Create app.py

app.py

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+import os
+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+
+# Streamlit App Title
+st.title("📄 DeepSeek-Powered RAG Chatbot")
+
+# Step 1: Input API Key
+api_key = st.text_input("🔑 Enter your DeepSeek API Key:", type="password")
+
+if api_key:
+    # Set the API key as an environment variable (optional)
+    os.environ["DEEPSEEK_API_KEY"] = api_key
+
+    # Step 2: Upload PDF Document
+    uploaded_file = st.file_uploader("📂 Upload a PDF document", type=["pdf"])
+
+    if uploaded_file:
+        # Load and process the document
+        with st.spinner("Processing document..."):
+            loader = PyPDFLoader(uploaded_file)
+            documents = loader.load()
+
+            # Split the document into chunks
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+            chunks = text_splitter.split_documents(documents)
+
+            # Generate embeddings and store them in a vector database
+            embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+            vector_store = FAISS.from_documents(chunks, embeddings)
+
+        st.success("Document processed successfully!")
+
+        # Step 3: Ask Questions About the Document
+        st.subheader("💬 Chat with Your Document")
+        user_query = st.text_input("Ask a question:")
+
+        if user_query:
+            # Set up the RAG pipeline with DeepSeek LLM
+            retriever = vector_store.as_retriever()
+            llm = ChatOpenAI(
+                model="deepseek-chat",
+                openai_api_key=api_key,
+                openai_api_base="https://api.deepseek.com/v1",
+                temperature=0.85,
+                max_tokens=4000  # Ensure compliance with DeepSeek's token limit
+            )
+            qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+
+            # Generate response
+            with st.spinner("Generating response..."):
+                try:
+                    response = qa_chain.run(user_query)
+                    st.write(f"**Answer:** {response}")
+                except Exception as e:
+                    st.error(f"Error: {e}")
+else:
+    st.warning("Please enter your DeepSeek API key to proceed.")