Create app.py

app.py

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+import streamlit as st
+import PyPDF2
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores.chroma import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from langchain_groq import ChatGroq
+
+
+
+# Function to initialize conversation chain with GROQ language model
+groq_api_key = "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"
+
+# Initializing GROQ chat with provided API key, model name, and settings
+llm_groq = ChatGroq(
+    groq_api_key=groq_api_key, model_name="mixtral-8x7b-32768", temperature=0.2
+)
+
+# Streamlit app
+st.set_page_config(page_title="DocDynamo", layout="wide")
+
+st.title("DocDynamo🚀")
+uploaded_file = st.file_uploader("Please upload a PDF file to begin!", type="pdf")
+
+st.sidebar.title("DocDynamo By OpenRAG")
+st.sidebar.markdown(
+    """
+    🌟 **Introducing DocDynamo by OpenRAG: Your PDF Companion!** 📚
+
+Welcome, esteemed users, to the groundbreaking release of DocDynamo on May 21, 2024. At OpenRAG, we are committed to pioneering solutions for modern challenges, and DocDynamo is our latest triumph.
+
+    """
+    
+)
+
+st.sidebar.markdown(
+    """
+    💡 **How DocDynamo Works**
+
+Simply upload your PDF, and let DocDynamo work its magic. Once processed, you can ask DocDynamo any question pertaining to the content of your PDF. It's like having a personal assistant at your fingertips, ready to provide instant answers.
+    """
+    
+)
+
+st.sidebar.markdown(
+    """
+    📧 **Get in Touch**
+
+For inquiries or collaboration proposals, please don't hesitate to reach out to us:
+📩 Email: openrag189@gmail.com
+🔗 LinkedIn: [OpenRAG](https://www.linkedin.com/company/102036854/admin/dashboard/)
+📸 Instagram: [OpenRAG](https://www.instagram.com/open.rag?igsh=MnFwMHd5cjU1OGFj)
+
+Experience the future of PDF interaction with DocDynamo. Welcome to a new era of efficiency and productivity. OpenRAG: Empowering You Through Innovation. 🚀
+    """
+    
+)
+if uploaded_file:
+    # Inform the user that processing has started
+    with st.spinner(f"Processing `{uploaded_file.name}`..."):
+        # Read the PDF file
+        pdf = PyPDF2.PdfReader(uploaded_file)
+        pdf_text = ""
+        for page in pdf.pages:
+            pdf_text += page.extract_text()
+
+        # Split the text into chunks
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1200, chunk_overlap=50)
+        texts = text_splitter.split_text(pdf_text)
+
+        # Create metadata for each chunk
+        metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+
+        # Create a Chroma vector store
+        embeddings = OllamaEmbeddings(model="nomic-embed-text")
+        docsearch = Chroma.from_texts(texts, embeddings, metadatas=metadatas)
+
+        # Initialize message history for conversation
+        message_history = ChatMessageHistory()
+
+        # Memory for conversational context
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key="answer",
+            chat_memory=message_history,
+            return_messages=True,
+        )
+
+        # Create a chain that uses the Chroma vector store
+        chain = ConversationalRetrievalChain.from_llm(
+            llm=llm_groq,
+            chain_type="stuff",
+            retriever=docsearch.as_retriever(),
+            memory=memory,
+            return_source_documents=True,
+        )
+
+    st.success(f"Processing `{uploaded_file.name}` done. You can now ask questions!")
+
+    user_input = st.text_input("Ask a question about the PDF:")
+
+    if user_input:
+        # Call the chain with user's message content
+        res = chain.invoke(user_input)
+        answer = res["answer"]
+        source_documents = res["source_documents"]
+
+        text_elements = []  # Initialize list to store text elements
+
+        # Process source documents if available
+        if source_documents:
+            for source_idx, source_doc in enumerate(source_documents):
+                source_name = f"source_{source_idx}"
+                # Create the text element referenced in the message
+                text_elements.append(source_doc.page_content)
+            source_names = [f"source_{idx}" for idx in range(len(source_documents))]
+
+            # Add source references to the answer
+            if source_names:
+                answer += f"\nSources: {', '.join(source_names)}"
+            else:
+                answer += "\nNo sources found"
+
+        # Display the results
+        st.markdown(f"**Answer:** {answer}")
+
+        for idx, element in enumerate(text_elements):
+            with st.expander(f"Source {idx}"):
+                st.write(element)