Update app.py

app.py

@@ -1,39 +1,66 @@
 import gradio as gr
 import requests
 
-# Your API endpoint (replace with your own)
-API_URL = "http://8.213.40.255/transcribe"
+# API endpoints with model names
+NVIDIA_CONFORMER_API = "http://8.213.40.255/transcribe"  # nvidia-conformer-ctc-large-arabic
+OPENAI_WHISPER_API = "http://8.213.32.123/transcribe?language=ar&task=transcribe&word_timestamps=false"  # openai-whisper-large-v3
 
 def process_audio(audio_file):
+    result_conformer = "No response from NVIDIA Conformer API"
+    result_whisper = "No response from OpenAI Whisper API"
+    
     try:
-        # Send the audio file to your API with the correct field name
         with open(audio_file, "rb") as f:
-            files = {"audio_file": f}  # <-- match API spec
-            response = requests.post(API_URL, files=files)
+            files = {"audio_file": f}
+            response = requests.post(NVIDIA_CONFORMER_API, files=files)
 
         if response.status_code == 200:
             try:
                 data = response.json()
                 transcription = data.get("transcription", "No transcription found")
-                return f"📝 Transcription: {transcription}"
+                result_conformer = f"📝 NVIDIA Conformer Transcription: {transcription}"
             except ValueError:
-                return f"⚠️ Could not parse JSON: {response.text}"
+                result_conformer = f"⚠️ NVIDIA Conformer could not parse JSON: {response.text}"
         else:
-            return f"❌ Error: {response.status_code} - {response.text}"
+            result_conformer = f"❌ NVIDIA Conformer Error: {response.status_code} - {response.text}"
 
     except Exception as e:
-        return f"⚠️ Exception: {str(e)}"
+        result_conformer = f"⚠️ NVIDIA Conformer Exception: {str(e)}"
+
+    try:
+        with open(audio_file, "rb") as f:
+            files = {"file": (audio_file, f, "audio/wav")}
+            headers = {"accept": "application/json"}
+            response = requests.post(OPENAI_WHISPER_API, files=files, headers=headers)
+
+        if response.status_code == 200:
+            try:
+                data = response.json()
+                text = data.get("text", "No text found")
+                result_whisper = f"📝 OpenAI Whisper Transcription: {text}"
+            except ValueError:
+                result_whisper = f"⚠️ OpenAI Whisper could not parse JSON: {response.text}"
+        else:
+            result_whisper = f"❌ OpenAI Whisper Error: {response.status_code} - {response.text}"
+
+    except Exception as e:
+        result_whisper = f"⚠️ OpenAI Whisper Exception: {str(e)}"
+
+    return result_conformer, result_whisper
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🎤 Record Audio and Send to API")
+    gr.Markdown("## 🎤 Record Audio and Send to NVIDIA Conformer & OpenAI Whisper APIs")
 
     with gr.Row():
         audio_input = gr.Audio(sources=["microphone"], type="filepath")
-        output_text = gr.Textbox(label="API Response")
+    
+    with gr.Row():
+        output_conformer = gr.Textbox(label="NVIDIA Conformer API Response")
+        output_whisper = gr.Textbox(label="OpenAI Whisper API Response")
 
-    submit_btn = gr.Button("Send to API")
-    submit_btn.click(process_audio, inputs=audio_input, outputs=output_text)
+    submit_btn = gr.Button("Send to APIs")
+    submit_btn.click(process_audio, inputs=audio_input, outputs=[output_conformer, output_whisper])
 
 if __name__ == "__main__":
     demo.launch()