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"""
MEXAR Core Engine - Multimodal Input Processing Module
Handles audio, image, and video input conversion to text.
"""
import os
import base64
import logging
import tempfile
from typing import Dict, List, Any, Optional
from pathlib import Path
from utils.groq_client import get_groq_client, GroqClient
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class MultimodalProcessor:
"""
Processes multimodal inputs (audio, image, video) and converts them to text.
Uses Groq Whisper for audio and Groq Vision for images.
"""
# Supported file types
AUDIO_EXTENSIONS = {'.mp3', '.wav', '.m4a', '.ogg', '.flac', '.webm'}
IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.gif', '.webp', '.bmp'}
VIDEO_EXTENSIONS = {'.mp4', '.avi', '.mov', '.mkv', '.webm'}
def __init__(self, groq_client: Optional[GroqClient] = None):
"""
Initialize the multimodal processor.
Args:
groq_client: Optional pre-configured Groq client
"""
self.client = groq_client or get_groq_client()
def process_audio(self, audio_path: str, language: str = "en") -> Dict[str, Any]:
"""
Transcribe audio file using Groq Whisper.
Args:
audio_path: Path to audio file
language: Language code for transcription
Returns:
Dict with transcription results
"""
path = Path(audio_path)
if not path.exists():
raise FileNotFoundError(f"Audio file not found: {audio_path}")
if path.suffix.lower() not in self.AUDIO_EXTENSIONS:
raise ValueError(f"Unsupported audio format: {path.suffix}")
try:
logger.info(f"Transcribing audio: {path.name}")
transcript = self.client.transcribe_audio(audio_path, language)
return {
"success": True,
"type": "audio",
"file_name": path.name,
"transcript": transcript,
"language": language,
"word_count": len(transcript.split())
}
except Exception as e:
logger.error(f"Audio transcription failed: {e}")
return {
"success": False,
"type": "audio",
"file_name": path.name,
"error": str(e)
}
def process_image(
self,
image_path: str,
prompt: str = "Describe this image in detail, including all visible text, objects, and relevant information."
) -> Dict[str, Any]:
"""
Describe image using Groq Vision.
Args:
image_path: Path to image file
prompt: Question or instruction for the vision model
Returns:
Dict with image description
"""
path = Path(image_path)
if not path.exists():
logger.error(f"Image file not found: {image_path}")
raise FileNotFoundError(f"Image file not found: {image_path}")
if path.suffix.lower() not in self.IMAGE_EXTENSIONS:
logger.error(f"Unsupported image format: {path.suffix}")
raise ValueError(f"Unsupported image format: {path.suffix}")
try:
logger.info(f"Analyzing image: {path.name} (size: {path.stat().st_size} bytes)")
# Call Groq Vision API
description = self.client.describe_image(image_path, prompt)
logger.info(f"Image analysis successful: {len(description)} chars returned")
return {
"success": True,
"type": "image",
"file_name": path.name,
"description": description,
"prompt_used": prompt
}
except Exception as e:
logger.error(f"Image analysis failed for {path.name}: {type(e).__name__}: {e}")
return {
"success": False,
"type": "image",
"file_name": path.name,
"error": str(e),
"error_type": type(e).__name__
}
def process_video(
self,
video_path: str,
max_frames: int = 5,
extract_audio: bool = True
) -> Dict[str, Any]:
"""
Process video by extracting keyframes and audio.
Args:
video_path: Path to video file
max_frames: Maximum number of keyframes to extract
extract_audio: Whether to extract and transcribe audio
Returns:
Dict with video analysis results
"""
path = Path(video_path)
if not path.exists():
raise FileNotFoundError(f"Video file not found: {video_path}")
if path.suffix.lower() not in self.VIDEO_EXTENSIONS:
raise ValueError(f"Unsupported video format: {path.suffix}")
result = {
"success": True,
"type": "video",
"file_name": path.name,
"frames": [],
"audio_transcript": None
}
try:
# Try to import OpenCV
try:
import cv2
has_opencv = True
except ImportError:
logger.warning("OpenCV not available, skipping video frame extraction")
has_opencv = False
if has_opencv:
# Extract keyframes
frames = self._extract_keyframes(video_path, max_frames)
# Analyze each frame
for i, frame_path in enumerate(frames):
frame_result = self.process_image(
frame_path,
f"This is frame {i+1} from a video. Describe what you see, focusing on actions, objects, and any text visible."
