Update app.py

app.py

@@ -1,28 +1,15 @@
 import gradio as gr
-import os
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Load the fine-tuned model and tokenizer
 model = AutoModelForSeq2SeqLM.from_pretrained("Codellama-7b-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("Codellama-7b-Instruct")
 
-# Define a variable to store the chat transcript
-chat_transcript = ""
-
-# Check if the chat transcript file exists
-if not os.path.exists("chat_transcript.txt"):
-    # If the file doesn't exist, create it
-    with open("chat_transcript.txt", "w") as f:
-        pass
-
-# Define a function to generate a response from the model and save the chat transcript
+# Define a function to generate a response from the model
 def generate_response(input_text):
-    nonlocal chat_transcript
     inputs = tokenizer(input_text, return_tensors="pt")
     outputs = model.generate(**inputs)
     response = tokenizer.decode(outputs[0])
-    chat_transcript += f"User: {input_text}\nAssistant: {response}\n\n"
-    with open("chat_transcript.txt", "a") as f:
-        f.write(chat_transcript)
     return response
 
 # Create a Gradio interface