Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Diamond Prices Prediction
-emoji: 🌍
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 4.37.2
+title: Diamond_Prices_Prediction
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.37.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,97 @@
+import pandas as pd
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestRegressor
+import gradio as gr
+
+# Load the diamonds dataset
+diamonds = sns.load_dataset("diamonds")
+
+# Prepare the features and target
+X = diamonds.drop("price", axis=1)
+y = diamonds["price"]
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# Define the preprocessing steps
+numeric_features = ["carat", "depth", "table", "x", "y", "z"]
+categorical_features = ["cut", "color", "clarity"]
+
+preprocessor = ColumnTransformer(
+    transformers=[
+        ("num", StandardScaler(), numeric_features),
+        ("cat", OneHotEncoder(drop="first"), categorical_features),
+    ]
+)
+
+
+# Create a pipeline with preprocessing and model
+model = Pipeline(
+    [
+        ("preprocessor", preprocessor),
+        ("regressor", RandomForestRegressor(n_estimators=100, random_state=42)),
+    ]
+)
+
+# Fit the model
+model.fit(X_train, y_train)
+
+
+# Create the Gradio interface
+def predict_price(carat, cut, color, clarity, depth, table, x, y, z):
+    input_data = pd.DataFrame(
+        {
+            "carat": [carat],
+            "cut": [cut],
+            "color": [color],
+            "clarity": [clarity],
+            "depth": [depth],
+            "table": [table],
+            "x": [x],
+            "y": [y],
+            "z": [z],
+        }
+    )
+    prediction = model.predict(input_data)[0]
+    return f"Predicted Price: ${prediction:.2f}"
+
+
+iface = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(
+            minimum=diamonds["carat"].min(),
+            maximum=diamonds["carat"].max(),
+            label="Carat",
+        ),
+        gr.Dropdown(["Fair", "Good", "Very Good", "Premium", "Ideal"], label="Cut"),
+        gr.Dropdown(["D", "E", "F", "G", "H", "I", "J"], label="Color"),
+        gr.Dropdown(
+            ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"], label="Clarity"
+        ),
+        gr.Slider(
+            minimum=diamonds["depth"].min(),
+            maximum=diamonds["depth"].max(),
+            label="Depth",
+        ),
+        gr.Slider(
+            minimum=diamonds["table"].min(),
+            maximum=diamonds["table"].max(),
+            label="Table",
+        ),
+        gr.Slider(minimum=diamonds["x"].min(), maximum=diamonds["x"].max(), label="X"),
+        gr.Slider(minimum=diamonds["y"].min(), maximum=diamonds["y"].max(), label="Y"),
+        gr.Slider(minimum=diamonds["z"].min(), maximum=diamonds["z"].max(), label="Z"),
+    ],
+    outputs="text",
+    title="Diamond Price Predictor",
+    description="Enter the characteristics of a diamond to predict its price.",
+)
+
+iface.launch(share=True)

requirements.txt

@@ -0,0 +1,4 @@
+scikit-learn
+pandas
+numpy
+seaborn