Rename requirem.txtapp.py to app.pu

app.pu

@@ -0,0 +1,62 @@
+import streamlit as st
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Set the title of the app
+st.title("Iris Flower Species Prediction App")
+
+# Load the Iris dataset
+iris = load_iris()
+X = pd.DataFrame(iris.data, columns=iris.feature_names)
+y = pd.Series(iris.target, name='species')
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Initialize the RandomForestClassifier
+clf = RandomForestClassifier(n_estimators=100, random_state=42)
+clf.fit(X_train, y_train)
+
+# Sidebar for user input features
+st.sidebar.header("Input Features")
+
+def user_input_features():
+    sepal_length = st.sidebar.slider('Sepal length (cm)', float(X['sepal length (cm)'].min()), float(X['sepal length (cm)'].max()), float(X['sepal length (cm)'].mean()))
+    sepal_width = st.sidebar.slider('Sepal width (cm)', float(X['sepal width (cm)'].min()), float(X['sepal width (cm)'].max()), float(X['sepal width (cm)'].mean()))
+    petal_length = st.sidebar.slider('Petal length (cm)', float(X['petal length (cm)'].min()), float(X['petal length (cm)'].max()), float(X['petal length (cm)'].mean()))
+    petal_width = st.sidebar.slider('Petal width (cm)', float(X['petal width (cm)'].min()), float(X['petal width (cm)'].max()), float(X['petal width (cm)'].mean()))
+    data = {'sepal length (cm)': sepal_length,
+            'sepal width (cm)': sepal_width,
+            'petal length (cm)': petal_length,
+            'petal width (cm)': petal_width}
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+input_df = user_input_features()
+
+# Main panel
+st.subheader("User Input Features")
+
+# Display the user input features
+st.write(input_df)
+
+# Make predictions
+prediction = clf.predict(input_df)
+prediction_proba = clf.predict_proba(input_df)
+
+# Display the prediction
+st.subheader("Prediction")
+st.write(iris.target_names[prediction][0])
+
+# Display the prediction probability
+st.subheader("Prediction Probability")
+st.write(pd.DataFrame(prediction_proba, columns=iris.target_names))
+
+# Evaluate model accuracy
+y_pred = clf.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+st.subheader("Model Accuracy")
+st.write(f"{accuracy * 100:.2f}%")

requirem.txtapp.py

@@ -1,7 +0,0 @@
-streamlit
-pandas
-numpy
-scikit-learn
-Pillow
-PyPDF2
-transformers