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README.md

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----
-title: Classify ASH
-emoji: 🔥
-colorFrom: green
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.33.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Project Name
+This repository contains the trained model and scripts for [brief description of what the model does or its purpose]. This model is designed to [describe applications or what the model can be used for, like generating text, classifying images, etc.].
+
+Model Description
+[Provide a detailed description of the model, including the architecture, training data, and any significant features that highlight its uniqueness or capabilities.]
+
+Features
+Feature 1: [Description]
+Feature 2: [Description]
+Feature 3: [Description]
+Installation
+To use this model, you first need to install the required packages. Run the following command:
+
+bash
+Copy code
+pip install -r requirements.txt
+Usage
+Here's how to use the model in your project:
+
+python
+Copy code
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "your-huggingface-model-identifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_class_id = logits.argmax().item()
+    return model.config.id2label[predicted_class_id]
+
+# Example usage
+text = "Your example text here"
+print(predict(text))
+Performance
+Discuss the performance metrics, benchmarks, or comparisons here, showing how the model performs in various scenarios.
+
+Contributing
+We welcome contributions to improve the model or scripts. Please follow these steps to contribute:
+
+Fork the repository.
+Create a new branch (git checkout -b feature-branch).
+Commit your changes (git commit -am 'Add some feature').
+Push to the branch (git push origin feature-branch).
+Open a new Pull Request.
+License
+This project is licensed under the [choose a license] - see the LICENSE file for details.
+
+Citation
+If you use this model in your research, please cite it as follows:
+
+bibtex
+Copy code
+@inproceedings{author2023model,
+  title={Title of Your Model},
+  author={Author Names},
+  booktitle={Where it was published},
+  year={2023}
+}
+Acknowledgments
+Mention any advisors, financial supporters, or data providers.
+Any other recognition or credits.
+Contact
+For issues, questions, or collaborations, you can contact [email contact] or create an issue in this repository.
+

