Create app.py

app.py

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+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()