Create app.py

app.py

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+import pandas as pd
+import streamlit as st
+import google.generativeai as palm
+import pandas as pd
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.embeddings import GooglePalmEmbeddings
+from langchain.llms import GooglePalm
+from langchain.document_loaders import PyPDFLoader,DirectoryLoader
+# from langchain.llms import CTransformers
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA,ConversationalRetrievalChain
+from langchain.prompts import PromptTemplate
+from langchain.cache import InMemoryCache
+from langchain.llms import VLLM
+from langchain.memory.buffer import ConversationBufferMemory
+from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
+import gradio as gr
+import requests
+import os
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+os.environ['GOOGLE_API_KEY']="AIzaSyABa4iATV5mQh0QmjGB-MntkydiGl4u0so"
+
+# models = [m for m in palm.list_models() if "generateText" in m.supported_generation_methods]
+# model = models[0].name
+# print('Imports Done')
+
+
+db_path = './vectordb/db_faiss'
+
+# print('Reading Document')
+# os.mkdir('/home/Sparsh/data')
+# url = 'https://ia803106.us.archive.org/13/items/Encyclopedia_Of_Agriculture_And_Food_Systems/Encyclopedia%20of%20Agriculture%20and%20Food%20Systems.pdf'
+# response = requests.get(url)
+# with open('/home/Sparsh/data/document.pdf', 'wb') as f:
+#          f.write(response.content)
+#
+# print('Creating Chunks')
+
+
+# loader = DirectoryLoader('C:/Users/HP/PycharmProjects/MLSCBot/venv/MLSCBot',glob = "*.pdf",loader_cls = PyPDFLoader)
+# data = loader.load()
+# splitter = RecursiveCharacterTextSplitter(chunk_size = 500,chunk_overlap = 100)
+# chunks = splitter.split_documents(data)
+#
+# print('Mapping Embeddings')
+model_name = "BAAI/bge-base-en"
+encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
+
+model_norm = HuggingFaceBgeEmbeddings(model_name=model_name,
+    encode_kwargs=encode_kwargs)
+embeddings = model_norm
+# db = FAISS.from_documents(chunks,embeddings)
+# db.save_local(db_path)
+db = FAISS.load_local(db_path,embeddings)
+print('Prompt Chain')
+
+custom_prompt_template = """You are a helpful bot designd for MLSC TIET that is Microsoft Student Learn Chapter,TIET which a technical society for thir website your task is to answer all queries about MLSC every answer you provide should be i context of MLSC if any question is not in that context then yyou should ecline that question by saying 'It is out of context',if you don't know the answer don't try to make it up just politely decline that question,you can extrapolayte the things a little just to be more informative but dont sound boasty and exaggerating say something else out of the context of the document,don't answer any questions that pertain to any specific persons and if questions about roles demnad names of position holders of MLSC give a general description of role instead of person
+You can accept some basic greetings to interact with the user but be sure to remisn in context of MLSC only
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+
+
+print('Creating LLM')
+
+llm2 = GooglePalm(
+           max_new_tokens=1024,
+           top_k=10,
+           top_p=0.5,
+           temperature=0.5)
+
+print(llm2("What is the capital of France ?"))
+# qa_chain = ConversationalRetrievalChain.from_llm(llm2,retriever=db.as_retriever(search_kwargs={'k': 2}),
+#                                        return_source_documents=False,
+#                                        memory=memory)
+qa_chain = RetrievalQA.from_chain_type(llm=llm2,
+                                      chain_type='stuff',
+                                      retriever=db.as_retriever(search_kwargs={'k': 5}),
+                                      return_source_documents=False,
+                                      chain_type_kwargs={'prompt': prompt})
+history_df = pd.DataFrame(columns = ['Question','Answer'])
+def qa_bot(query):
+  response = qa_chain({'query': query})
+  return (response['result'])
+
+st.title("MLSCBot")
+st.image('./banner.png',use_column_width=True)
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Hello!How can I help you?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = qa_bot(prompt)
+        message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+# with gr.Blocks(theme='upsatwal/mlsc_tiet') as demo:
+#     title = gr.HTML("<h1>MLSCBot</h1>")
+#     with gr.Row():
+#       img = gr.Image('C:/Users/HP/Downlo0ads/banner.png',label = 'MLSC Logo',show_label = False,elem_id = 'image',height = 200)
+#     input = gr.Textbox(label="How can I assist you?")  # Textbox for user input
+#     output = gr.Textbox(label="Here you go:")  # Textbox for chatbot response
+#     btn = gr.Button(value="Answer",elem_classes="button-chatbot",variant = "primary")  # Button to trigger the agent call
+#     btn.click(fn=qa_bot, inputs=input,outputs=output)
+# demo.launch(share=True, debug=True,show_api = False,show_error = False)