Create app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# App configuration
+st.set_page_config(page_title="PDF Chatbot", layout="wide")
+st.markdown(
+    """
+    <style>
+    body {
+        background: linear-gradient(90deg, rgba(255,224,230,1) 0%, rgba(224,255,255,1) 50%, rgba(224,240,255,1) 100%);
+        color: #000;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Title and "Created by" section
+st.markdown("<h1 style='text-align: center; color: #FF69B4;'>📄 PDF RAG Chatbot</h1>", unsafe_allow_html=True)
+st.markdown(
+    "<h4 style='text-align: center;'>Created by: <a href='https://www.linkedin.com/in/datascientisthameshraj/' style='color:#FF4500;'>Engr. Hamesh Raj</a></h4>",
+    unsafe_allow_html=True
+)
+
+# Sidebar for PDF file upload
+uploaded_files = st.sidebar.file_uploader("Upload PDF files", type="pdf", accept_multiple_files=True)
+
+# Query input
+query = st.text_input("Ask a question about the uploaded PDFs:")
+
+# Initialize session state to store conversation
+if "conversation" not in st.session_state:
+    st.session_state.conversation = []
+
+# Function to extract text from PDFs
+def extract_text_from_pdfs(files):
+    pdf_text = ""
+    for file in files:
+        reader = PdfReader(file)
+        for page_num in range(len(reader.pages)):
+            page = reader.pages[page_num]
+            pdf_text += page.extract_text() + "\n"
+    return pdf_text
+
+# Load model and tokenizer
+@st.cache_resource(allow_output_mutation=True)
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG")
+    model = AutoModelForCausalLM.from_pretrained(
+        "himmeow/vi-gemma-2b-RAG",
+        device_map="auto",
+        torch_dtype=torch.bfloat16
+    )
+    if torch.cuda.is_available():
+        model.to("cuda")
+    return tokenizer, model
+
+# Process and respond to user query
+if st.button("Submit"):
+    if uploaded_files and query:
+        pdf_text = extract_text_from_pdfs(uploaded_files)
+        tokenizer, model = load_model()
+
+        prompt = """
+        ### Instruction and Input:
+        Based on the following context/document:
+        {}
+        Please answer the question: {}
+
+        ### Response:
+        {}
+        """
+
+        input_text = prompt.format(pdf_text, query, " ")
+        input_ids = tokenizer(input_text, return_tensors="pt")
+
+        if torch.cuda.is_available():
+            input_ids = input_ids.to("cuda")
+
+        outputs = model.generate(
+            **input_ids,
+            max_new_tokens=500,
+            no_repeat_ngram_size=5,
+        )
+
+        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Store the conversation
+        st.session_state.conversation.insert(0, {"question": query, "answer": answer})
+
+# Display conversation
+if st.session_state.conversation:
+    st.markdown("## Previous Conversations")
+    for qa in st.session_state.conversation:
+        st.markdown(f"**Q: {qa['question']}**")
+        st.markdown(f"**A: {qa['answer']}**")
+        st.markdown("---")