add app

app.py

@@ -0,0 +1,68 @@
+# Importing 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
+
+# HuggingFace path where the fine tuned model is placed
+model_path = "Henok21/test_trainer"
+
+# Loading the model
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Loading config file
+config = AutoConfig.from_pretrained(model_path)
+
+# Loading tokenizer
+tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+# Using pipeline
+calssifier = pipeline("sentiment-analysis" , model , tokenizer = tokenizer)
+
+# 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)
+
+# Adjusting config
+config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
+
+
+# Function used for gradio app
+def sentiment_analysis(text):
+    # 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()
+
+    # Print labels and scores
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1]
+    for i in range(len(scores)):
+        l = config.id2label[ranking[i]]
+
+        s = scores[ranking[i]]
+
+        a = f"{i+1}) {l} {np.round(float(s), 4)}"
+
+        # Convert the numpy float32 object into a float
+        d[l] = float(s)
+
+    # Return the dictionary as the response content
+    return d
+
+# Create your interface
+demo = gr.Interface(fn=sentiment_analysis, inputs="text", outputs="label")
+
+# Launch your interface
+demo.launch(debug = True)