Update app.py

app.py

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+import streamlit as st
+import transformers
+import torch
+from langchain.llms import HuggingFacePipeline
+from langchain.document_loaders import WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from transformers import StoppingCriteria, StoppingCriteriaList
+
+# Load the Llama model and setup the conversation pipeline
+model_id = 'meta-llama/Llama-2-7b-chat-hf'
+# Add your authentication token here
+hf_auth = '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'
+
+# Load Llama model
+model_config = transformers.AutoConfig.from_pretrained(model_id, use_auth_token=hf_auth)
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    config=model_config,
+    device_map='auto',
+    use_auth_token=hf_auth
+)
+
+# Initialize the Llama pipeline
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, use_auth_token=hf_auth)
+
+bnb_config = transformers.BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+stop_list = ['\nHuman:', '\n```\n']
+stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
+stop_token_ids = [torch.LongTensor(x).to('cuda') for x in stop_token_ids]
+
+stopping_criteria = StoppingCriteriaList([transformers.StoppingCriteria(max_length=1024)])
+
+generate_text = transformers.pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    return_full_text=True,
+    task='text-generation',
+    stopping_criteria=stopping_criteria,
+    temperature=0.1,
+    max_new_tokens=512,
+    repetition_penalty=1.1
+)
+
+llm = HuggingFacePipeline(pipeline=generate_text)
+
+# Load source documents
+web_links = ["https://www.techtarget.com/whatis/definition/transistor",
+             "https://en.wikipedia.org/wiki/Transistor",
+             # Add more source links as needed
+             ]
+
+loader = WebBaseLoader(web_links)
+documents = loader.load()
+
+# Split source documents
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
+all_splits = text_splitter.split_documents(documents)
+
+# Create embeddings and vector store
+model_name = "sentence-transformers/all-mpnet-base-v2"
+model_kwargs = {"device": "cuda"}
+
+embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
+vectorstore = FAISS.from_documents(all_splits, embeddings)
+
+# Create the conversation retrieval chain
+chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
+
+# Streamlit app
+def main():
+    st.title("AI Chatbot")
+
+    user_question = st.text_input("Ask a question:")
+
+    sources = [
+        "Source 1",
+        "Source 2",
+        "Source 3",
+        # Add more sources as needed
+    ]
+    selected_source = st.selectbox("Select a source:", sources)
+
+    if st.button("Get Answer"):
+        chat_history = []
+
+        query = user_question
+        result = chain({"question": query, "chat_history": chat_history})
+
+        st.write("Answer:", result["answer"])
+
+        chat_history.append((query, result["answer"]))
+
+        if "source_documents" in result:
+            st.write("Source Documents:")
+            for source_doc in result["source_documents"]:
+                st.write(source_doc)
+
+if __name__ == "__main__":
+    main()