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app.py

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+# import subprocess
+
+# sub_p_res = subprocess.run(['pip', 'install', 'langchain', 'sentence-transformers', 'transformers', 'faiss-gpu', 'PyPDF2',"gradio_client"], 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 gradio_client import Client
+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
+from langchain.chains import LLMChain
+
+import time
+import streamlit as st
+
+
+
+loader = TextLoader("Data_blog.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)
+
+embedding = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') # machi top
+
+docs_text = [doc.page_content for doc in chunks_text]
+
+
+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,
+      }
+
+
+# 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,
+)
+def translate(text, source="English", target="Moroccan Arabic"):
+    client = Client("https://facebook-seamless-m4t-v2-large.hf.space/--replicas/2bmbx/")
+    result = client.predict(
+        text,
+        source,
+        target,
+        api_name="/t2tt"
+    )
+    return result
+
+#---------------------------------------------------------
+
+import streamlit as st
+import time
+
+
+# App title
+st.set_page_config(page_title="🤖💼 🇲🇦 Financial advisor is Here",
+                   page_icon="🤖")
+
+# 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. This includes legal considerations, budget planning, strategies for success, and much more.')
+    
+# 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="user.png"):
+          st.write(message["content"])
+    else : 
+      with st.chat_message(message["role"], avatar="logo.png"):
+          st.write(message["content"])
+
+def clear_chat_history():
+    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,
+    )
+    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
+
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+selected_language = st.sidebar.selectbox("Select Language", ["English", "Darija"], index=0)  # English is the default
+
+# Function for generating LLaMA2 response
+def generate_llm_response(prompt_input):
+    res = qa(f'''{prompt_input}''')
+
+    if selected_language == "Darija":
+        translated_response = translate(res['answer'])
+        return translated_response
+    else:
+        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="user.png"):
+        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="logo.png"):
+      with st.spinner("Thinking..."):
+          response = generate_llm_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('Examples :')
+
+def promptExample1():
+    prompt = "How can I start my company in Morocco?"
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+# Example prompt
+def promptExample2():
+    prompt = "What are some recommended cities for starting a business in the finance sector?"
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+# Example prompt
+def promptExample3():
+    prompt = "What is the estimated amount of money I need to start my company?"
+    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 the finance sector?', on_click=promptExample2)
+st.sidebar.button('What is the estimated amount of money I need to start my company?', on_click=promptExample3)
+
+
+with st.sidebar:
+    st.title('Disclaimer ⚠️:')
+    st.markdown('may introduce false information')
+    st.markdown('consult with a preofessionel advisor for more specific problems')

requirements.txt

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+langchain
+sentence-transformers
+transformers
+faiss-gpu
+PyPDF2
+torch
+llama-cpp-python
+gradio_client