add conf gradio

.gitignore

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+*venv/

app.py

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+import gradio as gr
+from scipy.special import softmax
+from transformers import AutoTokenizer, AutoConfig
+from transformers import AutoModelForSequenceClassification
+import numpy as np
+
+#Setup
+Model = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
+tokenizer = AutoTokenizer.from_pretrained(Model)
+model_path = f"bambadij/sentiment_analysis_model_trainer"
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+#Function
+
+# Preprocess text (username and link placeholders)
+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
+        new_text.append(t)
+    return " ".join(new_text)
+
+# Input preprocessing
+text = "Covid cases are increasing fast!"
+text = preprocess(text)
+
+# PyTorch-based models
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+scores = output[0][0].detach().numpy()
+scores = softmax(scores)
+
+def sentiment_analysis(text):
+    text =preprocess(text)
+    #Pytorch-based models
+    encoded_input = tokenizer(text,return_tensors='pt')
+    output = model(**encoded_input)
+    scores_ = output[0][0].detach().numpy()
+    scores_ =softmax(scores_)
+
+    #Foramt ouptput dict of scores
+    labels =['Negative','Neutral','Positive']
+    scores = {l:float(s) for (l,s) in zip(labels,scores_)}
+    return scores
+
+demo = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Copy and paste /Write a tweet her..."),
+    outputs="text",
+    examples=[["what's up with the vaccine"],
+              ["Covid cases are increasing fast!"],
+              ["Covid has been invented by Issa"],
+              ["I have a covid"],
+              ["All the people are sick maybe it's covid"],
+              ],
+    title="Tutorial:Sentiment Analysis App",
+    description = "This Aplication assesses if a twitter post relating vaccination is positive"
+  )
+demo.launch(share=False)

requirements.txt

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+transformers==4.35.0
+gradio==4.1.1
+numpy==1.23.5
+scipy==1.11.3
+torch @ https://download.pytorch.org/whl/cu118/torch-2.1.0%2Bcu118-cp310-cp310-linux_x86_64.whl#sha256=a81b554184492005543ddc32e96469f9369d778dedd195d73bda9bed407d6589
+black