Update app.py

app.py

@@ -1,3 +1,121 @@
-import gradio as gr
+# import gradio as gr
 
-gr.load("models/mistralai/Mistral-7B-v0.1").launch()
+# gr.load("models/mistralai/Mistral-7B-v0.1").launch()
+
+import os
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmlTemplates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+
+# set this key as an environment variable
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']
+
+def get_pdf_text(pdf_docs : list) -> str:
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+def get_text_chunks(text:str) ->list:
+    text_splitter = CharacterTextSplitter(
+        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vectorstore(text_chunks : list) -> FAISS:
+    model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+    encode_kwargs = {
+        "normalize_embeddings": True
+    }  # set True to compute cosine similarity
+    embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
+    )
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
+    # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
+    llm = HuggingFaceHub(
+        repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        #repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
+        model_kwargs={"temperature": 0.5, "max_length": 1048},
+    )
+
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm, retriever=vectorstore.as_retriever(), memory=memory
+    )
+    return conversation_chain
+
+
+def handle_userinput(user_question:str):
+    response = st.session_state.conversation({"question": user_question})
+    st.session_state.chat_history = response["chat_history"]
+
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write("   Usuario: " + message.content)
+        else:
+            st.write("🤖 ChatBot: " + message.content)
+
+
+def main():
+    st.set_page_config(
+        page_title="Chat with a Bot that tries to answer questions about multiple PDFs",
+        page_icon=":books:",
+    )
+
+    st.markdown("# Chat with a Bot")
+    st.markdown("This bot tries to answer questions about multiple PDFs. Let the processing of the PDF finish before adding your question. 🙏🏾")
+
+    st.write(css, unsafe_allow_html=True)
+
+    
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+
+    
+    st.header("Chat with a Bot 🤖🦾 that tries to answer questions about multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    
+    with st.sidebar:
+        st.subheader("Your documents")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True
+        )
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                # get pdf text
+                raw_text = get_pdf_text(pdf_docs)
+
+                # get the text chunks
+                text_chunks = get_text_chunks(raw_text)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+
+
+if __name__ == "__main__":
+    main()