DR-Rakshitha
commited on
Commit
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402975e
1
Parent(s):
fdb4240
Update app.py
Browse files
app.py
CHANGED
@@ -1,23 +1,4 @@
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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# # Specify the directory containing the tokenizer's configuration file (config.json)
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# model_name = "pytorch_model-00001-of-00002.bin"
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# # Initialize the tokenizer
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# # tokenizer = AutoTokenizer.from_pretrained(model_name, local_files_only=True)
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# tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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# tokenizer.pad_token = tokenizer.eos_token
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# tokenizer.padding_side = "right"
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# # Initialize the GPT4All model
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# model = AutoModelForCausalLM.from_pretrained(model_name)
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# def generate_text(input_text):
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# pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
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# result = pipe(f"<s>[INST] {input_text} [/INST]")
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# return result[0]['generated_text']
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Specify the path to your fine-tuned model and tokenizer
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@@ -28,16 +9,14 @@ model_name = "pytorch_model-00001-of-00002.bin" # Replace with your model name
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model = AutoModelForCausalLM.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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#
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def generated_text(input_text):
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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output = model.generate(input_ids, max_length=50, num_return_sequences=1)
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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return generated_text
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# print(generated_text)
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text_generation_interface = gr.Interface(
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fn=generate_text,
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inputs=[
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],
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outputs=gr.outputs.Textbox(label="Generated Text"),
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title="GPT-4 Text Generation",
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)
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Specify the path to your fine-tuned model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# Define the function for text generation
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def generate_text(input_text):
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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output = model.generate(input_ids, max_length=50, num_return_sequences=1)
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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return generated_text
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# Create the Gradio interface
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text_generation_interface = gr.Interface(
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fn=generate_text,
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inputs=[
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],
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outputs=gr.outputs.Textbox(label="Generated Text"),
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title="GPT-4 Text Generation",
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)
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# Launch the Gradio interface
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text_generation_interface.launch()
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