Update app.py

app.py

@@ -1,49 +1,16 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-# Specify the path to your fine-tuned model and tokenizer
-# model_path = "./"  # Assuming the model is in the same directory as your notebook
-# model_name = "https://huggingface.co/spaces/DR-Rakshitha/wizardlm_api/blob/main/pytorch_model-00001-of-00002.bin"  # Replace with your model name
+# # Specify the directory containing the tokenizer's configuration file (config.json)
+model_name = "pytorch_model-00001-of-00002.bin"
 
-from llama_cpp import Llama 
-import timeit
+# # Initialize the GPT4All model
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Load Llama 2 model
-llm = Llama(model_path="./pytorch_model-00001-of-00002.bin",
-            n_ctx=512,
-            n_batch=128)
-
-# Start timer
-start = timeit.default_timer()
-
-# Generate LLM response
-# prompt = "What is Python?"
-
-# output = llm(prompt,
-#              max_tokens=-1,
-#              echo=False,
-#              temperature=0.1,
-#              top_p=0.9)
-
-
-# Load the model and tokenizer
-model = AutoModelForCausalLM.from_pretrained(model_path)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-# 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
+	text= model.generate(input_text)
+	return text
 
-    output = llm(input_text,
-             max_tokens=-1,
-             echo=False,
-             temperature=0.1,
-             top_p=0.9)
-
-# Create the Gradio interface
 text_generation_interface = gr.Interface(
     fn=generate_text,
     inputs=[
@@ -51,7 +18,4 @@ text_generation_interface = gr.Interface(
     ],
     outputs=gr.outputs.Textbox(label="Generated Text"),
     title="GPT-4 Text Generation",
-)
-
-# Launch the Gradio interface
-text_generation_interface.launch()
+).launch()