Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import requests
+import json
+
+# Define API keys (ensure these are securely handled and not exposed in public code repositories)
+API_KEY = "565c106133mshb641eb3210436aep10e7fbjsn9b12f087ae76"
+API_HOST = "ai-content-identifier2.p.rapidapi.com"
+
+# Code for Text AI Detect
+def text_check(user_input):
+    url = f"https://{API_HOST}/text"
+    payload = {
+        "text": user_input,
+        "threshold": 10  # Adjust this if needed to test different sensitivity levels
+    }
+    headers = {
+        "x-rapidapi-key": API_KEY,
+        "x-rapidapi-host": API_HOST,
+        "Content-Type": "application/json"
+    }
+    try:
+        response = requests.post(url, json=payload, headers=headers)
+        response.raise_for_status()  # Raises an HTTPError for bad responses
+        data = response.json()
+        # Format the output to be user-friendly
+        if 'success' in data and data['success']:
+            ai_status = 'Yes' if data['data']['ai'] else 'No'
+            return (f"AI Content Detected: {ai_status}\n"
+                    f"Confidence: {data['data']['percentage']}%\n"
+                    f"Total Words: {data['data']['stats']['totalWords']}\n"
+                    f"AI Words: {data['data']['stats']['aiWords']}\n"
+                    f"Human Words: {data['data']['stats']['humanWords']}\n"
+                    f"Note: {data['note']}")
+        else:
+            return "Failed to analyze text. Please try again."
+    except requests.exceptions.RequestException as e:
+        print(f"Error during API call: {e}")
+        return {"error": str(e)}
+
+demo = gr.Interface(fn=text_check, inputs="textbox", outputs="textbox")
+
+if __name__ == "_main_":
+    demo.launch()