app.py create
Browse files
app.py
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__import__('pysqlite3')
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import sys
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sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
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import os
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import gradio as gr
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import chromadb
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from sentence_transformers import SentenceTransformer
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import pandas as pd
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import numpy as np
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from chromadb.utils import embedding_functions
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from huggingface_hub import InferenceClient
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from dotenv import load_dotenv, find_dotenv
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_ = load_dotenv(find_dotenv())
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hf_api_key = os.environ['HF_API_KEY']
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dfs = pd.read_csv('Patents.csv')
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ids= [str(x) for x in dfs.index.tolist()]
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docs = dfs['text'].tolist()
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client = chromadb.Client()
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collection = client.get_or_create_collection("patents")
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collection.add(documents=docs,ids=ids)
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def text_embedding(text)-> None:
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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return model.encode(text)
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def gen_context(query):
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vector = text_embedding(query).tolist()
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results = collection.query(
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query_embeddings=vector,n_results=15,include=["documents"])
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res = "\n".join(str(item) for item in results['documents'][0])
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return res
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def chat_completion(user_prompt):
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length = 1000
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system_prompt = """\
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You are a helpful AI assistant that can answer questions on the patents dataset. Answer based on the context provided.If you cannot find the correct answer, say I don't know. Be concise and just include the response"""
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final_prompt = f"""<s>[INST]<<SYS>>
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{system_prompt}
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<</SYS>>
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{user_prompt}[/INST]"""
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return client.text_generation(prompt=final_prompt,max_new_tokens = length).strip()
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client = InferenceClient(model = "mistralai/Mixtral-8x7B-Instruct-v0.1")
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def chat_completion(query):
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length = 1000
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context = gen_context(query)
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user_prompt = f"""
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Based on the context:
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{context}
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Answer the below query:
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{query}
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"""
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system_prompt = """\
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You are a helpful AI assistant that can answer questions on the patents dataset. Answer based on the context provided.If you cannot find the correct answer, say I don't know. Be concise and just include the response"""
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final_prompt = f"""<s>[INST]<<SYS>>
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{system_prompt}
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<</SYS>>
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{user_prompt}[/INST]"""
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return client.text_generation(prompt=final_prompt,max_new_tokens = length).strip()
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demo = gr.Interface(fn=chat_completion,
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inputs=[gr.Textbox(label="Query", lines=2)],
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outputs=[gr.Textbox(label="Result", lines=16)],
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title="Chat on Patents Data")
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demo.queue().launch(share=True)
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