Create app.py

app.py

@@ -0,0 +1,148 @@
+__import__('pysqlite3')
+import sys
+sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+from langchain_community.llms import Ollama 
+from langchain_community.document_loaders import PyPDFLoader
+from path import Path
+from langchain.chains import create_history_aware_retriever
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_community.document_loaders import WebBaseLoader 
+from langchain_text_splitters import RecursiveCharacterTextSplitter 
+from langchain_community.vectorstores import Chroma 
+from langchain_community import embeddings 
+from langchain_core.messages import AIMessage, HumanMessage
+
+llm = Ollama(model = "mistral")
+
+
+
+def build_the_bot2(input_text):
+    
+    import os
+    print(input_text)
+    
+    global loader, vectorstore, rag_chain, qa_prompt, contextualize_q_system_prompt, contextualize_q_prompt, history_aware_retriever
+    contextualize_q_system_prompt = """Given a chat history and the latest user question \
+                                    which might reference context in the chat history, formulate a standalone question \
+                                    which can be understood without the chat history. Do NOT answer the question, \
+                                    just reformulate it if needed and otherwise return it as is."""
+    contextualize_q_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", contextualize_q_system_prompt),
+                MessagesPlaceholder("chat_history"),
+                ("human", "{input}"),
+            ]
+                )
+
+    #prompt = hub.pull("rlm/rag-prompt")
+
+    loader = PyPDFLoader(file_path=Path(input_text))
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = 1000,
+    chunk_overlap = 200,
+    add_start_index = True 
+    )
+    all_splits = text_splitter.split_documents(documents)
+
+    embedding = embeddings.OllamaEmbeddings(
+        model="nomic-embed-text"
+    )
+    vectorstore = Chroma.from_documents(documents=all_splits, embedding=embedding, persist_directory="./sfbook")
+    vectorstore.persist()
+    retriever = vectorstore.as_retriever(    search_type = "similarity",    search_kwargs = {"k":6})
+    
+    
+    qa_system_prompt = """You are an assistant for question-answering tasks. \
+    Use the following pieces of retrieved context to answer the question. \
+    If you don't know the answer, just say that you don't know. \
+    Use three sentences maximum and keep the answer concise.\
+
+    {context}"""
+    qa_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", qa_system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human", "{input}"),
+        ]
+    )
+
+
+    question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+    
+    #documents = loader.load_data("workspace/ACCD-0313-Connect12_v2.1-UserGuide-EN-Rev05.pdf")
+    #documents = loader.load_data(Path(input_text))
+    
+    # rag_chain = (
+    #   RunnablePassthrough.assign(
+    #       context = contextualized_question | retriever | format_docs
+    #   )
+    #   | qa_prompt 
+    #   | llm
+    #   )
+    history_aware_retriever = create_history_aware_retriever(
+        llm, retriever, contextualize_q_prompt
+            )
+    rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+
+
+    return('Index saved successful!!!')
+
+
+
+global chat_context
+chat_context = []
+def chat(chat_history, user_input,chat_context):
+    #print(chat_history)
+    #print(user_input)
+    chat_context = chat_context or []
+    ai_msg = rag_chain.invoke(
+    {
+        "question": user_input,
+        "chat_history": chat_history
+    }
+    )
+    #print(f"aimsg {ai_msg}")
+    chat_context.extend([HumanMessage(content=user_input), ai_msg])
+    #print(f"chathistory: {chat_history}")
+    #chat_history.append((user_input, ai_msg))
+      #print(bot_response)
+    response = ""
+    for letter in "".join(ai_msg): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:
+        response += letter + ""
+        yield chat_history + [(user_input, response)]
+
+    #return chat_history, chat_history
+
+def upload_file(files):
+    #file_paths = [file.name for file in files]
+    #return file_paths
+    print(files)
+    return files[0]
+
+
+import gradio as gr
+block = gr.Blocks()
+
+#based on this https://python.langchain.com/v0.1/docs/use_cases/question_answering/chat_history/
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Q&A Bot with Mistral Model')
+    with gr.Tab("Input Text Document"):
+        file_output = gr.File()
+        upload_button=gr.UploadButton(file_types=[".pdf",".csv",".docx"])
+        upload_button.upload(upload_file, upload_button, file_output)
+        text_output = gr.Textbox()
+        text_button = gr.Button("Build the Bot!!!")
+        text_button.click(build_the_bot2, file_output, text_output)
+    with gr.Tab("Knowledge Bot"):
+        chatbot = gr.Chatbot()
+        message = gr.Textbox("what is this document about?")
+        #state = gr.State()
+
+        #message = gr.Textbox ("SEND")
+        message.submit(chat2, [ chatbot, message, gr.State(chat_context)], chatbot)
+        #submit.click(chat, inputs= [message, state], outputs= [chatbot])
+demo.queue().launch(share=True, debug=True)