input_text):
     input_ids = tokenizer(input_text, return_tensors="pt").input_ids
     output = model.generate(input_ids, max_length=50, num_return_sequences=1)
     generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
     return generated_text
-# print(generated_text)
-
 
+# Create the Gradio interface
 text_generation_interface = gr.Interface(
     fn=generate_text,
     inputs=[
@@ -45,4 +24,7 @@ text_generation_interface = gr.Interface(
     ],
     outputs=gr.outputs.Textbox(label="Generated Text"),
     title="GPT-4 Text Generation",
-).launch()
+)
+
+# Launch the Gradio interface
+text_generation_interface.launch()