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Runtime error
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Multi model select and local model loading
Browse files- __init__.py +0 -0
- app.py +12 -30
- backend/__init__.py +0 -0
- backend/config.py +1 -0
- backend/inference.py +9 -20
- backend/main.py +0 -19
- backend/utils.py +11 -0
- requirements.txt +1 -1
__init__.py
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app.py
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import streamlit as st
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import pandas as pd
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import
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st.title('Demo using Flax-Sentence-Tranformers')
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@@ -20,12 +21,12 @@ For more cool information on sentence embeddings, see the [sBert project](https:
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Please enjoy!!
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''')
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anchor = st.text_input(
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'Please enter here the main text you want to compare:'
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)
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if anchor:
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n_texts = st.sidebar.number_input(
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f'''How many texts you want to compare with: '{anchor}'?''',
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value=2,
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inputs = []
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for i in range(n_texts):
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input = st.sidebar.text_input(f'Text {i+1}:')
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inputs.append(input)
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api_base_url = 'http://127.0.0.1:8000/similarity'
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if anchor:
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if st.sidebar.button('Tell me the similarity.'):
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inputs = inputs,
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model = 'mpnet'))
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res_minilm_l6 = requests.get(url = api_base_url, params = dict(anchor = anchor,
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inputs = inputs,
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model = 'minilm_l6'))
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d_distilroberta = res_distilroberta.json()['dataframe']
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d_mpnet = res_mpnet.json()['dataframe']
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d_minilm_l6 = res_minilm_l6.json()['dataframe']
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index = list(d_distilroberta['inputs'].values())
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df_total = pd.DataFrame(index=index)
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df_total['minilm_l6'] = list(d_minilm_l6['score'].values())
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st.write('Here are the results for
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st.write(df_total)
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st.write('Visualize the results of each model:')
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st.area_chart(df_total)
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import streamlit as st
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import pandas as pd
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from backend import inference
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from backend.config import MODELS_ID
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st.title('Demo using Flax-Sentence-Tranformers')
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Please enjoy!!
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''')
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anchor = st.text_input(
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'Please enter here the main text you want to compare:'
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)
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if anchor:
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select_models = st.sidebar.multiselect("Choose models", options=MODELS_ID.keys())
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n_texts = st.sidebar.number_input(
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f'''How many texts you want to compare with: '{anchor}'?''',
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value=2,
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inputs = []
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for i in range(n_texts):
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input = st.sidebar.text_input(f'Text {i + 1}:')
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inputs.append(input)
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if anchor:
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if st.sidebar.button('Tell me the similarity.'):
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results = {model: inference.text_similarity(anchor, inputs, model) for model in select_models}
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df_results = {model: results[model] for model in results}
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index = inputs
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df_total = pd.DataFrame(index=index)
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for key, value in df_results.items():
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df_total[key] = list(value['score'].values)
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st.write('Here are the results for selected models:')
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st.write(df_total)
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st.write('Visualize the results of each model:')
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st.area_chart(df_total)
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backend/__init__.py
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backend/config.py
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MODELS_ID = dict(distilroberta = 'flax-sentence-embeddings/st-codesearch-distilroberta-base',
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mpnet = 'flax-sentence-embeddings/all_datasets_v3_mpnet-base',
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minilm_l6 = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L6')
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MODELS_ID = dict(distilroberta = 'flax-sentence-embeddings/st-codesearch-distilroberta-base',
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mpnet = 'flax-sentence-embeddings/all_datasets_v3_mpnet-base',
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mpnet_qa = 'flax-sentence-embeddings/mpnet_stackexchange_v1',
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minilm_l6 = 'flax-sentence-embeddings/all_datasets_v3_MiniLM-L6')
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backend/inference.py
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from sentence_transformers import SentenceTransformer
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import pandas as pd
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import jax.numpy as jnp
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from typing import List
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import config
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# We download the models we will be using.
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# If you do not want to use all, you can comment the unused ones.
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distilroberta_model = SentenceTransformer(config.MODELS_ID['distilroberta'])
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mpnet_model = SentenceTransformer(config.MODELS_ID['mpnet'])
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minilm_l6_model = SentenceTransformer(config.MODELS_ID['minilm_l6'])
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# Defining cosine similarity using flax.
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def cos_sim(a, b):
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return jnp.matmul(a, jnp.transpose(b))/(jnp.linalg.norm(a)*jnp.linalg.norm(b))
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# We get similarity between embeddings.
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def text_similarity(anchor: str, inputs: List[str],
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# Creating embeddings
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inputs_emb = distilroberta_model.encode([input for input in inputs])
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elif model == 'mpnet':
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anchor_emb = mpnet_model.encode(anchor)[None, :]
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inputs_emb = mpnet_model.encode([input for input in inputs])
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elif model == 'minilm_l6':
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anchor_emb = minilm_l6_model.encode(anchor)[None, :]
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inputs_emb = minilm_l6_model.encode([input for input in inputs])
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# Obtaining similarity
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similarity = list(jnp.squeeze(cos_sim(anchor_emb, inputs_emb)))
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# Returning a Pandas' dataframe
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d = {'inputs': [input for input in inputs],
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'score': [round(similarity[i],3) for i in range(len(similarity))]}
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df = pd.DataFrame(d, columns=['inputs', 'score'])
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return df.sort_values('score', ascending=False)
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import pandas as pd
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import jax.numpy as jnp
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from typing import List
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# Defining cosine similarity using flax.
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from backend.utils import load_model
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def cos_sim(a, b):
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return jnp.matmul(a, jnp.transpose(b)) / (jnp.linalg.norm(a) * jnp.linalg.norm(b))
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# We get similarity between embeddings.
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def text_similarity(anchor: str, inputs: List[str], model_name: str):
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model = load_model(model_name)
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# Creating embeddings
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anchor_emb = model.encode(anchor)[None, :]
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inputs_emb = model.encode([input for input in inputs])
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# Obtaining similarity
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similarity = list(jnp.squeeze(cos_sim(anchor_emb, inputs_emb)))
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# Returning a Pandas' dataframe
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d = {'inputs': [input for input in inputs],
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'score': [round(similarity[i], 3) for i in range(len(similarity))]}
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df = pd.DataFrame(d, columns=['inputs', 'score'])
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return df.sort_values('score', ascending=False)
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backend/main.py
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from fastapi import Query, FastAPI
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import config
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import inference
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from typing import List
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app = FastAPI()
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@app.get("/")
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def read_root():
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return {"message": "Welcome to the API of flax-sentence-embeddings."}
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@app.get('/similarity')
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def get_similarity(anchor: str, inputs: List[str] = Query([]), model: str = 'distilroberta'):
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return {'dataframe': inference.text_similarity(anchor, inputs, model)}
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#if __name__ == "__main__":
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# uvicorn.run("main:app", host="0.0.0.0", port=8080)
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backend/utils.py
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import streamlit as st
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from sentence_transformers import SentenceTransformer
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from .config import MODELS_ID
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@st.cache(allow_output_mutation=True)
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def load_model(model_name):
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assert model_name in MODELS_ID.keys()
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# Lazy downloading
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model = SentenceTransformer(MODELS_ID[model_name])
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return model
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requirements.txt
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fastapi
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sentence_transformers
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pandas
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jax
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streamlit
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sentence_transformers
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pandas
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jax
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jaxlib
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streamlit
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