add app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from get_gender import GenderPredictor
+from GitScraping import CommitInfo
+import pandas as pd
+import plotly.graph_objects as go
+class GenderPredictorApp:
+    def __init__(self, modelpath):
+        self.gender_predictor = GenderPredictor(modelpath)
+        self.setup_ui()
+
+    def setup_ui(self):
+        with gr.Blocks() as demo:
+            gr.Label(value="Exploring Global Gender Disparities in Public Code Contributions")
+            name = gr.Textbox(label="Name")
+            output = gr.Textbox(label="Predicted Gender")
+            name_buttom = gr.Button("Predict")
+            name_buttom.click(self.predict_name, inputs=name, outputs=output, api_name="greet")
+            
+            name = gr.Textbox(label="Git-url")
+            pie_chart_output = gr.Plot()
+            data_output =gr.Dataframe()
+
+            name_buttom = gr.Button("Predict")
+            name_buttom.click(self.predict_github_url, inputs=name, outputs=[pie_chart_output, data_output])
+
+        self.demo = demo
+
+    def predict_name(self, name):
+        prediction, proba = self.gender_predictor.predict_gender(name)
+
+        if prediction == 0:
+            prediction = "Male with probability: " + str(proba) + "%"
+        elif prediction == 1:
+            prediction = "Female with probability: " + str(proba) + "%"
+        else:
+            prediction = "Unknown or not a name"
+
+        prediction = name + " is " + prediction
+        return prediction
+    def predict_github_url(self, url):
+        
+        commit_info = CommitInfo(url)
+
+        df,first_commit_dates = commit_info.get_first_commit_dates()
+        first_commit_dates[['Predicted_Gender', 'Confidence']] = first_commit_dates['Author'].apply(lambda name: pd.Series(self.gender_predictor.predict_gender(name)))
+        first_commit_dates['Predicted_Gender'] = first_commit_dates['Predicted_Gender'].replace({0: "Male", 1: "Female", 2: "Unknown"})
+        counts = first_commit_dates['Predicted_Gender'].value_counts()
+        colors = ["blue", "pink", "gray"]
+        fig = go.Figure(data=[go.Pie(labels=first_commit_dates['Predicted_Gender'].unique(), values=counts, marker=dict(colors=colors))])
+
+        # Convert the chart to HTML and return it
+        return fig,first_commit_dates
+    def launch(self):
+        self.demo.launch()
+
+if __name__ == "__main__":
+    modelpath = "saved_model/bestmodel.tf"
+    app = GenderPredictorApp(modelpath)
+    app.launch()