from openai import OpenAI
import base64

client = OpenAI()

def encode_image(image_path):
    with open(image_path, "rb") as f:
        return base64.standard_b64encode(f.read()).decode("utf-8")

def analyze_image(image_path, prompt):
    base64_image = encode_image(image_path)
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}",
                            "detail": "high"
                        }
                    }
                ]
            }
        ],
        max_tokens=1000
    )
    return response.choices[0].message.content

# Usage
result = analyze_image("chart.png", "Extract all data from this chart as JSON")
print(result)

import anthropic
import base64

client = anthropic.Anthropic()

def analyze_with_claude(image_path, prompt):
    with open(image_path, "rb") as f:
        image_data = base64.standard_b64encode(f.read()).decode("utf-8")
    
    # Determine media type
    media_type = "image/jpeg" if image_path.endswith(".jpg") else "image/png"
    
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": media_type,
                            "data": image_data
                        }
                    },
                    {
                        "type": "text",
                        "text": prompt
                    }
                ]
            }
        ]
    )
    return message.content[0].text

import asyncio
from concurrent.futures import ThreadPoolExecutor

async def process_images_batch(image_paths, prompt):
    async def process_single(path):
        return await asyncio.to_thread(analyze_image, path, prompt)
    
    tasks = [process_single(path) for path in image_paths]
    results = await asyncio.gather(*tasks)
    return dict(zip(image_paths, results))

# Process multiple images concurrently
images = ["img1.jpg", "img2.jpg", "img3.jpg"]
results = asyncio.run(process_images_batch(images, "Describe this image"))

from openai import OpenAI

client = OpenAI()

def transcribe_audio(audio_path, language="en"):
    with open(audio_path, "rb") as audio_file:
        transcript = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
            language=language,
            response_format="verbose_json",
            timestamp_granularities=["word", "segment"]
        )
    return transcript

# Get detailed transcription with timestamps
result = transcribe_audio("meeting.mp3")
for segment in result.segments:
    print(f"[{segment.start:.2f}s] {segment.text}")

from openai import OpenAI
from pathlib import Path

client = OpenAI()

def generate_speech(text, voice="nova", output_path="output.mp3"):
    response = client.audio.speech.create(
        model="tts-1-hd",
        voice=voice,  # alloy, echo, fable, onyx, nova, shimmer
        input=text,
        speed=1.0
    )
    
    response.stream_to_file(output_path)
    return output_path

# Generate natural speech
generate_speech(
    "Welcome to our multimodal AI tutorial. Let's explore the possibilities.",
    voice="nova"
)

import pyaudio
import numpy as np
from openai import OpenAI

client = OpenAI()

class RealTimeTranscriber:
    def __init__(self):
        self.audio = pyaudio.PyAudio()
        self.stream = None
        self.buffer = []
        
    def start_stream(self):
        self.stream = self.audio.open(
            format=pyaudio.paInt16,
            channels=1,
            rate=16000,
            input=True,
            frames_per_buffer=1024,
            stream_callback=self._callback
        )
        self.stream.start_stream()
    
    def _callback(self, in_data, frame_count, time_info, status):
        self.buffer.append(in_data)
        
        # Process every 5 seconds of audio
        if len(self.buffer) >= 78:  # ~5 seconds at 16kHz
            audio_data = b''.join(self.buffer)
            self.buffer = []
            self._process_audio(audio_data)
        
        return (None, pyaudio.paContinue)
    
    def _process_audio(self, audio_data):
        # Save to temp file and transcribe
        import tempfile
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
            # Write WAV header and data
            write_wav(f, audio_data, 16000)
            transcript = transcribe_audio(f.name)
            print(f"Transcribed: {transcript.text}")

import cv2
from pathlib import Path

def extract_key_frames(video_path, interval_seconds=5):
    cap = cv2.VideoCapture(video_path)
    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_interval = int(fps * interval_seconds)
    
    frames = []
    frame_count = 0
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
            
        if frame_count % frame_interval == 0:
            # Convert to RGB and save
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            timestamp = frame_count / fps
            frames.append({
                "frame": frame_rgb,
                "timestamp": timestamp
            })
        
        frame_count += 1
    
    cap.release()
    return frames

def analyze_video(video_path, prompt):
    frames = extract_key_frames(video_path)
    
    analyses = []
    for frame_data in frames:
        # Save frame temporarily
        temp_path = f"/tmp/frame_{frame_data['timestamp']}.jpg"
        cv2.imwrite(temp_path, cv2.cvtColor(frame_data['frame'], cv2.COLOR_RGB2BGR))
        
        analysis = analyze_image(temp_path, prompt)
        analyses.append({
            "timestamp": frame_data['timestamp'],
            "analysis": analysis
        })
    
    return analyses

async def summarize_video(video_path):
    # Extract frames
    frames = extract_key_frames(video_path, interval_seconds=10)
    
    # Analyze key frames
    frame_descriptions = []
    for frame in frames[:20]:  # Limit to 20 frames
        temp_path = save_temp_frame(frame['frame'])
        desc = analyze_image(temp_path, "Describe what's happening in this frame")
        frame_descriptions.append(f"[{frame['timestamp']:.1f}s]: {desc}")
    
    # Generate summary
    summary_prompt = f"""Based on these frame descriptions from a video, provide a concise summary:

{chr(10).join(frame_descriptions)}

Summarize the video content, key events, and overall narrative."""

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": summary_prompt}]
    )
    
    return response.choices[0].message.content

def analyze_document(document_image):
    prompt = """Analyze this document image:
    1. Extract all text content
    2. Identify the document type
    3. Extract key information (dates, names, amounts)
    4. Summarize the document purpose
    
    Return as structured JSON."""
    
    result = analyze_image(document_image, prompt)
    return json.loads(result)

def describe_image_audio(image_path, output_audio="description.mp3"):
    # Analyze image
    description = analyze_image(
        image_path,
        "Provide a vivid, detailed description of this image suitable for someone who cannot see it."
    )
    
    # Convert to speech
    audio_path = generate_speech(description, voice="nova", output_path=output_audio)
    
    return {
        "description": description,
        "audio": audio_path
    }

from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma

class MultimodalRAG:
    def __init__(self):
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = Chroma(embedding_function=self.embeddings)
        
    def add_image(self, image_path, metadata=None):
        # Generate description
        description = analyze_image(image_path, "Describe this image in detail")
        
        # Store with metadata
        self.vectorstore.add_texts(
            texts=[description],
            metadatas=[{
                "type": "image",
                "path": image_path,
                **(metadata or {})
            }]
        )
    
    def add_audio(self, audio_path, metadata=None):
        # Transcribe
        transcript = transcribe_audio(audio_path)
        
        # Store
        self.vectorstore.add_texts(
            texts=[transcript.text],
            metadatas=[{
                "type": "audio",
                "path": audio_path,
                **(metadata or {})
            }]
        )
    
    def search(self, query, k=5):
        results = self.vectorstore.similarity_search(query, k=k)
        return results

import hashlib
from functools import lru_cache

def get_image_hash(image_path):
    with open(image_path, "rb") as f:
        return hashlib.md5(f.read()).hexdigest()

@lru_cache(maxsize=1000)
def cached_analyze_image(image_hash, prompt):
    # Actual analysis happens here
    return analyze_image_internal(image_hash, prompt)

def analyze_with_cache(image_path, prompt):
    image_hash = get_image_hash(image_path)
    return cached_analyze_image(image_hash, prompt)

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def robust_analyze_image(image_path, prompt):
    try:
        return analyze_image(image_path, prompt)
    except Exception as e:
        logger.error(f"Image analysis failed: {e}")
        raise