audio+text in

app.py

@@ -1,7 +1,57 @@
 import gradio as gr
+import requests
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# FastAPI endpoint
+API_URL = "https://nexa-omni.nexa4ai.com/process-audio/"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def process_audio(audio_path, prompt=""):
+    """
+    Send audio file to FastAPI backend for processing
+    """
+    try:
+        # Prepare the file for upload
+        files = {
+            'file': ('audio.wav', open(audio_path, 'rb'), 'audio/wav')
+        }
+        # Send prompt as form data
+        data = {'prompt': prompt}
+        
+        # Make the request to FastAPI
+        response = requests.post(API_URL, files=files, data=data)
+        response.raise_for_status()
+        
+        return response.json()['response']
+    except Exception as e:
+        return f"Error processing audio: {str(e)}"
+    finally:
+        # Clean up the temporary file if it exists
+        if audio_path and os.path.exists(audio_path):
+            os.remove(audio_path)
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=process_audio,
+    inputs=[
+        gr.Audio(
+            type="filepath",
+            label="Upload or Record Audio",
+            sources=["upload", "microphone"]
+        ),
+        gr.Textbox(
+            placeholder="Enter prompt (optional)",
+            label="Prompt",
+            value="transcribe this audio in English and return me the transcription:"
+        )
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="Audio Processing Service",
+    description="Upload an audio file and optionally provide a prompt to analyze the audio content.",
+    examples=[
+        ["path/to/example.wav", "transcribe this audio in English"],
+        ["path/to/example2.wav", "describe what this audio is about"]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()