Update app.py

app.py

@@ -1,12 +1,14 @@
 # Load model directly
 import streamlit as st
-
-from peft import AutoPeftModelForCausalLM
-from transformers import AutoTokenizer
-model = AutoPeftModelForCausalLM.from_pretrained(
-        "shivam9980/mistral-7b-news", # YOUR MODEL YOU USED FOR TRAINING
-        load_in_4bit = True,)
-tokenizer = AutoTokenizer.from_pretrained("shivam9980/mistral-7b-news")
+from unsloth import FastLanguageModel
+import torch
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "shivam9980/mistral-7b-news-cnn-merged", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    token = hf_token, # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
 
 # alpaca_prompt = You MUST copy from above!
 
@@ -20,20 +22,22 @@ alpaca_prompt = """Below is an instruction that describes a task, paired with an
 ### Response:
 {}"""
 
-content = st.text_input('Content')
+  # alpaca_prompt = Copied from above
+c = st.text_input('Enter the contents ')
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
 inputs = tokenizer(
 [
     alpaca_prompt.format(
         "The following passage is content from a news report. Please summarize this passage in one sentence or less.", # instruction
-        content, # input
+        c,
         "", # output - leave this blank for generation!
     )
 ], return_tensors = "pt").to("cuda")
 
 outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
-results= tokenizer.batch_decode(outputs)
+results = tokenizer.batch_decode(outputs)
 out = results[0].split('\n')[-1]
-st.text_area(label='Headline',value=out[:len(out)-4])
+st.text_area(label='Headline',value=out[:])