Create app.py

app.py

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+import gradio as gr
+import requests
+import soundfile as sf
+import numpy as np
+import tempfile
+import os
+
+
+
+ASR_URL = "asr space url"
+LLM_URL = "llm space url"
+TTS_URL =  "tts space url"
+
+
+
+def call_asr(audio_path):
+    payload = {
+        "data": [audio_path]
+    }
+    r = requests.post(ASR_URL, json=payload, timeout=120)
+    return r.json()["data"][0]
+
+
+def call_llm(text):
+    payload = {
+        "data": [text]
+    }
+    r = requests.post(LLM_URL, json=payload, timeout=120)
+    return r.json()["data"][0]
+
+
+def call_tts(text, language):
+    payload = {
+        "data": [text, language]
+    }
+    r = requests.post(TTS_URL, json=payload, timeout=120)
+    return r.json()["data"][0]
+
+
+
+
+
+def healthatlas_pipeline(
+    mode,
+    text_input,
+    audio_input,
+    tts_language
+):
+    if mode == "Text":
+        if not text_input.strip():
+            return "Please enter text", None
+
+        llm_response = call_llm(text_input)
+
+        audio_path = call_tts(llm_response, tts_language)
+        return llm_response, audio_path
+
+    else:  # Audio mode
+        if audio_input is None:
+            return "Please upload audio", None
+
+        # Save uploaded audio
+        sr, audio = audio_input
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+            sf.write(f.name, audio, sr)
+            audio_path = f.name
+
+        transcription = call_asr(audio_path)
+        llm_response = call_llm(transcription)
+        audio_out = call_tts(llm_response, tts_language)
+
+        return llm_response, audio_out
+
+
+
+#gradio user interface
+with gr.Blocks(title="HealthAtlas AI") as demo:
+    gr.Markdown(
+        """
+        # 🏥 HealthAtlas  
+        Multilingual AI Health Triage Assistant  
+        **Languages:** English · Yoruba · Hausa · Igbo
+        """
+    )
+
+    mode = gr.Radio(
+        ["Text", "Audio"],
+        value="Text",
+        label="Input Mode"
+    )
+
+    text_input = gr.Textbox(
+        label="Text Input",
+        visible=True
+    )
+
+    audio_input = gr.Audio(
+        label="Audio Input",
+        type="numpy",
+        visible=False
+    )
+
+    tts_language = gr.Dropdown(
+        choices=["yoruba", "hausa"],
+        value="yoruba",
+        label="Speech Output Language"
+    )
+
+    output_text = gr.Textbox(
+        label="HealthAtlas Response"
+    )
+
+    output_audio = gr.Audio(
+        label="Spoken Response"
+    )
+
+    submit = gr.Button("Submit")
+
+    def toggle_inputs(m):
+        return (
+            gr.update(visible=m == "Text"),
+            gr.update(visible=m == "Audio")
+        )
+
+    mode.change(
+        toggle_inputs,
+        inputs=mode,
+        outputs=[text_input, audio_input]
+    )
+
+    submit.click(
+        healthatlas_pipeline,
+        inputs=[mode, text_input, audio_input, tts_language],
+        outputs=[output_text, output_audio]
+    )
+
+if __name__ == "__main__":
+    demo.launch()