Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,98 +1,41 @@
-# Libraries 
 import streamlit as st
+import os
 import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
 
-# Title
-st.title("Diabetes Prediction System")
-st.write("A Machine Learning model trained on the PIMA Indians Diabetes Dataset.")
+st.title("🕵️ File Finder Tool")
 
-# Load data and Train Data
-@st.cache_resource
-def train_model():
-    try:
-        df = pd.read_csv("diabetes.csv")
-    except FileNotFoundError:
-        # We create a tiny dummy dataset.
-        # This prevents the app from crashing.
-        st.warning("'diabetes.csv' not found. Using dummy data for demonstration.")
-        data = {
-            'Pregnancies': [6, 1, 8, 1, 0, 5, 3, 10],
-            'Glucose': [148, 85, 183, 89, 137, 116, 78, 115],
-            'BloodPressure': [72, 66, 64, 66, 40, 74, 50, 0],
-            'SkinThickness': [35, 29, 0, 23, 35, 0, 32, 0],
-            'Insulin': [0, 0, 0, 94, 168, 0, 88, 0],
-            'BMI': [33.6, 26.6, 23.3, 28.1, 43.1, 25.6, 31.0, 35.3],
-            'DiabetesPedigreeFunction': [0.627, 0.351, 0.672, 0.167, 2.288, 0.201, 0.248, 0.134],
-            'Age': [50, 31, 32, 21, 33, 30, 26, 29],
-            'Outcome': [1, 0, 1, 0, 1, 0, 1, 0]
-        }
-        df = pd.DataFrame(data)
-
-    # Data Split
-    X = df.drop(columns=['Outcome'])
-    y = df['Outcome']
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # Train Model
-    model = RandomForestClassifier(random_state=42)
-    model.fit(X_train, y_train)
-    
-    # Calculate accuracy
-    acc = accuracy_score(y_test, model.predict(X_test))
-    
-    return model, acc
-
-# Loading the Model
-model, accuracy = train_model()
+st.write("Current Working Directory:", os.getcwd())
 
-# Display Model Accuracy
-st.write(f"**Model Accuracy:** {accuracy * 100:.2f}%")
-st.markdown("---")
+# 1. List ALL files in the entire container (Recursive Search)
+st.subheader("Scanning for 'diabetes.csv'...")
+found = False
+csv_path = ""
 
-# UI
-st.sidebar.header("Patient Data")
+# Walk through every folder in the app
+for root, dirs, files in os.walk("."):
+    for file in files:
+        # Check if the file name contains 'diabetes' (case insensitive)
+        if "diabetes" in file.lower():
+            full_path = os.path.join(root, file)
+            st.success(f"✅ FOUND IT! Path: `{full_path}`")
+            found = True
+            csv_path = full_path
 
-def user_input_features():
-    pregnancies = st.sidebar.slider('Pregnancies', 0, 17, 3)
-    glucose = st.sidebar.slider('Glucose', 0, 199, 117)
-    bp = st.sidebar.slider('Blood Pressure', 0, 122, 72)
-    skin = st.sidebar.slider('Skin Thickness', 0, 99, 23)
-    insulin = st.sidebar.slider('Insulin', 0, 846, 30)
-    bmi = st.sidebar.slider('BMI', 0.0, 67.1, 32.0)
-    dpf = st.sidebar.slider('Diabetes Pedigree Function', 0.078, 2.42, 0.3725)
-    age = st.sidebar.slider('Age', 21, 81, 29)
+if not found:
+    st.error("❌ logical_error: The file 'diabetes.csv' is NOT in the container.")
+    st.warning("Make sure you didn't name it 'Diabetes.csv' (Capital D) or 'diabetes.csv.txt'.")
     
-    data = {
-        'Pregnancies': pregnancies,
-        'Glucose': glucose,
-        'BloodPressure': bp,
-        'SkinThickness': skin,
-        'Insulin': insulin,
-        'BMI': bmi,
-        'DiabetesPedigreeFunction': dpf,
-        'Age': age
-    }
-    features = pd.DataFrame(data, index=[0])
-    return features
-
-input_df = user_input_features()
-
-# Display User Input
-st.subheader('User Input parameters')
-st.write(input_df)
-
-# Prediction
-if st.button('Predict Diabetes Risk'):
-    prediction = model.predict(input_df)
-    prediction_proba = model.predict_proba(input_df)
-
-    st.subheader('Prediction Result')
-    if prediction[0] == 1:
-        st.error('Positive (High Risk of Diabetes)')
-    else:
-        st.success('Negative (Low Risk of Diabetes)')
-
-    st.write(f"Probability: {prediction_proba[0][prediction[0]] * 100:.2f}%")
+    st.subheader("Listing ALL files I can see:")
+    # List all files so you can see what IS there
+    for root, dirs, files in os.walk("."):
+        for file in files:
+            st.code(os.path.join(root, file))
+else:
+    # 2. Try to load it using the path we found
+    st.subheader("Trying to load data...")
+    try:
+        df = pd.read_csv(csv_path)
+        st.write("Preview of data:", df.head())
+        st.success("🎉 It works! Now you know the correct path to use.")
+    except Exception as e:
+        st.error(f"Found the file but failed to read it: {e}")