Update app.py

app.py

@@ -1,66 +1,47 @@
-import joblib
+from flask import Flask, render_template, request, redirect, url_for, flash
 import pandas as pd
-import streamlit as st 
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-EDU_DICT = {'Preschool': 1,
-            '1st-4th': 2,
-            '5th-6th': 3,
-            '7th-8th': 4,
-            '9th': 5,
-            '10th': 6,
-            '11th': 7,
-            '12th': 8,
-            'HS-grad': 9, 
-            'Some-college': 10,
-            'Assoc-voc': 11,
-            'Assoc-acdm': 12,
-            'Bachelors': 13,
-            'Masters': 14,
-            'Prof-school': 15,
-            'Doctorate': 16
-            }
+app = Flask(__name__)
+app.secret_key = 'your_secret_key'
 
-model = joblib.load('model.joblib')
-unique_values = joblib.load('unique_values.joblib')
-    
-unique_class =  unique_values["workclass"]
-unique_education =  unique_values["education"]
-unique_marital_status =  unique_values["marital.status"]
-unique_relationship =  unique_values["relationship"]
-unique_occupation =  unique_values["occupation"]
-unique_sex =  unique_values["sex"]
-unique_race = unique_values["race"]
-unique_country =  unique_values["native.country"]
+# Load Hugging Face Transformers model and tokenizer
+model_name = "distilbert-base-uncased"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-def main():
-    st.title("Adult Income Analysis")
+@app.route('/')
+def index():
+    return render_template('index.html')
 
-    with st.form("questionaire"):
-        age = st.slider("Age", min_value=10, max_value=100)
-        workclass = st.selectbox("Workclass", unique_class)
-        education = st.selectbox("Education", unique_education)
-        Marital_Status = st.selectbox("Marital Status", unique_marital_status)
-        occupation = st.selectbox("Occupation", unique_occupation)
-        relationship = st.selectbox("Relationship", unique_relationship)
-        race = st.selectbox("Race", unique_race)
-        sex = st.selectbox("Sex", unique_sex)
-        hours_per_week = st.slider("Hours per week", min_value=1, max_value=100)
-        native_country = st.selectbox("Country", unique_country)
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        flash('No file part')
+        return redirect(request.url)
 
-        clicked = st.form_submit_button("Predict income")
-        if clicked:
-            result=model.predict(pd.DataFrame({"age": [age],
-                                               "workclass": [workclass],
-                                               "education": [EDU_DICT[education]],
-                                               "marital.status": [Marital_Status],
-                                               "occupation": [occupation],
-                                               "relationship": [relationship],
-                                               "race": [race],
-                                               "sex": [sex],
-                                               "hours.per.week": [hours_per_week],
-                                               "native.country": [native_country]}))
-            result = '>50K' if result[0] == 1 else '<=50K'
-            st.success('The predicted income is {}'.format(result))
+    file = request.files['file']
 
-if __name__=='__main__':
-    main()
+    if file.filename == '':
+        flash('No selected file')
+        return redirect(request.url)
+
+    if file:
+        try:
+            df = pd.read_csv(file)
+            # Your data cleaning logic here
+            # For example, let's perform sentiment analysis on a column 'text'
+            df['sentiment_score'] = df['text'].apply(lambda x: analyze_sentiment(x))
+            return df.to_html(classes='table table-bordered', escape=False)
+        except Exception as e:
+            flash(f'Error processing the file: {str(e)}')
+            return redirect(request.url)
+
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    output = model(**inputs)
+    score = output.logits.softmax(dim=1).tolist()[0]
+    return score
+
+if __name__ == '__main__':
+    app.run(debug=True)