Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,37 +1,50 @@
+import os
 import streamlit as st
-from pydub import AudioSegment
-from transformers import pipeline
+import soundfile as sf
 from openai import OpenAI
+from transformers import pipeline
 
+# -----------------------------
+# HF Token
+# -----------------------------
 HF_TOKEN = st.secrets["HF_TOKEN"]
+
 client = OpenAI(base_url="https://router.huggingface.co/v1", api_key=HF_TOKEN)
 
+# -----------------------------
+# ASR Model (Whisper)
+# -----------------------------
 @st.cache_resource
 def load_asr():
     return pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
 
 asr = load_asr()
 
+# -----------------------------
+# Streamlit UI
+# -----------------------------
 st.title("🏥 AZ Medical Speech → Insight")
-st.write("Audio faylı yükləyin və tibbi insight çıxarın.")
+st.write("Audio yükləyin və tibbi insight çıxarın.")
 
 uploaded_file = st.file_uploader("Audio seçin (.wav, .mp3, .ogg, .m4a)", type=["wav", "mp3", "ogg", "m4a"])
 
 if uploaded_file is not None:
+    # Soundfile ilə .wav fayl yarat
     wav_path = "temp.wav"
-    audio = AudioSegment.from_file(uploaded_file)
-    audio.export(wav_path, format="wav")
-    
+    data, samplerate = sf.read(uploaded_file)
+    sf.write(wav_path, data, samplerate)
+
     st.audio(wav_path, format="audio/wav")
 
-    with st.spinner("Audio tanınır..."):
-        az_text = asr(wav_path, generate_kwargs={"task": "transcribe", "language": "az"})["text"].strip()
-        en_text = asr(wav_path, generate_kwargs={"task": "translate", "language": "az"})["text"].strip()
+    # ASR → AZ və EN
+    az_text = asr(wav_path, generate_kwargs={"task": "transcribe", "language": "az"})["text"].strip()
+    en_text = asr(wav_path, generate_kwargs={"task": "translate", "language": "az"})["text"].strip()
 
     st.subheader("🎧 Transcripts")
     st.write("AZ:", az_text)
     st.write("EN:", en_text)
 
+    # LLM → Insight
     messages = [
         {
             "role": "system",
@@ -47,16 +60,14 @@ if uploaded_file is not None:
         }
     ]
 
-    with st.spinner("Tibbi insight hazırlanır..."):
-        completion = client.chat.completions.create(
-            model="Intelligent-Internet/II-Medical-8B-1706:featherless-ai",
-            messages=messages,
-            max_tokens=400,
-            temperature=0.4
-        )
+    completion = client.chat.completions.create(
+        model="Intelligent-Internet/II-Medical-8B-1706:featherless-ai",
+        messages=messages,
+        max_tokens=400,
+        temperature=0.4
+    )
 
-        llm_response = completion.choices[0].message.content.strip()
+    llm_response = completion.choices[0].message.content.strip()
 
     st.subheader("💡 MODEL INSIGHT")
     st.write(llm_response)
-