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Browse files- Dockerfile +11 -0
- app.py +4 -4
Dockerfile
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@@ -0,0 +1,11 @@
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FROM python:3.9
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WORKDIR /usr/src/app
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COPY requirements.txt ./
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RUN pip install -r requirements.txt
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COPY ./app.py .
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CMD [ "python", "/usr/src/app/app.py" ]
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app.py
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@@ -2,7 +2,6 @@ import gradio as gr
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import numpy as np
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import torch
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from transformers import AutoModelForSequenceClassification
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from transformers import TFAutoModelForSequenceClassification
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from transformers import AutoTokenizer, AutoConfig
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from scipy.special import softmax
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@@ -38,7 +37,7 @@ def sentiment_analysis(text):
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# Format output dictionary of scores
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labels = ['Negative', 'Neutral', 'Positive']
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scores = {l:float(s) for (l,s) in zip(labels, scores_)
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return scores
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@@ -47,7 +46,7 @@ def sentiment_analysis(text):
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app = gr.Interface(
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fn=sentiment_analysis,
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inputs=gr.Textbox("Input tweet here:"),
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outputs="
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title="Sentiment Analysis of Tweets on Covid-19 Vaccines",
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description="With this App, you can type Tweets related to the Covid Vaccine and the app will rate the sentiment of the tweet..!",
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examples=[["Be careful of covid vaccination"],
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@@ -55,4 +54,5 @@ app = gr.Interface(
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["I cant wait for the Covid Vaccine!"]]
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)
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import numpy as np
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import torch
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from transformers import AutoModelForSequenceClassification
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from transformers import AutoTokenizer, AutoConfig
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from scipy.special import softmax
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# Format output dictionary of scores
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labels = ['Negative', 'Neutral', 'Positive']
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scores = {l:float(s) for (l,s) in zip(labels, scores_)}
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return scores
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app = gr.Interface(
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fn=sentiment_analysis,
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inputs=gr.Textbox("Input tweet here:"),
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outputs="text",
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title="Sentiment Analysis of Tweets on Covid-19 Vaccines",
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description="With this App, you can type Tweets related to the Covid Vaccine and the app will rate the sentiment of the tweet..!",
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examples=[["Be careful of covid vaccination"],
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["I cant wait for the Covid Vaccine!"]]
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)
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if __name__ == "__main__":
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app.launch(server_name="0.0.0.0", server_port="7860")
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