Upload app.py

app.py

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+import subprocess
+
+#sub_p_res = subprocess.run(['pip', 'install', 'langchain', 'sentence-transformers', 'transformers', 'faiss-gpu', 'PyPDF2', 'torch','llama-cpp-python'], stdout=subprocess.PIPE).stdout.decode('utf-8') #<cc-cm>
+#print("pip install downloded ", sub_p_res)
+
+
+#command = 'CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python'
+
+#sub_p_res = subprocess.run(command, shell=True, check=True)
+
+#print("llama-cpp-python GPU downloaded ",sub_p_res)
+
+
+from langchain.document_loaders.text import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema import Document
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain import PromptTemplate
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.callbacks.manager import CallbackManager
+
+
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+from huggingface_hub import hf_hub_download
+from langchain.llms import LlamaCpp
+
+import time
+
+import streamlit as st
+
+#from PyPDF2 import PdfReader
+
+# from google.colab import drive
+# drive.mount('/content/drive')
+
+loader = TextLoader("./blog_data_1.txt")
+pages = loader.load()
+
+def split_text(documents: list[Document]):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=150,
+        length_function=len,
+        add_start_index=True,
+    )
+    chunks = text_splitter.split_documents(documents)
+    print(f"Split {len(documents)} documents into {len(chunks)} chunks.")
+
+    document = chunks[10]
+    print(document.page_content)
+    print(document.metadata)
+
+    return chunks
+
+chunks_text = split_text(pages)
+
+print("chunks")
+
+
+# def Pdf_to_text(path) :
+#     pdf_reader = PdfReader(path)
+
+#     text = ""
+#     for page in pdf_reader.pages:
+#         text += page.extract_text()
+
+#     text_splitter = RecursiveCharacterTextSplitter(
+#         chunk_size=1000,
+#         chunk_overlap=200,
+#         length_function=len
+#         )
+#     chunks = text_splitter.split_text(text=text)
+#     return chunks
+
+#chunks_pdf = Pdf_to_text("./drive/MyDrive/Colab Notebooks/hackathon_MoroccoAI/Doing-Business-Guide-Morocco.pdf")
+
+#embeddings = HuggingFaceEmbeddings(model_name="all-mpnet-base-v2")
+embedding = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') # machi top
+
+docs_text = [doc.page_content for doc in chunks_text]
+
+# final_chunks = []
+
+# # for chunk in chunks_pdf :
+# #   final_chunks.append(chunk)
+
+# for chunk in docs_text :
+#     final_chunks.append(chunk)
+
+VectorStore = FAISS.from_texts(docs_text, embedding=embedding)
+
+MODEL_ID = "TheBloke/Mistral-7B-OpenOrca-GGUF"
+MODEL_BASENAME = "mistral-7b-openorca.Q4_K_M.gguf"
+
+model_path = hf_hub_download(
+            repo_id=MODEL_ID,
+            filename=MODEL_BASENAME,
+            resume_download=True,
+        )
+
+print("model_path : ", model_path)
+
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+
+CONTEXT_WINDOW_SIZE = 1500
+MAX_NEW_TOKENS = 2000
+N_BATCH = 512
+n_gpu_layers = 40
+kwargs = {
+          "model_path": model_path,
+          "n_ctx": CONTEXT_WINDOW_SIZE,
+          "max_tokens": MAX_NEW_TOKENS,
+          "n_batch": N_BATCH,
+          "n_gpu_layers": n_gpu_layers,
+          "callback_manager": callback_manager,
+          "verbose":True,
+      }
+
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.chains import LLMChain
+from langchain.llms import LlamaCpp
+
+# Callbacks support token-wise streaming
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+n_gpu_layers = 40  # Change this value based on your model and your GPU VRAM pool.
+n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
+max_tokens = 2000
+# Make sure the model path is correct for your system!
+llm = LlamaCpp(
+    model_path=model_path,
+    n_gpu_layers=n_gpu_layers,
+
+    n_batch=n_batch,
+    max_tokens= max_tokens,
+    callback_manager=callback_manager,
+    verbose=True,  # Verbose is required to pass to the callback manager
+)
+
+llm = LlamaCpp(**kwargs)
+
+memory = ConversationBufferMemory(
+    memory_key="chat_history",
+    return_messages=True,
+    input_key='question',
+    output_key='answer'
+)
+
+# memory.clear()
+
+qa = ConversationalRetrievalChain.from_llm(
+    llm,
+    chain_type="stuff",
+    retriever=VectorStore.as_retriever(search_kwargs={"k": 5}),
+    memory=memory,
+    return_source_documents=True,
+    verbose=False,
+)
+
+# start = time.time()
+# res = qa(f"""
+# I'm intressted in starting the buisness in Casa , what I should do next?""")
+# end = time.time()
+# execution_time = end - start
+
+
+#---------------------------------------------------------
+
+import streamlit as st
+import time
+
+# App title
+st.set_page_config(page_title="🤖💼 🇲🇦 Financial advisor is Here")
+
+# Replicate Credentials
+with st.sidebar:
+    st.title(' Mokawil.AI is Here 🤖💼 🇲🇦')
+    st.markdown('📖 an AI-powered advisor designed to assist founders (or anyone aspiring to start their own company) with various aspects of business in Morocco, including legal considerations, budget planning, available investors, and strategies for success.')
+
+# Store LLM generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+# Display or clear chat messages
+for message in st.session_state.messages:
+    if message["role"] == "user" :
+      with st.chat_message(message["role"], avatar="👨‍💻"):
+          st.write(message["content"])
+    else : 
+      with st.chat_message(message["role"], avatar="🤖"):
+          st.write(message["content"])
+
+def clear_chat_history():
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+
+# Function for generating LLaMA2 response
+def generate_llama2_response(prompt_input):
+    res = qa(f'''{prompt_input}''')
+    return res['answer']
+
+# User-provided prompt
+if prompt := st.chat_input("What is up?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user", avatar="👨‍💻"):
+        st.write(prompt)
+
+# Generate a new response if last message is not from assistant
+if st.session_state.messages[-1]["role"] != "assistant":
+  with st.chat_message("assistant", avatar="🤖"):
+      with st.spinner("Thinking..."):
+          response = generate_llama2_response(st.session_state.messages[-1]["content"])
+      placeholder = st.empty()
+      full_response = ''
+      for item in response:
+          full_response += item
+          placeholder.markdown(full_response)
+          time.sleep(0.05)
+      placeholder.markdown(full_response)
+  message = {"role": "assistant", "content": full_response}
+  st.session_state.messages.append(message)
+
+# Example prompt
+with st.sidebar : 
+    st.title('Input examples')
+def promptExample1():
+    prompt = "how can I start my company example 1"
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+# Example prompt
+def promptExample2():
+    prompt = "how can I start my company example 2"
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+# Example prompt
+def promptExample3():
+    prompt = "how can I start my company example 3"
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+
+st.sidebar.button('how can I start my company in morocco?', on_click=promptExample1)
+st.sidebar.button('What are some recommended cities for starting a business in finance', on_click=promptExample2)
+st.sidebar.button('what is the estimate money I need for starting my company', on_click=promptExample3)