Update app.py

app.py

@@ -1,63 +1,200 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import os
+import time
+import re
+import random
+import streamlit as st
+from streamlit_chat import message
+# from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.memory import ConversationSummaryBufferMemory
+from langchain.llms import OpenAI
+from langchain.chains import ConversationalRetrievalChain, ConversationChain
+from langchain import PromptTemplate
+import qdrant_client
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Qdrant
+
+from dotenv import load_dotenv
+load_dotenv(".env")
+
+
+prompt_template = """Use the following pieces of context to answer the question at the end. If you don't
+ know the answer, or similar answer is not in the context, you should say that 'I've searched my database,
+ but I couldn't locate the exact information you're looking for. May be you want to be more specific
+ in your search. Or checkout similar documents'.
+ Answer user greetings and ask them what they i'd like to learn about. You are a bot that teaches users
+ about american law codes
+
+Context: {context}
+Question: {question}
+Helpful Answer:"""
+QA_PROMPT_ERROR = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
+
+# Use different logo
+def logo(logo: str = None) -> str:
+    logos = [
+
+        "https://res.cloudinary.com/webmonc/image/upload/v1696515089/3558860_r0hs4y.png"
+    ]
+    logo = random.choice(logos)
+    return logo
+
+
+memory = ConversationSummaryBufferMemory(
+    llm=OpenAI(
+        temperature=0),
+    max_token_limit=150,
+    memory_key='chat_history',
+    return_messages=True,
+    output_key='answer')
+
+
+# Streamlit Component
+
+st.set_page_config(
+    page_title="USA Law Codes",
+    # page_icon=":robot:"
+    page_icon=":us:"
 )
 
+st.header("📋 ChatBot for Learning About USA Laws")
+# st.title("👋 📝 ChatBot for Learning About American Laws")
+user_city = st.selectbox("Select a City", ("Maricopa", "LAH", "PGC"))
+
+
+hide_st_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            header {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_st_style, unsafe_allow_html=True)
+
+
+if 'responses' not in st.session_state:
+    st.session_state['responses'] = ["I'm here to assist you!"]
+
+if 'requests' not in st.session_state:
+    st.session_state['requests'] = []
+
+if 'buffer_memory' not in st.session_state:
+    st.session_state.buffer_memory = memory
+
+
+# connect to a Qdrant Cluster
+client = qdrant_client.QdrantClient(
+    url=os.getenv("QDRANT_HOST"),
+    api_key=os.getenv("QDRANT_API_KEY")
+)
+
+embeddings = OpenAIEmbeddings()
+
+
+# Change Db base on city
+def connect_db(db: str = None) -> str:
+    db = user_city
+    if user_city == "LAH":
+        db = "collection_two"  # I.e set a collection/DB name
+    elif db == "Maricopa":
+        db = "test3"
+    elif db == "PGC":
+        db = "pgc"
+
+    vector_store = Qdrant(
+        client=client,
+        collection_name=db,
+        embeddings=embeddings
+    )
+    return vector_store
+
+
+def get_urls(doc: str = None) -> "list[str]":
+    url_regex = '(http[s]?://?[A-Za-z0-9–_\\.\\-]+\\.[A-Za-z]+/?[A-Za-z0-9$\\–_\\-\\/\\.\\?]*)[\\.)\"]*'
+    url = re.findall(url_regex, doc)
+    return url
+
+
+def print_answer_metadata(result: "list[dict]") -> str:
+    links = []
+    output_answer = ""
+    output_answer += result['answer']
+    for doc in result['source_documents']:
+        link = get_urls(doc.page_content)
+        links.extend(link)
+    link = "\n".join(links)
+
+    if links != []:
+        output_answer += "\n" + "See also: " + link
+
+    # print("OUT", output_answer)
+    return output_answer
+
+
+def print_page_content(result: "list[dict]") -> str:
+    extracted_string = ""
+
+    for doc in result['source_documents']:
+
+        page_content = doc.page_content[:200] + "..."
+
+        title = doc.page_content[0:35] + "..."
+        if page_content and title:
+            extracted_string += f"<hr><h4>Document Title:</h4> {title}\n\n\n <h4>Excerpt:</h4>\
+                {page_content}\n\n"
+    return extracted_string
+
+
+qa = ConversationalRetrievalChain.from_llm(
+    OpenAI(temperature=0),
+    connect_db().as_retriever(),
+    memory=st.session_state.buffer_memory,
+    verbose=True,
+    return_source_documents=True,
+    combine_docs_chain_kwargs={'prompt': QA_PROMPT_ERROR})
+
+
+response_container = st.container()
+textcontainer = st.container()
+
+details = ''
+
+with textcontainer:
+    query = st.text_input("You: ", key="input", placeholder="start chat")
+    submit = st.button("send")
+    if submit:
+        res = qa({"question": query})
+        response = print_answer_metadata(res)
+        details = print_page_content(res)
+        st.session_state.requests.append(query)
+        st.session_state.responses.append(response)
+
+
+with response_container:
+    if st.session_state['responses']:
+        for i in range(len(st.session_state['responses'])):
+            message(
+                st.session_state['responses'][i],
+                key=str(i),
+                avatar_style="no-avatar",
+                logo=logo(),
+                allow_html=True)
+            if i < len(st.session_state['requests']):
+                message(
+                    st.session_state["requests"][i],
+                    is_user=True,
+                    key=str(i) + '_user',
+                    allow_html=True
+                )
+
+
+with st.sidebar:
+    st.image("https://res.cloudinary.com/webmonc/image/upload/v1696603202/Bot%20Streamlit/law_justice1_yqaqvd.jpg")
 
-if __name__ == "__main__":
-    demo.launch()
+    if details:
+        with st.spinner("Processing..."):
+            time.sleep(1)
+            st.markdown('__Similar Documents__')
+        st.markdown(f'''<small>{details}</small>''', unsafe_allow_html=True)
+