Update app.py
Browse files
app.py
CHANGED
@@ -1,140 +1,140 @@
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import torch
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import transformers
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from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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dataset_path = "./5k_index_data/my_knowledge_dataset"
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index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
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tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
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retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
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passages_path = dataset_path,
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index_path = index_path,
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n_docs = 5)
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rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
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rag_model.retriever.init_retrieval()
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rag_model.to(device)
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model = AutoModelForCausalLM.from_pretrained('google/gemma-
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device_map = 'auto',
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torch_dtype = torch.bfloat16,
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)
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def strip_title(title):
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if title.startswith('"'):
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title = title[1:]
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if title.endswith('"'):
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title = title[:-1]
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return title
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# getting the correct format to input in gemma model
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def input_format(query, context):
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sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
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message = f'Question: {query}'
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return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
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# retrieving and generating answer in one call
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def retrieved_info(query, rag_model = rag_model, generating_model = model):
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# Tokenize Query
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retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
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[query],
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return_tensors = 'pt',
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padding = True,
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truncation = True,
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)['input_ids'].to(device)
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# Retrieve Documents
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question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
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question_encoder_pool_output = question_encoder_output[0]
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result = rag_model.retriever(
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retriever_input_ids,
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question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
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prefix = rag_model.rag.generator.config.prefix,
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n_docs = rag_model.config.n_docs,
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return_tensors = 'pt',
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)
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# Preparing query and retrieved docs for model
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all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
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retrieved_context = []
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for docs in all_docs:
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titles = [strip_title(title) for title in docs['title']]
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texts = docs['text']
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for title, text in zip(titles, texts):
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retrieved_context.append(f'{title}: {text}')
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generation_model_input = input_format(query, retrieved_context)
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# Generating answer using gemma model
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-
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input_ids = tokenizer(generation_model_input, return_tensors='pt').to(device)
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output = generating_model.generate(input_ids, max_new_tokens = 512)
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return tokenizer.decode(output[0])
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def respond(
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message,
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history: list[tuple[str, str]],
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system_message,
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max_tokens ,
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temperature,
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top_p,
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):
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if message: # If there's a user query
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response = retrieved_info(message) # Get the answer from your local FAISS and Q&A model
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return response
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# In case no message, return an empty string
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return ""
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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# Custom title and description
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title = "🧠 Welcome to Your AI Knowledge Assistant"
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description = """
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My capabilities are limited because I am still in development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
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"""
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demo = gr.ChatInterface(
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respond,
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type = 'messages',
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additional_inputs=[
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gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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title=title,
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description=description,
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textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
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examples=[["✨Future of AI"], ["📱App Development"]],
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example_icons=["🤖", "📱"],
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theme="compact",
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)
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if __name__ == "__main__":
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demo.launch(share = True )
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import torch
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import transformers
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from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM
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import gradio as gr
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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dataset_path = "./5k_index_data/my_knowledge_dataset"
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index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
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tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
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retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
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passages_path = dataset_path,
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index_path = index_path,
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n_docs = 5)
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rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
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rag_model.retriever.init_retrieval()
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rag_model.to(device)
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model = AutoModelForCausalLM.from_pretrained('google/gemma-7b-it',
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device_map = 'auto',
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torch_dtype = torch.bfloat16,
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)
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def strip_title(title):
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if title.startswith('"'):
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title = title[1:]
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if title.endswith('"'):
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title = title[:-1]
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return title
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# getting the correct format to input in gemma model
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def input_format(query, context):
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sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
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message = f'Question: {query}'
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return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
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# retrieving and generating answer in one call
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def retrieved_info(query, rag_model = rag_model, generating_model = model):
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# Tokenize Query
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retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
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[query],
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return_tensors = 'pt',
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padding = True,
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truncation = True,
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)['input_ids'].to(device)
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# Retrieve Documents
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question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
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question_encoder_pool_output = question_encoder_output[0]
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result = rag_model.retriever(
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retriever_input_ids,
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question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
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prefix = rag_model.rag.generator.config.prefix,
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n_docs = rag_model.config.n_docs,
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return_tensors = 'pt',
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)
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# Preparing query and retrieved docs for model
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all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
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retrieved_context = []
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for docs in all_docs:
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titles = [strip_title(title) for title in docs['title']]
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texts = docs['text']
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for title, text in zip(titles, texts):
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retrieved_context.append(f'{title}: {text}')
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generation_model_input = input_format(query, retrieved_context)
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# Generating answer using gemma model
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")
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input_ids = tokenizer(generation_model_input, return_tensors='pt').to(device)
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output = generating_model.generate(input_ids, max_new_tokens = 512)
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return tokenizer.decode(output[0])
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def respond(
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message,
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history: list[tuple[str, str]],
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system_message,
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max_tokens ,
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temperature,
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top_p,
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):
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if message: # If there's a user query
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response = retrieved_info(message) # Get the answer from your local FAISS and Q&A model
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return response
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# In case no message, return an empty string
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return ""
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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# Custom title and description
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title = "🧠 Welcome to Your AI Knowledge Assistant"
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description = """
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Hi!! I am your loyal assistant. My functionality is based on the RAG model. I retrieve relevant information and provide answers based on that. Ask me any questions, and let me assist you.
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My capabilities are limited because I am still in the development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
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"""
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demo = gr.ChatInterface(
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respond,
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type = 'messages',
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additional_inputs=[
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gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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title=title,
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description=description,
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textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
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examples=[["✨Future of AI"], ["📱App Development"]],
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example_icons=["🤖", "📱"],
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theme="compact",
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)
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if __name__ == "__main__":
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demo.launch(share = True )
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