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.gitignore

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+venv

LICENSE

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+MIT License
+
+Copyright (c) 2023 NARESH KUMAR LAHAJAL
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Procfile

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+web: gunicorn app:app

app.yaml

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+runtime: python311

main.py

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+from flask import Flask,request,jsonify
+import numpy as np
+import pickle
+
+model = pickle.load(open('model.pkl','rb'))
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return "Stock prediction"
+
+@app.route('/predict',methods=['POST'])
+def predict():
+    high = request.form.get('high')
+    low = request.form.get('low')
+    open = request.form.get('open')
+    volume = request.form.get('volume')
+    high=float(high)
+    low=float(low)
+    open=float(open)
+    volume=float(volume)
+    input_query = np.array([[high,low,open,volume]])
+    result = model.predict(input_query)[0]
+    return jsonify({'res':str(result)})
+
+if __name__ == '__main__':
+    app.run(debug=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d5a99e0309c7d50fb45171fd927c7dc84140c132dc1bd0bbc2214113c9649d75
+size 523

requirements.txt

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+flask
+numpy
+sklearn
+gunicorn

streamlit_app.py

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+import streamlit as st
+import requests
+
+st.title("Predictive Model App")
+
+# Create input fields
+high = st.number_input("High", format="%f")
+low = st.number_input("Low", format="%f")
+open_val = st.number_input("Open", format="%f")  # renamed to avoid conflict with the built-in open function
+volume = st.number_input("Volume", format="%f")
+
+url = "https://nareshstp.pythonanywhere.com/predict"
+
+# Create a button to trigger the prediction
+if st.button("Predict"):
+    # Prepare the parameters for the POST request
+    params = {
+        "high": str(high),
+        "low": str(low),
+        "open": str(open_val),
+        "volume": str(volume)
+    }
+    
+    # Make the POST request
+    try:
+        response = requests.post(url, data=params)
+        
+        # Parse the response and display the result
+        if response.status_code == 200:
+            result_data = response.json()
+            
+            # Display the result in a bigger font and inside a text box
+            st.markdown(f"## Result")
+            st.markdown(f"<div style='background-color: ##050505; padding: 20px; border-radius: 5px;'><span style='font-size: 24px;'>{result_data.get('res')}</span></div>", unsafe_allow_html=True)
+        else:
+            st.error(f"API Error: {response.status_code}. {response.text}")
+            
+    except Exception as e:
+        st.error(f"Error: {e}")