adding files

inference.py

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+# Import a module
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoModel, AutoTokenizer
+from transformers import AutoTokenizer , pipeline , AutoConfig
+import numpy as np
+
+import gradio as gr
+from scipy.special import softmax
+import torch
+
+# Loading requirements from Hugging Face
+# HuggingFace path where the fine tuned model is placed
+model_path = "Henok21/test_trainer"
+
+# Loading the model
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+config = AutoConfig.from_pretrained(model_path)
+
+# Loading tokenizer
+tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+# Creating pipeline
+calssifier = pipeline("sentiment-analysis" , model , tokenizer = tokenizer)
+
+# Preparing gradio app
+# Preprocessor Function
+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)
+
+# Configuring the outputs
+config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
+config.label2id = {"NEGATIVE": 0, "NEUTRAL": 1, "POSITIVE": 2}
+
+# creating a function used for gradio app
+# Creating dictionary
+dictionary = {}
+
+def sentiment_analysis(text):
+
+    # Encode the text using the tokenizer
+    encoded_input = tokenizer(text, return_tensors='pt')
+    # Get the output logits from the model
+    output = model(**encoded_input)
+
+    # Your code to get the scores for each class
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    
+    # Convert the numpy array into a list
+    scores = scores.tolist()
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1]
+
+    for i in range(len(scores)):
+        l = config.id2label[ranking[i]]
+        s = scores[ranking[i]]
+       
+        # Convert the numpy float32 object into a float
+        dictionary[l] = float(s)
+
+    # Return the dictionary as the response content
+    return dictionary
+
+# Create your interface
+demo = gr.Interface(
+    fn=sentiment_analysis, 
+    inputs="text", 
+    outputs="label"
+    )
+
+# Launch your interface
+demo.launch(debug = True)

requirements.txt

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+gradio
+transformers