update application file

app.py

@@ -1,7 +1,99 @@
+import transformers
+from torch import cuda, bfloat16
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.document_loaders import HuggingFaceDatasetLoader
+from langchain.vectorstores import Chroma
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from langchain.schema import AIMessage, HumanMessage
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
+
+device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+
+embed_model = HuggingFaceEmbeddings(
+    model_name=embed_model_id,
+    model_kwargs={'device': device},
+    encode_kwargs={'device': device, 'batch_size': 4}
+)
+
+dataset_name = "beinghasnain16/company-policies"
+page_content_column = "chunk"
+hf_auth = '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'
+
+loader = HuggingFaceDatasetLoader(dataset_name, page_content_column, use_auth_token=hf_auth)
+
+data = loader.load()
+
+vectordb = Chroma.from_documents(data, embed_model)
+
+model_id = 'meta-llama/Llama-2-13b-chat-hf'
+# model_id = 'microsoft/phi-1_5'
+# model_id = 'meta-llama/Llama-2-7b-chat-hf'
+
+device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+
+# set quantization configuration to load large model with less GPU memory
+# this requires the `bitsandbytes` library
+bnb_config = transformers.BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=bfloat16
+)
+
+# begin initializing HF items, need auth token for these
+
+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,
+    quantization_config=bnb_config,
+    device_map='auto',
+    use_auth_token=hf_auth
+)
+model.eval()
+print(f"Model loaded on {device}")
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+    model_id,
+    use_auth_token=hf_auth
+)
+
+generate_text = transformers.pipeline(
+    model=model, tokenizer=tokenizer,
+    return_full_text=True,  # langchain expects the full text
+    task='text-generation',
+    # we pass model parameters here too
+    temperature=0.0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
+    max_new_tokens=512,  # mex number of tokens to generate in the output
+    repetition_penalty=1.1,  # without this output begins repeating
+)
+
+res = generate_text("Explain to me the difference between nuclear fission and fusion.")
+print(res[0]["generated_text"])
+
+llm = HuggingFacePipeline(pipeline=generate_text)
+
+rag_pipeline = RetrievalQA.from_chain_type(
+    llm=llm, chain_type='stuff',
+    retriever=vectordb.as_retriever()
+)
+
+def predict(message, history):
+    history_langchain_format = []
+    for human, ai in history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
+    history_langchain_format.append(HumanMessage(content=message))
+    llm_response = rag_pipeline(message)
+    return llm_response['result']
+
+gr.ChatInterface(predict).launch()