Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import openai
+import os
+# Initialize the OpenAI API client with your actual API key
+
+class Classifier:
+    def __init__(self):
+      openai.api_key = os.getenv("OPENAI_API_KEY")
+    def classify_text(self,text):
+      # Specify the desired model and additional options
+      response = openai.Completion.create(
+          engine="text-davinci-003",
+          prompt=f"Classify the following text as 'True' if related to Mental healthcare issue or 'False' if not related:\n{text}\n",
+          temperature=0,
+          max_tokens=1,  # We only need a single token as the classification result
+          n=1,
+          stop=None,
+      )
+
+      # Extract and return the generated classification result
+      generated_text = response.choices[0].text.strip()
+      return generated_text
+    def clear_func(self):
+      return " "," "
+    def gradio_interface(self):
+      with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
+        gr.HTML("""<center><h1>Mental healthcare</h1></center>""")
+        with gr.Column(elem_id="col-container"):
+          with gr.Row(elem_id="row-flex"):
+            with gr.Column(scale=0.90, min_width=160):
+              question =gr.Textbox(
+                  show_label=True,
+                  label="Question",
+              ).style(container=True)
+            with gr.Column(scale=0.10, min_width=160):
+              result =gr.Textbox(
+                  show_label=True,
+                  label="Result",
+              ).style(container=True)
+          with gr.Row(elem_id="row-flex"):
+            with gr.Column(scale=0.50, min_width=0):
+                submit=gr.Button(value="Submit")
+            with gr.Column(scale=0.50):
+                emptyBtn = gr.Button("🧹 Clear",)
+          
+        submit.click(self.classify_text,question,result)
+        emptyBtn.click(self.clear_func,[],[question,result])
+
+      demo.queue().launch(debug=True)
+
+if __name__ == "__main__":
+    classifier = Classifier()
+    classifier.gradio_interface()