Create app.py

Added flask source code for the API

app.py

@@ -0,0 +1,153 @@
+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)