add cors, inference, models and fastapi

Dockerfile

@@ -1,7 +1,7 @@
 # Dockerfile
 
 # 1. Choose a base Python image
-FROM python:3.9-slim # Using Python 3.9 slim variant for smaller size
+FROM python:3.9-slim
 
 # 2. Set the working directory inside the container
 WORKDIR /code

main.py

@@ -0,0 +1,65 @@
+# main.py
+
+import os
+import io                     # For handling bytes data in memory
+import yt_dlp                 # YouTube audio downloader
+import requests               # For making HTTP requests (to audio URLs)
+from fastapi import FastAPI, HTTPException, Request # The web framework
+from fastapi.middleware.cors import CORSMiddleware # For allowing frontend access
+from pydantic import BaseModel # For data validation
+from huggingface_hub import InferenceClient # HF API client
+from dotenv import load_dotenv # To load .env file locally
+
+# --- Initial Setup ---
+
+# Load environment variables from .env file (for local development)
+# In HF Spaces, secrets are set in the Space settings, not via .env
+load_dotenv()
+
+HF_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
+
+# Check if the API key is loaded (crucial!)
+if not HF_API_KEY:
+    print("ERROR: HUGGINGFACE_API_KEY environment variable not found.")
+    # I might want to exit or raise an error here in a real deployment
+    # For now, we'll let it proceed but API calls will fail later.
+
+# Define the models we'll use from Hugging Face
+# I can change these! Smaller Whisper models (base, small, medium) are faster.
+# Different LLMs have different strengths.
+ASR_MODEL = "openai/whisper-large-v3"
+LLM_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
+
+# Initialize the Hugging Face Inference Client
+# Handles authentication using the API key automatically
+try:
+    hf_inference = InferenceClient(token=HF_API_KEY)
+    print("Hugging Face Inference Client initialized.")
+except Exception as e:
+    print(f"ERROR: Failed to initialize Hugging Face Inference Client: {e}")
+    hf_inference = None # Ensure it's None if initialization fails
+
+# Initialize the FastAPI application
+app = FastAPI(
+    title="Video Note Taker API",
+    description="Transcribes videos and generates notes using Hugging Face models.",
+    version="0.1.0",
+)
+
+# --- CORS Configuration ---
+# Configure Cross-Origin Resource Sharing (CORS)
+# This is VITAL to allow Vercel frontend (running on a different domain)
+# to make requests to this backend API.
+origins = [
+    "http://localhost:3000", # Allow my local frontend dev server
+    # !!! IMPORTANT: Add my DEPLOYED Vercel frontend URL here later !!!
+    # Example: "https://videos-notes-app.vercel.app",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins, # List of allowed origins
+    allow_credentials=True, # Allow cookies (not strictly needed now, but good practice)
+    allow_methods=["*"], # Allow all HTTP methods (GET, POST, etc.)
+    allow_headers=["*"], # Allow all headers
+)