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app.py

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+
+import logging
+import sys
+import gradio as gr
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
+from llama_index.llms import HuggingFaceLLM
+
+
+documents = SimpleDirectoryReader(
+    input_files=["bio.pdf"]
+).load_data()
+
+from llama_index.prompts.prompts import SimpleInputPrompt
+
+
+system_prompt = "You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided."
+
+
+
+# This will wrap the default prompts that are internal to llama-index
+query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
+
+from huggingface_hub import login
+login(token="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")
+
+
+import torch
+
+llm = HuggingFaceLLM(
+    context_window=4096,
+    max_new_tokens=256,
+    generate_kwargs={"temperature": 0.0, "do_sample": False},
+    system_prompt=system_prompt,
+    query_wrapper_prompt=query_wrapper_prompt,
+    tokenizer_name="meta-llama/Llama-2-7b-chat-hf",
+    model_name="meta-llama/Llama-2-7b-chat-hf",
+    device_map="auto",
+    # uncomment this if using CUDA to reduce memory usage
+    model_kwargs={"torch_dtype": torch.float16 , "load_in_8bit":True}
+)
+
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index import LangchainEmbedding, ServiceContext
+
+embed_model = LangchainEmbedding(
+  HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+)
+
+service_context = ServiceContext.from_defaults(
+    chunk_size=1024,
+    llm=llm,
+    embed_model=embed_model
+)
+
+index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+
+
+query_engine = index.as_query_engine()
+
+# Define a function to get responses from your Q&A model
+def get_response(query):
+    response = query_engine.query(query)
+    return response
+
+# Create an input component for user queries
+query_input = gr.inputs.Textbox(label="Enter your question", lines=2)
+
+# Create an output component to display the response
+response_output = gr.outputs.Textbox(label="Response")
+
+# Create a Gradio interface
+gr.Interface(
+    fn=get_response,
+    inputs=query_input,
+    outputs=response_output,
+    title="Q&A Assistant",
+    description="Ask a question and get an answer based on the provided documents.",
+).launch()

requirements.txt

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+pypdf
+python-dotenv
+transformers
+einops 
+accelerate 
+langchain 
+bitsandbytes
+sentence_transformers
+llama-index
+gradio
+huggingface_hub