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.gitattributes

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Dockerfile

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-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: My Second Docker App
-emoji: 👁
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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-import streamlit as st
-import pandas as pd
-import numpy as np
-# from scipy.special import softmax
-# import os
-from utils import run_sentiment_analysis, preprocess
-from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification
-import os
-import time
-
-# Requirements
-model_path = "bright1/fine-tuned-distilbert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-config = AutoConfig.from_pretrained(model_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-# dark_theme = set_theme()
-
-
-st.set_page_config(
-    page_title="Tweet Analyzer",
-    page_icon="🤖",
-    initial_sidebar_state="expanded",
-    menu_items={
-        'About': "# This is a header. This is an *extremely* cool app!"
-    }
-)  
-
-
-my_expander  = st.container()
-
-
-# st.sidebar.selectbox('Menu', ['About', 'Model'])
-with my_expander:
-    
-    st.markdown("""
-        <style>
-        h1 {
-            text-align: center;
-        }
-        </style>
-        """, unsafe_allow_html=True)
-    st.title(':green[Covid-19 Vaccines Tweets Analyzer]')
-    st.sidebar.markdown("""
-    ## Demo App
-
-    This app analyzes your tweets on covid vaccines and classifies them us Neutral, Negative or Positive
-    """)        
-    # my_expander.write('Container')
-    # create a three column layout
-
-    col1, col2, col3 = st.columns((1.6, 1,0.3))
-    # col2.markdown("""
-    #         <p style= font-color:red>
-    #             Results from Analyzer
-    #         </p>
-    # """,unsafe_allow_html=True)
-    st.markdown("""
-        <style>
-        p {
-            font-color: blue;
-        }
-        </style>
-        """, unsafe_allow_html=True)
-    tweet = col1.text_area('Tweets to analyze',height=200, max_chars=520, placeholder='Write your Tweets here')
-    colA, colb, colc, cold = st.columns(4)
-    clear_button = colA.button(label='Clear', type='secondary', use_container_width=True)
-    submit_button = colb.button(label='Submit', type='primary', use_container_width=True)
-    empty_container = col2.container()
-    empty_container.text("Results from Analyzer")
-    empty_container2 = col3.container()
-    empty_container2.text('Scores')
-    text = preprocess(tweet)
-    results = run_sentiment_analysis(text=text, model=model, tokenizer=tokenizer)
-    if submit_button:
-        success_message = st.success('Success', icon="✅")
-        
-        with empty_container:
-            
-            neutral = st.progress(value=results['Neutral'], text='Neutral',)
-            negative = st.progress(value=results['Negative'], text='Negative')
-            positive = st.progress(value=results['Positive'], text='Positive')
-        with empty_container2:
-            st.markdown(
-                """
-            <style>
-            [data-testid="stMetricValue"] {
-                font-size: 20px;
-            }
-            </style>
-            """,
-                unsafe_allow_html=True,
-            )
-            neutral_score = st.metric(label='Score', value=round(results['Neutral'], 4), label_visibility='collapsed')
-            negative_score = st.metric(label='Score', value=round(results['Negative'], 4), label_visibility='collapsed')
-            positive_score = st.metric(label='Score', value=round(results['Positive'], 4), label_visibility='collapsed')
-            time.sleep(5)
-            success_message.empty()
-    interpret_button = col2.button(label='Interpret',type='secondary', use_container_width=True)
-
-
-# st.help()
-    # create a date input to receive date
-

requirements.txt

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-streamlit==1.22.0
-nltk==3.8.1
-torch==2.0.0
-datasets==2.12.0
-numpy==1.22.4
-pandas==1.5.3
-session_info==1.0.0
-scikit-learn==1.2.2
-transformers==4.28.1
-IPython==7.34.0
-jupyter_client==6.1.12
-jupyter_core==5.3.0
-notebook==6.4.8
-uvicorn[standard]==0.17.*

utils.py

@@ -1,54 +0,0 @@
-import numpy as np
-import pandas as pd
-from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification
-from scipy.special import softmax
-import os
-
-# Requirements
-# model_path = "bright1/fine-tuned-distilbert-base-uncased"
-# tokenizer = AutoTokenizer.from_pretrained(model_path)
-# config = AutoConfig.from_pretrained(model_path)
-# model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-
-
-# def check_csv(csv_file, data):
-#     if os.path.isfile(csv_file):
-#         data.to_csv(csv_file, mode='a', header=False, index=False, encoding='utf-8')
-#     else:
-#         history = data.copy()
-#         history.to_csv(csv_file, index=False)
-
-#Preprocess text
-def preprocess(text):
-    new_text = []
-    for t in text.split(" "):
-        t = "@user" if t.startswith("@") and len(t) > 1 else t
-        t = "http" if t.startswith("http") else t
-        print(t)
-        new_text.append(t)
-    print(new_text)
-
-    return " ".join(new_text)
-
-#Process the input and return prediction
-def run_sentiment_analysis(text, tokenizer, model):
-    # save_text =  {'tweet': text}
-    encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
-    output = model(**encoded_input)
-    scores_ = output[0][0].detach().numpy()
-    scores_ = softmax(scores_)
-
-    # Format output dict of scores
-    labels = ["Negative", "Neutral", "Positive"]
-    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-    # save_text.update(scores)
-    # user_data = {key: [value] for key,value in save_text.items()}
-    # data = pd.DataFrame(user_data,)
-    # check_csv('history.csv', data)
-    # hist_df = pd.read_csv('history.csv')
-    return scores
-
-
-    
-