Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import openai
+import re  # import the regular expression module to parse the score
+
+def evaluate_text(api_key, model, text_to_evaluate):
+    # Setting the API key for OpenAI
+    openai.api_key = api_key
+    
+    # Few-shot learning prompt template
+    prompt = """Given below are some texts. Please evaluate how likely each text is AI-generated on a scale from 1-100, where 100 means highly likely and 1 means highly unlikely to be AI-generated.
+
+1. "I am going to the park today." - Human written
+    Evaluation: 5
+    
+2. "The quixotic endeavor synergizes cryptographic algorithms." - AI generated
+    Evaluation: 90
+
+3. "{}"
+    Evaluation: """.format(text_to_evaluate)
+
+    try:
+        # Generate model's response
+        response = openai.Completion.create(
+            engine=model,
+            prompt=prompt,
+            max_tokens=10
+        )
+        
+        raw_evaluation_score = response.choices[0].text.strip()
+        
+        # Parsing the evaluation score using regular expressions
+        parsed_score = re.search(r"\d+", raw_evaluation_score)
+        
+        if parsed_score:
+            return parsed_score.group()
+        else:
+            return "Score could not be parsed."
+
+    except Exception as e:
+        return str(e)
+
+# Gradio UI
+iface = gr.Interface(
+    fn=evaluate_text,  
+    inputs=[
+        gr.inputs.Textbox(lines=1, label="Enter your OpenAI API Key", type="password"),
+        gr.inputs.Dropdown(choices=["text-davinci-002", "text-ada-002"], label="Model"),
+        gr.inputs.Textbox(lines=5, label="Text to Evaluate")
+    ],
+    outputs=gr.outputs.Textbox(label="AI Score out of 100"),  # Customized label here
+    live=False
+)
+
+iface.launch()