Upload app (1).py

app (1).py

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+import streamlit as st
+from streamlit_chat import message
+import tempfile
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import CTransformers
+from langchain.chains import ConversationalRetrievalChain
+from dl_hf_model import dl_hf_model
+from ctransformers import AutoModelForCausalLM
+from langchain_g4f import G4FLLM
+from g4f import Provider, models
+import requests
+# Define the path for generated embeddings
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+# Load the model of choice
+def load_llm():
+    # url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin"  # 2.87G
+
+    # model_loc, file_size = dl_hf_model(url)
+    
+    # llm = CTransformers(
+    #     model=model_loc,
+    #     temperature=0.2,
+    #     model_type="llama",
+    #     top_k=10,
+    #     top_p=0.9,
+    #     repetition_penalty=1.0,
+    #     max_new_tokens=512,  # adjust as needed
+    #     seed=42,
+    #     reset=True,  # reset history (cache)
+    #     stream=False,
+    #     # threads=cpu_count,
+    #     # stop=prompt_prefix[1:2],
+               
+        
+    # )
+    llm = G4FLLM(
+        model=models.gpt_35_turbo,
+        provider=Provider.DeepAi,
+    )
+    return llm
+hide_streamlit_style = """
+        <style>
+        #MainMenu {visibility: hidden;}
+        footer {visibility: hidden;}
+        </style>
+        """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+# Set the title for the Streamlit app
+st.title("Coloring Anime ChatBot")
+
+csv_url = "https://huggingface.co/spaces/uyen13/chatbot/raw/main/testchatdata.csv"
+# csv_url="https://docs.google.com/uc?export=download&id=1fQ2v2n9zQcoi6JoOU3lCBDHRt3a1PmaE"
+
+# Define the path where you want to save the downloaded file
+tmp_file_path = "testchatdata.csv"
+
+# Download the CSV file
+response = requests.get(csv_url)
+if response.status_code == 200:
+    with open(tmp_file_path, 'wb') as file:
+        file.write(response.content)
+else:
+    raise Exception(f"Failed to download the CSV file from {csv_url}")
+
+# Load CSV data using CSVLoader
+loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
+data = loader.load()
+
+# Create embeddings using Sentence Transformers
+embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
+
+# Create a FAISS vector store and save embeddings
+db = FAISS.from_documents(data, embeddings)
+db.save_local(DB_FAISS_PATH)
+
+
+# Load the language model
+llm = load_llm()
+
+# Create a conversational chain
+chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
+
+# Function for conversational chat
+def conversational_chat(query):
+    result = chain({"question": query, "chat_history": st.session_state['history']})
+    st.session_state['history'].append((query, result["answer"]))
+    return result["answer"]
+
+# Initialize chat history
+if 'history' not in st.session_state:
+    st.session_state['history'] = []
+
+# Initialize messages
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = ["Hello ! Ask me about this page like coloring book,how to buy ... 🤗"]
+
+if 'past' not in st.session_state:
+    st.session_state['past'] = ["your chat here"]
+
+# Create containers for chat history and user input
+response_container = st.container()
+container = st.container()
+
+# User input form
+with container:
+    with st.form(key='my_form', clear_on_submit=True):
+        user_input = st.text_input("ChatBox", placeholder="Ask anything...  ", key='input')
+        submit_button = st.form_submit_button(label='Send')
+
+    if submit_button and user_input:
+        output = conversational_chat(user_input)
+        st.session_state['past'].append(user_input)
+        st.session_state['generated'].append(output)
+
+# Display chat history
+if st.session_state['generated']:
+    with response_container:
+        for i in range(len(st.session_state['generated'])):
+            message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
+            message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")