Update app.py

app.py

@@ -1,15 +1,14 @@
 from flask import Flask, request, render_template
-import pandas as pd
+import seaborn as sns
 from sklearn.linear_model import LogisticRegression
 
 # Load dataset
-
-df= sns.loadset("iris")
+df = sns.load_dataset("iris")
 X = df.iloc[:, :4].values
 y = df.iloc[:, 4].values
 
 # Train model
-model = LogisticRegression(max_iter=200)
+model = LogisticRegression(max_iter=200, multi_class="auto")
 model.fit(X, y)
 
 # Flask app
@@ -24,13 +23,52 @@ def home():
             petal_length = float(request.form["petal_length"])
             petal_width = float(request.form["petal_width"])
 
-            prediction = model.predict([[sepal_length, sepal_width, petal_length, petal_width]])[0]
-            return render_template("index.html", prediction_text=f"Predicted Flower: {prediction}")
+            prediction = model.predict(
+                [[sepal_length, sepal_width, petal_length, petal_width]]
+            )[0]
 
+            return render_template("index.html", prediction_text=f"🌸 Predicted Flower: {prediction}")
         except Exception as e:
-            return render_template("index.html", prediction_text=f"Error: {e}")
+            return render_template("index.html", prediction_text=f"⚠️ Error: {e}")
 
     return render_template("index.html", prediction_text="")
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860, debug=True)
+    app.run(host="0.0.0.0", port=7860, debug=True)
+
+# from flask import Flask, request, render_template
+# import pandas as pd
+# from sklearn.linear_model import LogisticRegression
+
+# # Load dataset
+
+# df= sns.loadset("iris")
+# X = df.iloc[:, :4].values
+# y = df.iloc[:, 4].values
+
+# # Train model
+# model = LogisticRegression(max_iter=200)
+# model.fit(X, y)
+
+# # Flask app
+# app = Flask(__name__)
+
+# @app.route("/", methods=["GET", "POST"])
+# def home():
+#     if request.method == "POST":
+#         try:
+#             sepal_length = float(request.form["sepal_length"])
+#             sepal_width = float(request.form["sepal_width"])
+#             petal_length = float(request.form["petal_length"])
+#             petal_width = float(request.form["petal_width"])
+
+#             prediction = model.predict([[sepal_length, sepal_width, petal_length, petal_width]])[0]
+#             return render_template("index.html", prediction_text=f"Predicted Flower: {prediction}")
+
+#         except Exception as e:
+#             return render_template("index.html", prediction_text=f"Error: {e}")
+
+#     return render_template("index.html", prediction_text="")
+
+# if __name__ == "__main__":
+#     app.run(host="0.0.0.0", port=7860, debug=True)