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app.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from string import Template
+import streamlit as st
+import base64
+from datasets import load_dataset
+from datasets import Dataset
+import torch
+from tqdm import tqdm
+from peft import LoraConfig, get_peft_model
+
+import transformers
+# from transformers import AutoModelForCausalLM, AdapterConfig
+from transformers import AutoConfig,AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
+from transformers import TrainingArguments
+
+from peft import LoraConfig
+from peft import *
+
+from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
+from langchain.prompts import PromptTemplate
+
+from IPython.display import Markdown, display
+
+peft_model_id = "Fine_Tuned"
+config = PeftConfig.from_pretrained(peft_model_id)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict=True,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+tokenizer.pad_token = tokenizer.eos_token
+
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+prompt_template = """Answer the following multiple choice question by giving the most appropriate response. Answer should be one among [A, B, C, D, E] \
+ in order of the most likely to be correct to the least likely to be correct.'
+    
+Question: {prompt}\n
+A) {a}\n
+B) {b}\n
+C) {c}\n
+D) {d}\n
+E) {e}\n
+
+Answer: """
+
+prompt = PromptTemplate(template=prompt_template, input_variables=['prompt', 'a', 'b', 'c', 'd', 'e'])
+
+
+def format_text_to_prompt(example):
+    ans = prompt.format(prompt=example['prompt'], 
+                         a=example['A'], 
+                         b=example['B'], 
+                         c=example['C'], 
+                         d=example['D'], 
+                         e=example['E'])
+    return {"ans": ans}
+
+def get_ans(text):
+    inputs = tokenizer(text, return_tensors='pt')
+    logits = model(input_ids=inputs['input_ids'].cuda(), attention_mask=inputs['attention_mask'].cuda()).logits[0, -1]
+    
+    # Create a list of tuples having (logit, 'option') format
+    options_list = [(logits[tokenizer(' A').input_ids[-1]], 'A'), (logits[tokenizer(' B').input_ids[-1]], 'B'), (logits[tokenizer(' C').input_ids[-1]], 'C'), (logits[tokenizer(' D').input_ids[-1]], 'D'), (logits[tokenizer(' E').input_ids[-1]], 'E')] 
+    options_list = sorted(options_list, reverse=True)
+    ans_list = []
+    for i in range(3):
+        ans_list.append(options_list[i][1])
+        
+    return ans_list
+
+
+def get_base64_of_bin_file(bin_file):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+def set_png_as_page_bg(png_file):
+    img = get_base64_of_bin_file(png_file)
+    page_bg_img = f"""
+        <style>
+        [data-testid="stAppViewContainer"] > .main {{
+        background-image: url("https://www.tata.com/content/dam/tata/images/verticals/desktop/banner_travel_umaidbhavan_desktop_1920x1080.jpg");
+        background-size: 200%;
+        background-position: center;
+        background-repeat: no-repeat;
+        background-attachment: local;
+        }}
+
+        [data-testid="stSidebar"] > div:first-child {{
+        background-image: url("data:image/png;base64,{img}");
+        background-position: center; 
+        background-repeat: no-repeat;
+        background-attachment: fixed;
+        }}
+
+        [data-testid="stHeader"] {{
+        background: rgba(0,0,0,0);
+        }}
+
+        [data-testid="stToolbar"] {{
+        right: 2rem;
+        }}
+        </style>
+        """
+
+    st.markdown(page_bg_img, unsafe_allow_html=True)
+
+def get_base64_encoded_image(image_path):
+    with open(image_path, "rb") as img_file:
+        encoded_string = base64.b64encode(img_file.read()).decode("utf-8")
+    return encoded_string
+
+
+def main():
+    set_png_as_page_bg("net_technology_5407.jpg")
+    image_path = "artificial-intelligence.jpg"  # Replace with the actual image file path
+
+    st.title("Sci-mcq-GPT")
+
+
+    link = "https://drive.google.com/file/d/1_2TqNNyoczhxIBmU7BpOzEi2bu3MC-sx/view?usp=sharing"
+    icon_path = "pdf download logo.png"
+    encoded_image = get_base64_encoded_image(icon_path)
+    lnk = f'<a href="{link}"><img src="data:image/png;base64,{encoded_image}" width="50" height="50"></a>'
+    col = st.sidebar
+    col.markdown(lnk, unsafe_allow_html=True)
+
+    st.subheader("Ask Q&A")
+    col1, col2 = st.columns(2)
+    query = col1.text_area("Enter your question")
+
+    if col1.button("Get Answer"):
+        ans = get_ans(query)
+        print(ans)
+        col2.text_area("Sci-mcq-GPT Response", ans)
+
+    else:
+        col2.text_area("Sci-mcq-GPT Response", value="")
+
+    col_sidebar = st.sidebar
+    col_sidebar.image(image_path, caption=" ", width=300)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+accelerate
+bitsandbytes
+einops
+langchain
+lvwerra-trl
+torch
+tqdm
+transformers
+wandb
+streamlit