Update app.py

app.py

@@ -1,88 +1,70 @@
 import os
 import csv
+from datetime import datetime
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
 import whisper
-import gradio as gr
 from transformers import pipeline
-from fastapi import FastAPI, UploadFile, File
-from fastapi.middleware.wsgi import WSGIMiddleware
 
-# ================== DIRECTORIES =====================
-UPLOAD_DIR = "uploads"
-CSV_DIR = "csv_files"
-os.makedirs(UPLOAD_DIR, exist_ok=True)
-os.makedirs(CSV_DIR, exist_ok=True)
+# ---------------- Config ----------------
+UPLOAD_DIR = os.getenv("UPLOAD_DIR", "./uploads")
 
-# ================== LOAD MODELS =====================
-print("Loading Whisper model...")
-whisper_model = whisper.load_model("base")
-print("Loading summarizer model...")
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-# ================== FUNCTION =======================
-def transcribe_and_summarize(audio_path):
-    if audio_path is None:
-        return "No file uploaded.", "No summary.", None
+if not os.path.exists(UPLOAD_DIR):
+    try:
+        os.makedirs(UPLOAD_DIR, exist_ok=True)
+    except PermissionError:
+        UPLOAD_DIR = "."
 
-    # Save audio to uploads folder
-    audio_filename = os.path.basename(audio_path)
-    saved_audio_path = os.path.join(UPLOAD_DIR, audio_filename)
-    if not os.path.exists(saved_audio_path):
-        os.rename(audio_path, saved_audio_path)
+# ---------------- App ----------------
+app = FastAPI()
 
-    # Transcription
-    try:
-        result = whisper_model.transcribe(saved_audio_path)
-        transcription = result["text"]
-    except Exception as e:
-        return f"Transcription failed: {e}", "No summary.", None
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-    # Summarization
-    try:
-        summary = summarizer(transcription, max_length=60, min_length=10, do_sample=False)[0]["summary_text"]
-    except Exception as e:
-        summary = f"Summarization failed: {e}"
+# ---------------- Models ----------------
+asr_model = whisper.load_model("base")  
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-    # Save CSV
-    csv_file = os.path.join(CSV_DIR, "transcriptions.csv")
+# ---------------- Utils ----------------
+def save_to_csv(audio_file: str, transcription: str, summary: str):
+    csv_file = os.path.join(UPLOAD_DIR, "output.csv")
     file_exists = os.path.isfile(csv_file)
+
     with open(csv_file, mode="a", newline="", encoding="utf-8") as f:
         writer = csv.writer(f)
         if not file_exists:
-            writer.writerow(["Audio File", "Transcription", "Summary"])
-        writer.writerow([saved_audio_path, transcription, summary])
-
-    return transcription, summary, csv_file
-
-# ================== GRADIO INTERFACE =================
-gradio_interface = gr.Interface(
-    fn=transcribe_and_summarize,
-    inputs=gr.Audio(type="filepath", label="Upload Audio File"),
-    outputs=[
-        gr.Textbox(label="🔊 Transcription"),
-        gr.Textbox(label="📝 Summary"),
-        gr.File(label="📄 Download CSV with Audio Links")
-    ],
-    title="🎤 Audio Transcriber + Summarizer",
-    description="Upload an audio file → Transcribe it with Whisper → Summarize it using BART → Download CSV."
-)
+            writer.writerow(["Timestamp", "Audio_File", "Transcription", "Summary"])
+        writer.writerow([datetime.now().isoformat(), audio_file, transcription, summary])
 
-# ================== FASTAPI APP ======================
-api_app = FastAPI(title="Audio Transcriber + Summarizer API")
+# ---------------- Routes ----------------
+@app.post("/transcribe/")
+async def transcribe_audio(file: UploadFile = File(...), summary_length: int = Form(100)):
+    try:
+        # Save uploaded file
+        file_path = os.path.join(UPLOAD_DIR, file.filename)
+        with open(file_path, "wb") as buffer:
+            buffer.write(await file.read())
 
-@api_app.post("/api/transcribe/")
-async def transcribe_api(file: UploadFile = File(...)):
-    # Save uploaded file temporarily
-    temp_path = f"/tmp/{file.filename}"
-    with open(temp_path, "wb") as f:
-        f.write(await file.read())
+        # Transcribe
+        result = asr_model.transcribe(file_path)
+        transcription = result["text"]
 
-    transcription, summary, csv_file = transcribe_and_summarize(temp_path)
-    return {"transcription": transcription, "summary": summary, "csv_file": csv_file}
+        # Summarize
+        summary = summarizer(transcription, max_length=summary_length, min_length=30, do_sample=False)[0]["summary_text"]
 
-# Mount Gradio on FastAPI
-api_app.mount("/gradio", WSGIMiddleware(gradio_interface.launch(prevent_thread_lock=True)))
+        # Save to CSV
+        save_to_csv(file.filename, transcription, summary)
 
-# ================== ENTRY POINT =====================
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(api_app, host="0.0.0.0", port=8000)
+        return {
+            "filename": file.filename,
+            "transcription": transcription,
+            "summary": summary,
+            "csv_saved": True
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))