Update app.py

app.py

@@ -1,46 +1,36 @@
 # -*- coding: utf-8 -*-
-"""Emotion Recognition_Fine Tuning
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1pZgt5n6943GB5oq_h43LjAYoA4yi-EST
-"""
-
-
-"""Our Application"""
-
+"""Emotion Recognition_Fine Tuning"""
 
 import numpy as np
-
-import tensorflow as tf # Apply softmax using tf.nn.softmax
-
-# Load the fine-tuned model from the saved directory
-# Load model directly
+import tensorflow as tf
 from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+import gradio as gr
 
+# Load the fine-tuned model and tokenizer
 loaded_model = TFAutoModelForSequenceClassification.from_pretrained("dhruvsaxena11/emoton_model_dhruv")
-# loaded_model = TFBertForSequenceClassification.from_pretrained("https://huggingface.co/spaces/dhruvsaxena11/Emotion_Recognition_in_Text/blob/main/tf_model.h5")
-loaded_tokenizer=AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
-
-def predict_emotion(text):
-
-  text_token=loaded_tokenizer(text,padding=True,return_tensors="np")
-  outputs=loaded_model(text_token)
-  probabilities = tf.nn.softmax(outputs.logits)
-  final=probabilities.numpy()
-  labels=["sadness","joy","love","anger","fear","surprise"]
-  final=final.tolist()
-  result_dict = {k: v for k, v in zip(labels,final[0])}
-  return result_dict
+loaded_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
 
-predict_emotion("dhruv")
-
-my_labels=["sadness","joy","love","anger","fear","surprise"]
+# Define labels
+labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]
 
+# Define prediction function
+def predict_emotion(text):
+    text_token = loaded_tokenizer(text, padding=True, return_tensors="tf")
+    outputs = loaded_model(text_token)
+    probabilities = tf.nn.softmax(outputs.logits, axis=-1)
+    result_dict = {k: v for k, v in zip(labels, probabilities.numpy()[0])}
+    return result_dict
 
-import gradio as gr
+# Set up Gradio interface
 inputs = gr.Textbox(lines=1, label="Input Text")
 outputs = gr.Label(num_top_classes=6)
-interface = gr.Interface(fn=predict_emotion, inputs=inputs, outputs=outputs,title="Emotion Recognition in Text - NLP")
-interface.launch()
+interface = gr.Interface(
+    fn=predict_emotion,
+    inputs=inputs,
+    outputs=outputs,
+    title="Emotion Recognition in Text - NLP",
+    description="Enter a text input to predict the emotion"
+)
+
+# Launch interface
+interface.launch(share=True)