Create app.py

app.py

@@ -0,0 +1,107 @@
+import streamlit as st
+from langchain_community.document_loaders import PyPDFLoader, PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain_groq import ChatGroq
+
+
+
+loader = PyPDFDirectoryLoader("Dataset")
+docx = loader.load()
+#st.write("Number of documents loaded:", len(docx))
+
+
+text_sp = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+chunks = text_sp.split_documents(docx)
+#st.write("Number of chunks created:", len(chunks))
+
+
+if not chunks:
+    st.error("No chunks were created. Please check the documents or text splitter settings.")
+else:
+    # Step 3: Create metadata
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(chunks))]
+
+    
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                       model_kwargs={'device': "cpu"})
+
+   
+    try:
+        sample_embedding = embeddings.embed_documents([chunks[0].page_content])
+        #st.write("Sample embedding length:", len(sample_embedding[0]))
+    except Exception as e:
+        st.error(f"Error in generating embeddings: {str(e)}")
+
+    
+    try:
+        vector_store = FAISS.from_documents(chunks, embeddings)
+        #st.write("Vector store created successfully.")
+    except IndexError as e:
+        st.error("IndexError in creating vector store: " + str(e))
+        st.write("Check if chunks and embeddings are non-empty and correctly formatted.")
+        st.write("Chunks:", chunks)
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+
+llm = ChatGroq(model_name="mixtral-8x7b-32768", groq_api_key="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")
+
+def retrieve(query, vector_store, embeddings):
+    query_embedding = embeddings.embed_query(query)
+    results = vector_store.similarity_search_by_vector(query_embedding, k=5)
+    return results
+
+from langchain.schema import HumanMessage, SystemMessage
+
+def generate_response(query, retrieved_docs, llm):
+    context = " ".join([doc.page_content for doc in retrieved_docs])
+    messages = [
+        SystemMessage(content="You are an expert in prompt engineering."),
+        HumanMessage(content=f"Context: {context}\n\nQuestion: {query}\n\nAnswer:")
+    ]
+    response = llm(messages)
+    return response.content.strip()
+
+
+
+st.title("PromptGuru 🚀📖")
+
+st.sidebar.markdown("PromptGuru By OpenRAG 🗣️")
+st.sidebar.markdown(
+    """
+PromptGuru is a tool you can use for asking any queries related Prompt Engineering and Get it solved within a couple of minutes.
+    """
+    )
+
+st.sidebar.markdown(
+    """
+Note -- This tool is in a beta stage. Kindly have some patience while generating the response and give the model time to think. 
+    """
+    )
+
+st.sidebar.markdown(
+    """
+📧 **Get in Touch**
+
+For inquiries or collaboration proposals, please don't hesitate to reach out to us:
+📩 Email: [email protected]
+🔗 LinkedIn: [OpenRAG](https://www.linkedin.com/company/102036854/admin/dashboard/)
+📸 Instagram: [OpenRAG](https://www.instagram.com/open.rag?igsh=MnFwMHd5cjU1OGFj)
+
+Experience the future of Human-Chatbot Interaction with OpenRAG. 
+    """
+    
+)
+
+query = st.text_input("Ask your question about prompt engineering:")
+if query:
+    with st.spinner("Retrieving documents..."):
+        retrieved_docs = retrieve(query, vector_store, embeddings)
+       # st.write("Retrieved Documents:", retrieved_docs)
+    
+    with st.spinner("Generating response..."):
+        response = generate_response(query, retrieved_docs, llm)
+        st.write("Response:", response)
+
+