Update app.py

app.py

@@ -1,4 +1,65 @@
-import streamlit as st
+import numpy as np
+from fastapi import FastAPI, Form
+import pandas as pd
+from starlette.responses import HTMLResponse 
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import tensorflow as tf
+import re
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+def preProcess_data(text): #cleaning the data
+    
+    text = text.lower()
+    new_text = re.sub('[^a-zA-z0-9\s]','',text)
+    new_text = re.sub('rt', '', new_text)
+    return new_text
+
+app = FastAPI()
+
+data = pd.read_csv('archive/Sentiment.csv')
+tokenizer = Tokenizer(num_words=2000, split=' ')
+tokenizer.fit_on_texts(data['text'].values)
+
+
+
+def my_pipeline(text): #pipeline
+  text_new = preProcess_data(text)
+  X = tokenizer.texts_to_sequences(pd.Series(text_new).values)
+  X = pad_sequences(X, maxlen=28)
+  return X
+
+
+@app.get('/') #basic get view
+def basic_view():
+    return {"WELCOME": "GO TO /docs route, or /post or send post request to /predict "}
+
+
+
+@app.get('/predict', response_class=HTMLResponse) #data input by forms
+def take_inp():
+    return '''<form method="post"> 
+    <input type="text" maxlength="28" name="text" value="Text Emotion to be tested"/>  
+    <input type="submit"/> 
+    </form>'''
+
+
+
+@app.post('/predict') #prediction on data
+def predict(text:str = Form(...)): #input is from forms
+    clean_text = my_pipeline(text) #cleaning and preprocessing of the texts
+    loaded_model = tf.keras.models.load_model('sentiment.h5') #loading the saved model
+    predictions = loaded_model.predict(clean_text) #making predictions
+    sentiment = int(np.argmax(predictions)) #index of maximum prediction
+    probability = max(predictions.tolist()[0]) #probability of maximum prediction
+    if sentiment==0: #assigning appropriate name to prediction
+        t_sentiment = 'negative'
+    elif sentiment==1:
+        t_sentiment = 'neutral'
+    elif sentiment==2:
+        t_sentiment='postive'
+    
+    return { #returning a dictionary as endpoint
+        "ACTUALL SENTENCE": text,
+        "PREDICTED SENTIMENT": t_sentiment,
+        "Probability": probability
+    }