Update gradioapp.py

gradioapp.py

@@ -1,8 +1,31 @@
+#Import the required Libraries
 import gradio as gr
-# Creating a gradio app using the inferene API
-App = gr.Interface.load("huggingface/AmpomahChief/sentiment_analysis_on_covid_tweets",
-  title="COVID 19 tweets sentiment analysis", description ="This is a sentiment analysis on COVID 19 tweets using pretrained model on hugging face",
- allow_flagging=False, examples=[["Input your text here"]]
-)
+import pickle
+import pandas as pd
+import numpy as np
+import transformers
 
-App.launch()
+# Load a BERT model from the Hugging Face model hub
+model = transformers.AutoModel.from_pretrained('AmpomahChief/sentiment_analysis_on_covid_tweets')
+
+
+# Define a function that takes in input and passes it through the model
+def predict(inputs):
+    input_ids = transformers.BertTokenizer.from_pretrained('AmpomahChief/sentiment_analysis_on_covid_tweets').encode(inputs, return_tensors='pt')
+    output = model(input_ids)[0]
+    return output
+
+# Create a Gradio interface for the model
+interface = gr.Interface(fn=predict, inputs=gr.Textbox(prompt="Input text:"), outputs=gr.Textbox(prompt="Model output:"))
+
+# Launch the interface
+interface.launch()
+
+# import gradio as gr
+# # Creating a gradio app using the inferene API
+# App = gr.Interface.load("huggingface/AmpomahChief/sentiment_analysis_on_covid_tweets",
+#   title="COVID 19 tweets sentiment analysis", description ="This is a sentiment analysis on COVID 19 tweets using pretrained model on hugging face",
+#  allow_flagging=False, examples=[["Input your text here"]]
+# )
+
+# App.launch()