Restore Dashboard as main app: serve frontend from FastAPI, add Dockerfile, remove Streamlit entry point

Dockerfile

@@ -0,0 +1,24 @@
+# Use Python 3.12 slim as base
+FROM python:3.12-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install
+COPY backend/requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Exposure port
+EXPOSE 7860
+
+# Run the application
+# Use 0.0.0.0 and port 7860 (default for Hugging Face Spaces)
+CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "7860", "--root-path", ""]

README.md

@@ -1,14 +1,3 @@
----
-title: Swecha Telugu Audio Tracker
-emoji: 🎙️
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-sdk_version: "1.42.0"
-app_file: streamlit_app.py
-pinned: false
----
-
 # audio-tracker
 
 

backend/main.py

@@ -10,6 +10,8 @@ from typing import Any
 import requests
 from dotenv import load_dotenv
 from fastapi import FastAPI, File, Form, HTTPException, UploadFile
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
 from pydantic import BaseModel
 from fastapi.middleware.cors import CORSMiddleware
 from transformers import pipeline
@@ -216,6 +218,20 @@ def health():
         "model_loaded": get_asr_pipeline.cache_info().currsize > 0,
     }
 
+# Mount static files from the frontend directory
+# This allows serving index.html, app.js, etc. directly from the backend
+# We mount at root "/" but we must do this AFTER declaring all API routes 
+# so the static files don't intercept API calls.
+frontend_path = os.path.absolute(os.path.join(os.path.dirname(__file__), "..", "frontend"))
+
+if os.path.exists(frontend_path):
+    @app.get("/", include_in_schema=False)
+    async def read_index():
+        return FileResponse(os.path.join(frontend_path, "index.html"))
+
+    # Mount remaining static files (js, css)
+    app.mount("/", StaticFiles(directory=frontend_path), name="static")
+
 
 @app.post("/transcribe")
 async def transcribe(audio: UploadFile = File(...)):

streamlit_ui.py

@@ -0,0 +1,132 @@
+import os
+import tempfile
+import subprocess
+import re
+from functools import lru_cache
+from typing import Any
+
+import streamlit as st
+from transformers import pipeline
+import requests
+
+# Page Config
+st.set_page_config(
+    page_title="Swecha Telugu Audio Tracker",
+    page_icon="🎙️",
+    layout="centered"
+)
+
+# Constants & Env
+MODEL_ID = os.getenv("MODEL_ID", "viswamaicoe/swecha-gonthuka-asr")
+ASR_DEVICE = os.getenv("ASR_DEVICE", "cpu").lower()
+SWECHA_API_BASE = os.getenv("SWECHA_API_BASE", "https://api.corpus.swecha.org")
+SWECHA_UPLOAD_PATH = os.getenv("SWECHA_UPLOAD_PATH", "/api/v1/content")
+SWECHA_AUTH_TOKEN = os.getenv("SWECHA_AUTH_TOKEN", "")
+
+# --- Logic from backend/main.py ---
+
+@lru_cache(maxsize=1)
+def get_asr_pipeline():
+    device = 0 if ASR_DEVICE == "cuda" else -1
+    return pipeline(
+        task="automatic-speech-recognition",
+        model=MODEL_ID,
+        device=device,
+        chunk_length_s=30,
+        batch_size=8,
+    )
+
+def clean_noisy_telugu(text: str) -> str:
+    if not text:
+        return ""
+    cleaned = re.sub(r'్{2,}', '్', text)
+    cleaned = re.sub(r'\s+', ' ', cleaned).strip()
+    return cleaned
+
+def transcribe_audio(raw_bytes: bytes, suffix: str) -> str:
+    asr = get_asr_pipeline()
+    
+    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as input_file:
+        input_file.write(raw_bytes)
+        input_path = input_file.name
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as output_file:
+        output_path = output_file.name
+
+    try:
+        ffmpeg_command = [
+            "ffmpeg", "-y", "-i", input_path,
+            "-acodec", "pcm_s16le", "-ac", "1", "-ar", "16000",
+            output_path,
+        ]
+        subprocess.run(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+        result = asr(output_path, generate_kwargs={"task": "transcribe", "language": "telugu"})
+        
+        text = ""
+        if isinstance(result, dict) and "text" in result:
+            text = clean_noisy_telugu(result["text"])
+        elif isinstance(result, str):
+            text = clean_noisy_telugu(result)
+        return text
+
+    finally:
+        for path in (input_path, output_path):
+            if os.path.exists(path):
+                os.remove(path)
+
+def push_to_swecha(audio_bytes, filename, transcript, title, description):
+    if not SWECHA_AUTH_TOKEN:
+        return {"error": "SWECHA_AUTH_TOKEN is not configured"}
+
+    url = f"{SWECHA_API_BASE.rstrip('/')}/{SWECHA_UPLOAD_PATH.lstrip('/')}"
+    headers = {"Authorization": f"Bearer {SWECHA_AUTH_TOKEN}"}
+    files = {"audio": (filename, audio_bytes, "audio/webm")}
+    data = {"title": title, "description": description, "transcript": transcript}
+    
+    resp = requests.post(url, headers=headers, files=files, data=data, timeout=60)
+    return resp.json() if resp.ok else {"error": resp.text}
+
+# --- Streamlit UI ---
+
+st.title("🎙️ Swecha Telugu Audio Tracker")
+st.markdown("Convert Telugu audio to text and store it in the Swecha Corpus.")
+
+tab1, tab2 = st.tabs(["Upload/Record", "Settings"])
+
+with tab2:
+    st.header("Configuration")
+    model_id = st.text_input("ASR Model ID", MODEL_ID)
+    auth_token = st.text_input("Swecha Auth Token", SWECHA_AUTH_TOKEN, type="password")
+    if st.button("Save Settings"):
+         os.environ["MODEL_ID"] = model_id
+         os.environ["SWECHA_AUTH_TOKEN"] = auth_token
+         st.success("Settings updated for this session!")
+
+with tab1:
+    audio_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "webm", "m4a"])
+    
+    # Simple Record placeholder since custom components like streamlit-mic-recorder 
+    # might need specific installation and configuration.
+    st.info("You can also record audio if you have 'streamlit-mic-recorder' installed. For now, please upload a file.")
+
+    if audio_file:
+        st.audio(audio_file)
+        
+        with st.expander("Metadata (Optional for storage)"):
+            title = st.text_input("Title", value=audio_file.name)
+            desc = st.text_area("Description")
+
+        if st.button("Transcribe", type="primary"):
+            with st.spinner("Transcribing Telugu..."):
+                try:
+                    text = transcribe_audio(audio_file.read(), os.path.splitext(audio_file.name)[1])
+                    st.subheader("Transcription:")
+                    st.write(text)
+                    
+                    if text and SWECHA_AUTH_TOKEN:
+                        if st.button("Push to Swecha Corpus"):
+                            res = push_to_swecha(audio_file.getvalue(), audio_file.name, text, title, desc)
+                            st.json(res)
+                except Exception as e:
+                    st.error(f"Error: {e}")