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import os
import subprocess
import whisper
import requests
from flask import Flask, request, jsonify, send_file
import tempfile
app = Flask(__name__)
# Gemini API settings
from dotenv import load_dotenv
import requests
# Load the .env file
load_dotenv()
# Fetch the API key from the .env file
API_KEY = os.getenv("FIRST_API_KEY")
# Ensure the API key is loaded correctly
if not API_KEY:
raise ValueError("API Key not found. Make sure it is set in the .env file.")
GEMINI_API_ENDPOINT = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-latest:generateContent"
GEMINI_API_KEY = API_KEY
# Load Whisper AI model at startup
print("Loading Whisper AI model...")
whisper_model = whisper.load_model("base") # Choose model size: tiny, base, small, medium, large
print("Whisper AI model loaded successfully.")
@app.route('/process-video', methods=['POST'])
def process_video():
"""
Flask endpoint to process video:
1. Extract audio and transcribe using Whisper AI.
2. Send transcription to Gemini API for recipe information extraction.
3. Return structured data in the response.
"""
if 'video' not in request.files:
return jsonify({"error": "No video file provided"}), 400
video_file = request.files['video']
try:
# Save video to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video_file:
video_file.save(temp_video_file.name)
print(f"Video file saved: {temp_video_file.name}")
# Extract audio and transcribe using Whisper AI
transcription = transcribe_audio(temp_video_file.name)
if not transcription:
return jsonify({"error": "Audio transcription failed"}), 500
# Generate structured recipe information using Gemini API
structured_data = query_gemini_api(transcription)
return jsonify(structured_data)
except Exception as e:
return jsonify({"error": str(e)}), 500
finally:
# Clean up temporary files
if os.path.exists(temp_video_file.name):
os.remove(temp_video_file.name)
def transcribe_audio(video_path):
"""
Extract audio from video file and transcribe using Whisper AI.
"""
try:
# Extract audio using ffmpeg
audio_path = video_path.replace(".mp4", ".wav")
command = [
"ffmpeg",
"-i", video_path,
"-q:a", "0",
"-map", "a",
audio_path
]
subprocess.run(command, check=True)
print(f"Audio extracted to: {audio_path}")
# Transcribe audio using Whisper AI
print("Transcribing audio...")
result = whisper_model.transcribe(audio_path)
# Clean up audio file after transcription
if os.path.exists(audio_path):
os.remove(audio_path)
return result.get("text", "").strip()
except Exception as e:
print(f"Error in transcription: {e}")
return None
def query_gemini_api(transcription):
"""
Send transcription text to Gemini API and fetch structured recipe information.
"""
try:
# Define the structured prompt
prompt = (
"Analyze the provided cooking video transcription and extract the following structured information:\n"
"1. Recipe Name: Identify the name of the dish being prepared.\n"
"2. Ingredients List: Extract a detailed list of ingredients with their respective quantities (if mentioned).\n"
"3. Steps for Preparation: Provide a step-by-step breakdown of the recipe's preparation process, organized and numbered sequentially.\n"
"4. Cooking Techniques Used: Highlight the cooking techniques demonstrated in the video, such as searing, blitzing, wrapping, etc.\n"
"5. Equipment Needed: List all tools, appliances, or utensils mentioned, e.g., blender, hot pan, cling film, etc.\n"
"6. Nutritional Information (if inferred): Provide an approximate calorie count or nutritional breakdown based on the ingredients used.\n"
"7. Serving size: In count of people or portion size.\n"
"8. Special Notes or Variations: Include any specific tips, variations, or alternatives mentioned.\n"
"9. Festive or Thematic Relevance: Note if the recipe has any special relevance to holidays, events, or seasons.\n"
f"Text: {transcription}\n"
)
# Prepare the payload and headers
payload = {
"contents": [
{
"parts": [
{"text": prompt}
]
}
]
}
headers = {"Content-Type": "application/json"}
# Send request to Gemini API
print("Querying Gemini API...")
response = requests.post(
f"{GEMINI_API_ENDPOINT}?key={GEMINI_API_KEY}",
json=payload,
headers=headers
)
response.raise_for_status()
# Extract and return the structured data
data = response.json()
return data.get("candidates", [{}])[0].get("content", {}).get("parts", [{}])[0].get("text", "No result found")
except requests.exceptions.RequestException as e:
print(f"Error querying Gemini API: {e}")
return {"error": str(e)}
if __name__ == '__main__':
app.run(debug=True)
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