)
result["frames"].append(frame_result)
# Clean up temp frame
try:
os.remove(frame_path)
except:
pass
# Extract and transcribe audio
if extract_audio:
audio_path = self._extract_audio(video_path)
if audio_path:
audio_result = self.process_audio(audio_path)
result["audio_transcript"] = audio_result.get("transcript", "")
# Clean up temp audio
try:
os.remove(audio_path)
except:
pass
logger.info(f"Video processed: {len(result['frames'])} frames, audio: {result['audio_transcript'] is not None}")
except Exception as e:
logger.error(f"Video processing failed: {e}")
result["success"] = False
result["error"] = str(e)
return result
def _extract_keyframes(self, video_path: str, max_frames: int = 5) -> List[str]:
"""Extract keyframes from video using OpenCV."""
import cv2
cap = cv2.VideoCapture(video_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if total_frames == 0:
cap.release()
return []
# Calculate frame intervals
interval = max(1, total_frames // max_frames)
frame_paths = []
frame_count = 0
while cap.isOpened() and len(frame_paths) < max_frames:
ret, frame = cap.read()
if not ret:
break
if frame_count % interval == 0:
# Save frame to temp file
temp_path = tempfile.mktemp(suffix=".jpg")
cv2.imwrite(temp_path, frame)
frame_paths.append(temp_path)
frame_count += 1
cap.release()
return frame_paths
def _extract_audio(self, video_path: str) -> Optional[str]:
"""Extract audio track from video."""
try:
# Try using ffmpeg via subprocess
import subprocess
temp_audio = tempfile.mktemp(suffix=".mp3")
cmd = [
"ffmpeg",
"-i", video_path,
"-vn", # No video
"-acodec", "libmp3lame",
"-q:a", "2",
"-y", # Overwrite
temp_audio
]
result = subprocess.run(
cmd,
capture_output=True,
text=True,
timeout=120
)
if os.path.exists(temp_audio) and os.path.getsize(temp_audio) > 0:
return temp_audio
return None
except Exception as e:
logger.warning(f"Audio extraction failed: {e}")
return None
def fuse_inputs(
self,
text: str = "",
audio_result: Optional[Dict] = None,
image_result: Optional[Dict] = None,
video_result: Optional[Dict] = None
) -> str:
"""
Fuse all multimodal inputs into a unified text context.
Args:
text: Direct text input
audio_result: Result from process_audio
image_result: Result from process_image
video_result: Result from process_video
Returns:
Unified text context
"""
context_parts = []
# Add text input
if text and text.strip():
context_parts.append(f"[USER TEXT]\n{text.strip()}")
# Add audio transcript
if audio_result and audio_result.get("success"):
transcript = audio_result.get("transcript", "")
if transcript:
context_parts.append(f"[AUDIO TRANSCRIPT]\n{transcript}")
# Add image description
if image_result and image_result.get("success"):
description = image_result.get("description", "")
if description:
context_parts.append(f"[IMAGE DESCRIPTION]\n{description}")
# Add video content
if video_result and video_result.get("success"):
video_context = []
# Add frame descriptions
for i, frame in enumerate(video_result.get("frames", [])):
if frame.get("success"):
video_context.append(f"Frame {i+1}: {frame.get('description', '')}")
# Add audio transcript
if video_result.get("audio_transcript"):
video_context.append(f"Audio: {video_result['audio_transcript']}")
if video_context:
context_parts.append(f"[VIDEO ANALYSIS]\n" + "\n".join(video_context))
# Combine all parts
fused_context = "\n\n".join(context_parts)
logger.info(f"Fused context: {len(fused_context)} characters from {len(context_parts)} sources")
return fused_context
def process_upload(
self,
file_path: str,
additional_text: str = ""
) -> Dict[str, Any]:
"""
Automatically detect file type and process accordingly.
Args:
file_path: Path to uploaded file
additional_text: Additional text context
Returns:
Processing result with fused context
"""
path = Path(file_path)
ext = path.suffix.lower()
result = {
"success": True,
"file_type": "unknown",
"processing_result": None,
"fused_context": ""
}
try:
if ext in self.AUDIO_EXTENSIONS:
result["file_type"] = "audio"
audio_result = self.process_audio(file_path)
result["processing_result"] = audio_result
result["fused_context"] = self.fuse_inputs(
text=additional_text,
audio_result=audio_result
)
elif ext in self.IMAGE_EXTENSIONS:
result["file_type"] = "image"
image_result = self.process_image(file_path)
result["processing_result"] = image_result
result["fused_context"] = self.fuse_inputs(
text=additional_text,
image_result=image_result
)
elif ext in self.VIDEO_EXTENSIONS:
result["file_type"] = "video"
video_result = self.process_video(file_path)
result["processing_result"] = video_result
result["fused_context"] = self.fuse_inputs(
text=additional_text,
video_result=video_result
)
else:
# Treat as text file
result["file_type"] = "text"
with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
file_text = f.read()
result["fused_context"] = self.fuse_inputs(
text=f"{additional_text}\n\n[FILE CONTENT]\n{file_text}"
)
except Exception as e:
result["success"] = False
result["error"] = str(e)
logger.error(f"Upload processing failed: {e}")
return result
# Factory function
def create_multimodal_processor() -> MultimodalProcessor:
"""Create a new MultimodalProcessor instance."""
return MultimodalProcessor()
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