Delete app.py

app.py

@@ -1,54 +0,0 @@
-import os
-import gradio as gr
-from PIL import Image
-from timeit import default_timer as timer
-from tensorflow import keras
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-import numpy as np
-
-username = "runaksh"
-repo_name = "finetuned-sentiment-model"
-repo_path = username+ '/' + repo_name
-model_1 = pipeline(model= repo_path)
-
-model_2 = AutoModelForSequenceClassification.from_pretrained("runaksh/Symptom-2-disease_distilBERT")
-tokenizer_2 = AutoTokenizer.from_pretrained("runaksh/Symptom-2-disease_distilBERT")
-
-# Function for response generation
-def predict_sentiment(text):
-    result = model_1(text)
-    if result[0]['label'].endswith('0'):
-        return 'Negative'
-    else:
-        return 'Positive'
-
-def predict(sample, validate=True):
-  pred = classifier(sample)[0]['label']
-  return pred
-
-def make_block(dem):
-    with dem:
-        gr.Markdown("Practicing for Capstone")
-        with gr.Tabs():
-            with gr.TabItem("Sentiment Classification"):
-                with gr.Row():
-                    in_prompt_1 = gr.components.Textbox(lines=10, placeholder=None, label='Enter review text')
-                    out_response_1 = gr.components.Textbox(type="text", label='Sentiment')
-                b1 = gr.Button("Enter")
-
-            with gr.TabItem("Symptoms and Disease Classification"):
-                with gr.Row():
-                    in_prompt_2 = gr.components.Textbox(lines=2, label='Enter the Symptoms')
-                    out_response_2 = gr.components.Textbox(label='Disease')
-                b2 = gr.Button("Enter")
-            b1.click(predict_sentiment, inputs=in_prompt_1, outputs=out_response_1)
-            b2.click(predict, inputs=in_prompt_2, outputs=out_response_2)
-           
-if __name__ == '__main__':
-    model_1 = pipeline(model= repo_path)
-    classifier = pipeline("text-classification", model=model_2, tokenizer=tokenizer_2)
-
-    demo = gr.Blocks()
-    make_block(demo)
-    demo.launch()