Create app.py

app.py

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+import streamlit as st
+import os
+import openai
+from sentence_transformers import SentenceTransformer, util
+import PyPDF2
+
+
+openai.api_key = os.getenv("openapikey")
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def load_pdf(uploaded_file):
+    with open("temp.pdf", "wb") as f:
+        f.write(uploaded_file.getvalue())
+
+    with open("temp.pdf", 'rb') as file:
+        pdf_reader = PyPDF2.PdfReader(file)
+        text = ""
+        for page_num in range(len(pdf_reader.pages)):
+            page = pdf_reader.pages[page_num]
+            text += page.extract_text()
+    return text
+
+def chunk_text(text, chunk_size=500, overlap=100):
+    chunks = []
+    for i in range(0, len(text), chunk_size - overlap):
+        chunks.append(text[i:i + chunk_size])
+    return chunks
+
+
+def create_embeddings(chunks):
+    embeddings = model.encode(chunks, convert_to_tensor=True)
+    return embeddings
+
+def find_relevant_chunks(query_embedding, chunk_embeddings, chunks, top_k=3):
+    cosine_scores = util.pytorch_cos_sim(query_embedding, chunk_embeddings)[0]
+    top_results = sorted(range(len(cosine_scores)), key=lambda i: cosine_scores[i], reverse=True)[:top_k]
+    relevant_chunks = [chunks[i] for i in top_results]
+    return relevant_chunks
+
+def generate_response(query, context):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant that answers questions based on provided context."},
+        {"role": "user", "content": f"Context: {context}\nQuestion: {query}"}
+    ]
+    response = openai.chat.completions.create(
+        model="gpt-3.5-turbo",  # Or another suitable chat model
+        messages=messages,
+        max_tokens=200
+    )
+
+    return response.choices[0].message.content.strip()
+
+st.title("Simple RAG Application (No LangChain)")
+uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+
+if uploaded_file:
+    with st.spinner("Processing PDF..."):
+        pdf_text = load_pdf(uploaded_file)
+        chunks = chunk_text(pdf_text)
+        chunk_embeddings = create_embeddings(chunks)
+
+        query = st.text_input("Ask a question:")
+        if query:
+            query_embedding = model.encode([query], convert_to_tensor=True)
+            relevant_chunks = find_relevant_chunks(query_embedding, chunk_embeddings, chunks)
+            context = "\n".join(relevant_chunks)
+            answer = generate_response(query, context)
+            st.write("Answer:", answer)