Update app.py

app.py

@@ -1,10 +1,36 @@
+import streamlit as st
+import PyPDF2
+from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
+
+# Load PDF and extract text
+def load_pdf(uploaded_file):
+    reader = PyPDF2.PdfReader(uploaded_file)
+    text = ""
+    for page in reader.pages:
+        if page.extract_text():  # Ensure text extraction is valid
+            text += page.extract_text() + "\n"
+    return text
+
+# Initialize RAG model
+def initialize_rag_model():
+    # Load the tokenizer and model
+    tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+    retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="legacy", use_dummy_dataset=True)
+    model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq")
+    return tokenizer, retriever, model
+
 # Process user query
 def generate_answer(query, context, tokenizer, retriever, model):
     # Tokenize the input question
     inputs = tokenizer(query, return_tensors="pt")
-    # Retrieve the relevant documents
+    
+    # Generate context embeddings using retriever
     context_input_ids = retriever(context, return_tensors="pt")["input_ids"]
+    
+    # Prepare inputs for the model
     inputs["context_input_ids"] = context_input_ids
+    
+    # Generate the answer
     outputs = model.generate(**inputs)
     answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)
     return answer[0]
@@ -23,4 +49,5 @@ if uploaded_file is not None:
     user_query = st.text_input("Ask a question about the PDF:")
     if user_query:
         answer = generate_answer(user_query, text, tokenizer, retriever, model)
-        st.write(f"Answer: {answer}")  # Corrected line with closing parenthesis
+        st.write(f"Answer: {answer}")  # Display the answer
+