Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+getting_real_basecamp.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,187 @@
+import os
+from io import BytesIO
+from PIL import Image
+import google.generativeai as genai
+import google.ai.generativelanguage as glm
+from langchain.vectorstores import Chroma
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import streamlit as st
+
+st.title("DocsGPT")
+
+genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
+
+st.markdown(
+    """
+    <style>
+    .css-1jc7ptx, .e1ewe7hr3, .viewerBadge_container__1QSob,
+    .styles_viewerBadge__1yB5_, .viewerBadge_link__1S137,
+    .viewerBadge_text__1JaDK {
+        display: none;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+rag = glm.Tool(
+    function_declarations=[
+      glm.FunctionDeclaration(
+        name='vector_search',
+        description="Returns the content of the document user attached. Make sure that your not passing query as a question use like **keywords** instead. Use this function to search for contents in the user attached or uploaded documents to you. Try not to completly paste the user question as query, instead use keywords.",
+        parameters=glm.Schema(
+            type=glm.Type.OBJECT,
+            properties={
+                'query': glm.Schema(type=glm.Type.STRING),
+            },
+            required=['query']
+        )
+      )
+    ]
+)
+
+gemini = genai.GenerativeModel('gemini-pro', tools=[rag])
+gemini_vision = genai.GenerativeModel('gemini-pro-vision')
+
+class rawkn:
+    def __init__(self, text):
+        self.text = text
+    def get_relevant_documents(self, query):
+        return self.text
+
+def loader_data(files, include_getting_real):
+    file_type = files[0].type if len(files) > 0 else "application/pdf"
+    total_content = ''
+    num_pages = 0
+    if include_getting_real:
+        files.append("./getting_real_basecamp.pdf")
+    for file in files:
+        if file_type == "application/pdf":
+            pdf_reader = PdfReader(file)
+            content = ''
+            for page in pdf_reader.pages:
+                num_pages += 1
+                content += page.extract_text()
+                for img in page.images:
+                    try:
+                        image_stream = BytesIO(img.data)
+                        img = Image.open(image_stream)
+                        img_desc = gemini_vision.generate_content(["Generate a detailed description of the image. If it is a flow chart, please create a flowchart that exactly as it is. If it is table, try to create a table exactly like in the image. write all the text in the image it it contains any text. Clearly explain the image in more detailed.\nAlso make sure give a nice heading to the image contant.", img]).candidates[0].content.parts[0].text
+                        print("***************************")
+                        print(img_desc)
+                        print("***************************")
+                        content += "Image content:\n" + img_desc
+                    except:
+                        print("cannot extract image")
+
+        if file_type == "text/plain":
+            content = file.read()
+            content = content.decode("utf-8")
+        total_content += content
+
+    if num_pages <= 2:
+        chunk_size = 500
+    elif num_pages <= 3:
+        chunk_size = 1000
+    elif num_pages <= 5:
+        chunk_size = 2000
+    elif num_pages <= 10:
+        chunk_size = 3000
+    else:
+        chunk_size = 4000
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0)
+    texts = text_splitter.split_text(total_content)
+    try:
+        embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+        vector_store = Chroma.from_texts(texts, embeddings).as_retriever()
+        st.session_state.knowledge = vector_store
+        st.session_state.chat.history.append(glm.Content(
+            parts=[glm.Part(
+                text=f"Now i've uploaded some files.\nHere are the list of documents you have access to:\n{[i.name if type(i) != str else i for i in files]}"
+            )],
+            role="user"
+        )
+        )
+        st.session_state.chat.history.append(glm.Content(
+            parts=[glm.Part(
+                text=f"Sure! Ask me anything about the documents you have uploaded. I can help you with that."
+            )],
+            role="model"
+        )
+        )
+    except:
+        st.session_state.knowledge = rawkn(total_content)
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+if "knowledge" not in st.session_state:
+    st.session_state.knowledge = None
+
+if "chat" not in st.session_state:
+    st.session_state.chat = gemini.start_chat(history=[glm.Content(
+            parts=[glm.Part(
+                text="Your name is DocsGPT. You are very helpful and can assist with documents uploaded by the user. Use the vector_search tool/function to search for contents in the user attached or uploaded documents to you.\nYou have access to all documents uploaded by the user."
+            )],
+            role="user"
+        ),
+        glm.Content(
+            parts=[glm.Part(
+                text="Sure, i can do that for you."
+            )],
+            role="model"
+        )])
+
+for history in st.session_state.history:
+    with st.chat_message(history["role"]):
+        st.markdown(history["text"])
+
+with st.sidebar:
+    st.title("Knowledge")
+    st.markdown("""### Tips to use DocsGPT:
+- Upload your documents [pdf, txt] to DocsGPT and make sure to click on the process button.
+- wait for a second and then start chatting with DocsGPT.
+- While asking questions to DocsGPT about your uploaded files, please refer your uploaded files as *Document*, *Docs*, *attached or uploaded docs*, so the model can easily understands what you are referring to.""")
+    files = st.file_uploader("Upload a file", accept_multiple_files=True, type=["pdf", "txt"])
+    include_getting_real = st.checkbox("Include getting-real?")
+    process = st.button("Process")
+    if process and files:
+        with st.spinner('loading your file. This may take a while...'):
+            loader_data(files, include_getting_real)
+    elif process and include_getting_real:
+        with st.spinner('loading your file. This may take a while...'):
+            loader_data([], include_getting_real)
+
+if prompt := st.chat_input("Enter your message..."):
+    st.session_state.history.append({"role": "user", "text": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        response = st.session_state.chat.send_message(prompt)
+        if response.candidates[0].content.parts[0].text == '':
+            args = response.candidates[0].content.parts[0].function_call.args['query']
+            if st.session_state.knowledge is not None:
+                print("searching for ", args)
+                related_docs = str(st.session_state.knowledge.get_relevant_documents(args))
+                print(related_docs)
+            else:
+                related_docs = 'No knowledge documents loaded'
+            response = st.session_state.chat.send_message(
+                glm.Content(
+                    parts=[glm.Part(
+                        function_response = glm.FunctionResponse(
+                        name='vector_search',
+                        response={'rag': related_docs},
+                        )
+                    )]
+                )
+            ).candidates[0].content.parts[0].text
+        else:
+            response = response.candidates[0].content.parts[0].text
+        print(st.session_state.chat.history)
+        message_placeholder.markdown(response)
+    st.session_state.history.append({"role": "assistant", "text": response})

getting_real_basecamp.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a369da3ab9d824af8eddc9bfbaa6f8d9ae4a6cc3981f0bb92c2b19e46a563af
+size 5118368

requirements.txt

@@ -0,0 +1,8 @@
+langchain
+pypdf
+PyPDF2
+chromadb
+langchain-google-genai
+langchain-community
+streamlit
+google-generativeai