app.py

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+"""
+stores vectores in session state, or locally.
+loading from local does not yet work.
+load funciton must recieve the uploaded file from fileuploader.
+"""
+
+import time
+from datetime import datetime
+import openai
+import tiktoken
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from html_templates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+import os
+import numpy as np
+
+
+def merge_faiss_indices(index1, index2):
+    """
+    Merge two FAISS indices into a new index, assuming both are of the same type and dimensionality.
+
+    Args:
+    index1 (faiss.Index): The first FAISS index.
+    index2 (faiss.Index): The second FAISS index.
+
+    Returns:
+    faiss.Index: A new FAISS index containing all vectors from index1 and index2.
+    """
+
+    # Check if both indices are the same type
+    if type(index1) != type(index2):
+        raise ValueError("Indices are of different types")
+
+    # Check dimensionality
+    if index1.d != index2.d:
+        raise ValueError("Indices have different dimensionality")
+
+    # Determine type of indices
+    if isinstance(index1, FAISS.IndexFlatL2):
+        # Handle simple flat indices
+        d = index1.d
+        # Extract vectors from both indices
+        xb1 = FAISS.rev_swig_ptr(index1.xb.data(), index1.ntotal * d)
+        xb2 = FAISS.rev_swig_ptr(index2.xb.data(), index2.ntotal * d)
+
+        # Combine vectors
+        xb_combined = np.vstack((xb1, xb2))
+
+        # Create a new index and add combined vectors
+        new_index = FAISS.IndexFlatL2(d)
+        new_index.add(xb_combined)
+        return new_index
+
+    elif isinstance(index1, FAISS.IndexIVFFlat):
+        # Handle quantized indices (IndexIVFFlat)
+        d = index1.d
+        nlist = index1.nlist
+        quantizer = FAISS.IndexFlatL2(d)  # Re-create the appropriate quantizer
+
+        # Create a new index with the same configuration
+        new_index = FAISS.IndexIVFFlat(quantizer, d, nlist, FAISS.METRIC_L2)
+
+        # If the indices are already trained, you can directly add the vectors
+        # Otherwise, you may need to train new_index using a representative subset of vectors
+        vecs1 = FAISS.rev_swig_ptr(index1.xb.data(), index1.ntotal * d)
+        vecs2 = FAISS.rev_swig_ptr(index2.xb.data(), index2.ntotal * d)
+        new_index.add(vecs1)
+        new_index.add(vecs2)
+        return new_index
+
+    else:
+        raise TypeError("Index type not supported for merging in this function")
+
+
+def get_pdf_text(pdf_docs):
+    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):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_faiss_vectorstore(text_chunks):
+    if st.session_state.openai:
+        my_embeddings = OpenAIEmbeddings()
+    else:
+        my_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=my_embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    if st.session_state.openai:
+        llm = ChatOpenAI()
+    else:
+        llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
+
+    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):
+    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):
+        # Display user message
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            print(message)
+            # Display AI response
+            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+            # Display source document information if available in the message
+            if hasattr(message, 'source') and message.source:
+                st.write(f"Source Document: {message.source}", unsafe_allow_html=True)
+
+def set_global_variables():
+    global BASE_URL
+    BASE_URL = "https://api.vectara.io/v1"
+    global OPENAI_API_KEY
+    OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+    global OPENAI_ORG_ID
+    OPENAI_ORG_ID = os.environ["OPENAI_ORG_ID"]
+    global PINECONE_API_KEY
+    PINECONE_API_KEY = os.environ["PINECONE_API_KEY_LCBIM"]
+    global HUGGINGFACEHUB_API_TOKEN
+    HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"]
+    global VECTARA_API_KEY
+    VECTARA_API_KEY = os.environ["VECTARA_API_KEY"]
+    global VECTARA_CUSTOMER_ID
+    VECTARA_CUSTOMER_ID = os.environ["VECTARA_CUSTOMER_ID"]
+    global headers
+    headers = {"Authorization": f"Bearer {VECTARA_API_KEY}", "Content-Type": "application/json"}
+
+
+def main():
+    set_global_variables()
+    st.set_page_config(page_title="Anna Seiler Haus KI-Assistent", page_icon=":hospital:")
+    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
+    if "page" not in st.session_state:
+        st.session_state.page = "home"
+    if "openai" not in st.session_state:
+        st.session_state.openai = True
+    if "login" not in st.session_state:
+        st.session_state.login = False
+
+    st.header("Anna Seiler Haus KI-Assistent ASH :hospital:")
+    if st.text_input("ASK_ASH_PASSWORD: ", type="password") == ASK_ASH_PASSWORD:
+        if True:
+            OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
+            # ASK_ASH_PASSWORD = False
+            OPENAI_API_KEY = False
+            OPENAI_ORG_ID = os.environ["OPENAI_ORG_ID"]
+            PINECONE_API_KEY = os.environ["PINECONE_API_KEY_LCBIM"]
+            HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"]
+            VECTARA_CORPUS_ID = "3"
+            VECTARA_API_KEY = os.environ["VECTARA_API_KEY"]
+            VECTARA_CUSTOMER_ID = os.environ["VECTARA_CUSTOMER_ID"]
+
+        user_question = st.text_input("Ask a question about your documents:")
+
+        st.session_state.openai = st.toggle(label="use openai?")
+        # if st.session_state.openai:
+          #   st.session_state.openai_key = st.text_input("openai api key", type="password")
+            # OPENAI_API_KEY = st.session_state.openai_key
+
+        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"):
+                    raw_text = get_pdf_text(pdf_docs)
+                    text_chunks = get_text_chunks(raw_text)
+                    vec = get_faiss_vectorstore(text_chunks)
+                    st.session_state.vectorstore = vec
+                    st.session_state.conversation = get_conversation_chain(vec)
+
+            # Save and Load Embeddings
+            if st.button("Save Embeddings"):
+                if "vectorstore" in st.session_state:
+                    st.session_state.vectorstore.save_local(str(datetime.now().strftime("%Y%m%d%H%M%S")) + "faiss_index")
+                    st.sidebar.success("saved")
+                else:
+                    st.sidebar.warning("No embeddings to save. Please process documents first.")
+
+            if st.button("Load Embeddings"): 
+                if "vectorstore" in st.session_state:
+                    new_db = FAISS.load_local()
+                    if new_db is not None:  # Check if this is working
+                        combined_db = merge_faiss_indices(new_db, st.session_state.vectorstore)
+                        st.session_state.vectorstore = combined_db
+                        st.session_state.conversation = get_conversation_chain(combined_db)
+                    else:
+                        st.sidebar.warning("Couldn't load embeddings")
+                else:
+                    new_db = FAISS.load_local("faiss_index")
+                    if new_db is not None:  # Check if this is working
+                        st.session_state.vectorstore = new_db
+                        st.session_state.conversation = get_conversation_chain(new_db)
+
+
+if __name__ == '__main__':
+    set_global_variables()
+    ASK_ASH_PASSWORD = os.environ["ASK_ASH_PASSWORD"]
+    main()

html_templates.py

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+css = '''
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+'''
+
+bot_template = '''
+<div class="chat-message bot">
+    <div class="avatar">
+        <img src="https://www.insel.ch/_ari/115280/49841742b8afbc44928918244fb4c6f9b487d5b3/9f6e35f65cbd0d6c47c145f90b1d5a297eb50bcd/1400/0/og/20230704-Anna-Seiler-Haus-009-screen.jpg.webp" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+user_template = '''
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://media.licdn.com/dms/image/C4D03AQHi5rJfheyUtQ/profile-displayphoto-shrink_800_800/0/1638174649461?e=2147483647&v=beta&t=KOsttcLGIwB9pBEVfceHj-ckv_zPHs-2COyrp7aYR-k">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''

requirements.txt

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+streamlit~=1.33.0
+bcrypt~=4.1.2
+psycopg2-binary~=2.9.9
+openai~=1.23.2
+pypdf2~=3.0.1
+langchain~=0.1.16
+tiktoken~=0.6.0
+numpy~=1.26.4
+requests~=2.31.0
+faiss-cpu