Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,161 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import smtplib
+import re
+import os
+from dotenv import load_dotenv  # <--- Import this
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
+
+# --- Configuration ---
+load_dotenv()  # <--- This loads the variables from .env
+
+# Now we read the secrets using os.getenv
+SENDER_EMAIL = os.getenv("SENDER_EMAIL")
+SENDER_PASSWORD = os.getenv("SENDER_PASSWORD")
+
+if not SENDER_EMAIL or not SENDER_PASSWORD:
+    st.error("Error: Email credentials not found. Please check your .env file.")
+    st.stop()
+
+# --- Helper Functions ---
+
+def validate_email(email):
+    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
+    return re.match(pattern, email)
+
+def send_email(receiver_email, result_df, filename="result.csv"):
+    try:
+        msg = MIMEMultipart()
+        msg['From'] = SENDER_EMAIL
+        msg['To'] = receiver_email
+        msg['Subject'] = "Your TOPSIS Result is Ready"
+
+        body = "Hello,\n\nPlease find the attached result file for your TOPSIS calculation.\n\nBest,\nTOPSIS Web Service"
+        msg.attach(MIMEText(body, 'plain'))
+
+        # Convert DataFrame to CSV string
+        csv_data = result_df.to_csv(index=False)
+        
+        # Attachment
+        part = MIMEBase('application', 'octet-stream')
+        part.set_payload(csv_data)
+        encoders.encode_base64(part)
+        part.add_header('Content-Disposition', f"attachment; filename= {filename}")
+        msg.attach(part)
+
+        # SMTP Server Setup (For Gmail)
+        server = smtplib.SMTP('smtp.gmail.com', 587)
+        server.starttls()
+        server.login(SENDER_EMAIL, SENDER_PASSWORD)
+        text = msg.as_string()
+        server.sendmail(SENDER_EMAIL, receiver_email, text)
+        server.quit()
+        return True, "Email sent successfully!"
+    except Exception as e:
+        return False, str(e)
+
+def calculate_topsis(df, weights, impacts):
+    # This logic is adapted from your CLI package
+    try:
+        # Preprocessing: Drop first column (Object Names) and convert to float
+        data = df.iloc[:, 1:].values.astype(float)
+        
+        # Normalization
+        rss = np.sqrt(np.sum(data**2, axis=0))
+        normalized_data = data / rss
+
+        # Weighting
+        weighted_data = normalized_data * weights
+
+        # Ideal Best and Worst
+        ideal_best = []
+        ideal_worst = []
+
+        for i in range(len(impacts)):
+            if impacts[i] == '+':
+                ideal_best.append(np.max(weighted_data[:, i]))
+                ideal_worst.append(np.min(weighted_data[:, i]))
+            else:
+                ideal_best.append(np.min(weighted_data[:, i]))
+                ideal_worst.append(np.max(weighted_data[:, i]))
+
+        # Euclidean Distance
+        s_best = np.sqrt(np.sum((weighted_data - ideal_best)**2, axis=1))
+        s_worst = np.sqrt(np.sum((weighted_data - ideal_worst)**2, axis=1))
+
+        # Topsis Score
+        topsis_score = s_worst / (s_best + s_worst)
+        
+        # Add to DF
+        df['Topsis Score'] = topsis_score
+        df['Rank'] = df['Topsis Score'].rank(ascending=False).astype(int)
+        
+        return df
+
+    except Exception as e:
+        st.error(f"Error in calculation: {e}")
+        return None
+
+# --- Main App UI ---
+
+st.title("Topsis Web Service")
+st.write("Upload your data, define weights/impacts, and get results via email.")
+
+# 1. Inputs
+email_id = st.text_input("Email ID (to receive results)", placeholder="name@example.com")
+uploaded_file = st.file_uploader("Upload Input File (CSV)", type=["csv"])
+weights_input = st.text_input("Weights (comma-separated)", placeholder="1,1,1,2")
+impacts_input = st.text_input("Impacts (comma-separated, + or -)", placeholder="+,+,-,+")
+
+# 2. Submit Button
+if st.button("Submit"):
+    # --- Validations ---
+    if not uploaded_file:
+        st.error("Please upload a CSV file.")
+    elif not email_id or not validate_email(email_id):
+        st.error("Please enter a valid email address.")
+    elif not weights_input:
+        st.error("Please enter weights.")
+    elif not impacts_input:
+        st.error("Please enter impacts.")
+    else:
+        # Process Inputs
+        try:
+            df = pd.read_csv(uploaded_file)
+            
+            # Parse Weights
+            weights = [float(w) for w in weights_input.split(',')]
+            
+            # Parse Impacts
+            impacts = impacts_input.split(',')
+            
+            # Validation: Column Count
+            num_cols = df.shape[1] - 1  # Exclude first column
+            if len(weights) != num_cols or len(impacts) != num_cols:
+                st.error(f"Count Mismatch! Input file has {num_cols} numerical columns, but you provided {len(weights)} weights and {len(impacts)} impacts.")
+            elif not all(i in ['+', '-'] for i in impacts):
+                st.error("Impacts must be either '+' or '-'.")
+            else:
+                # --- Run Logic ---
+                st.info("Calculating TOPSIS Score...")
+                result_df = calculate_topsis(df, weights, impacts)
+                
+                if result_df is not None:
+                    # --- Send Email ---
+                    st.info(f"Sending result to {email_id}...")
+                    success, message = send_email(email_id, result_df)
+                    
+                    if success:
+                        st.success("Success! Result file has been sent to your email.")
+                        st.dataframe(result_df) # Show preview
+                    else:
+                        st.error(f"Failed to send email: {message}")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        except ValueError:
+            st.error("Weights must be numeric values separated by commas.")
+        except Exception as e:
+            st.error(f"An unexpected error occurred: {e}")