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import os
import pathlib
import tempfile
from collections.abc import Iterator
from threading import Thread
import av
import gradio as gr
import spaces
import torch
from gradio.utils import get_upload_folder
from transformers import AutoModelForImageTextToText, AutoProcessor
from transformers.generation.streamers import TextIteratorStreamer
model_id = "google/gemma-3n-E4B-it"
# Get HF token from environment
HF_TOKEN2 = os.getenv("HF_TOKEN2")
access_token = HF_TOKEN2
# Load processor and model with authentication token
processor = AutoProcessor.from_pretrained(model_id, token=access_token)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
token=access_token
)
IMAGE_FILE_TYPES = (".jpg", ".jpeg", ".png", ".webp")
VIDEO_FILE_TYPES = (".mp4", ".mov", ".webm")
AUDIO_FILE_TYPES = (".mp3", ".wav")
GRADIO_TEMP_DIR = get_upload_folder()
TARGET_FPS = int(os.getenv("TARGET_FPS", "3"))
MAX_FRAMES = int(os.getenv("MAX_FRAMES", "30"))
MAX_INPUT_TOKENS = int(os.getenv("MAX_INPUT_TOKENS", "10_000"))
def get_file_type(path: str) -> str:
if path.endswith(IMAGE_FILE_TYPES):
return "image"
if path.endswith(VIDEO_FILE_TYPES):
return "video"
if path.endswith(AUDIO_FILE_TYPES):
return "audio"
error_message = f"Unsupported file type: {path}"
raise ValueError(error_message)
def count_files_in_new_message(paths: list[str]) -> tuple[int, int]:
video_count = 0
non_video_count = 0
for path in paths:
if path.endswith(VIDEO_FILE_TYPES):
video_count += 1
else:
non_video_count += 1
return video_count, non_video_count
def validate_media_constraints(message: dict) -> bool:
print(f"Debug - Validating message: {message}")
if not message.get("files"):
print("Debug - No files in message")
return True
files = message["files"]
print(f"Debug - Files to validate: {files}")
video_count, non_video_count = count_files_in_new_message(files)
print(f"Debug - Video count: {video_count}, Non-video count: {non_video_count}")
if video_count > 1:
gr.Warning("⚠️ Only one video is supported per message.")
return False
if video_count == 1 and non_video_count > 0:
gr.Warning("⚠️ Cannot mix videos with other media types.")
return False
return True
def extract_frames_to_tempdir(
video_path: str,
target_fps: float,
max_frames: int | None = None,
parent_dir: str | None = None,
prefix: str = "frames_",
) -> str:
print(f"Debug - Extracting frames from: {video_path}")
# Validate video file exists
if not os.path.exists(video_path):
raise ValueError(f"Video file not found: {video_path}")
temp_dir = tempfile.mkdtemp(prefix=prefix, dir=parent_dir)
print(f"Debug - Created temp dir: {temp_dir}")
try:
container = av.open(video_path)
video_stream = container.streams.video[0]
print(f"Debug - Video stream found: {video_stream}")
if video_stream.duration is None or video_stream.time_base is None:
raise ValueError("Video stream is missing duration or time_base information")
time_base = video_stream.time_base
duration = float(video_stream.duration * time_base)
interval = 1.0 / target_fps
total_frames = int(duration * target_fps)
if max_frames is not None:
total_frames = min(total_frames, max_frames)
print(f"Debug - Will extract {total_frames} frames over {duration:.2f} seconds")
target_times = [i * interval for i in range(total_frames)]
target_index = 0
extracted_count = 0
for frame in container.decode(video=0):
if frame.pts is None:
continue
timestamp = float(frame.pts * time_base)
if target_index < len(target_times) and abs(timestamp - target_times[target_index]) < (interval / 2):
frame_path = pathlib.Path(temp_dir) / f"frame_{target_index:04d}.jpg"
frame.to_image().save(frame_path)
target_index += 1
extracted_count += 1
if max_frames is not None and target_index >= max_frames:
break
container.close()
print(f"Debug - Successfully extracted {extracted_count} frames to {temp_dir}")
return temp_dir
except Exception as e:
print(f"Debug - Error during frame extraction: {e}")
# Clean up temp directory on error
import shutil
shutil.rmtree(temp_dir, ignore_errors=True)
raise
def process_new_user_message(message: dict) -> list[dict]:
# Debug: Print the message structure
print(f"Debug - Received message: {message}")
if not message.get("files"):
return [{"type": "text", "text": message["text"]}]
file_types = [get_file_type(path) for path in message["files"]]
print(f"Debug - Detected file types: {file_types}")
# Handle video files
if len(file_types) == 1 and file_types[0] == "video":
print(f"Debug - Processing video: {message['files'][0]}")
gr.Info(f"🎥 Processing video at {TARGET_FPS} FPS, max {MAX_FRAMES} frames. This may take a moment...")
try:
temp_dir = extract_frames_to_tempdir(
message["files"][0],
target_fps=TARGET_FPS,
max_frames=MAX_FRAMES,
parent_dir=GRADIO_TEMP_DIR,
)
paths = sorted(pathlib.Path(temp_dir).glob("*.jpg"))
if not paths:
gr.Warning("⚠️ Could not extract frames from video. Please try a different video format.")
return [{"type": "text", "text": message["text"]}]
gr.Success(f"✅ Extracted {len(paths)} frames from video successfully!")
print(f"Debug - Extracted {len(paths)} frames")
return [
{"type": "text", "text": message["text"]},
*[{"type": "image", "image": path.as_posix()} for path in paths],
]
except Exception as e:
print(f"Debug - Video processing error: {e}")
gr.Error(f"❌ Error processing video: {str(e)}")
return [{"type": "text", "text": message["text"]}]
# Handle mixed files or multiple videos
if "video" in file_types:
video_count = file_types.count("video")
if video_count > 1:
gr.Warning("⚠️ Only one video is supported per message. Please upload one video at a time.")
return [{"type": "text", "text": message["text"]}]
non_video_count = len(file_types) - video_count
if non_video_count > 0:
gr.Warning("⚠️ Cannot mix videos with other file types. Please upload either a video alone or other files without video.")
return [{"type": "text", "text": message["text"]}]
# Handle other file types normally
return [
{"type": "text", "text": message["text"]},
*[{"type": file_type, file_type: path} for path, file_type in zip(message["files"], file_types, strict=True)],
]
def process_history(history: list[dict]) -> list[dict]:
messages = []
current_user_content: list[dict] = []
for item in history:
if item["role"] == "assistant":
if current_user_content:
messages.append({"role": "user", "content": current_user_content})
current_user_content = []
messages.append({"role": "assistant", "content": [{"type": "text", "text": item["content"]}]})
else:
content = item["content"]
if isinstance(content, str):
current_user_content.append({"type": "text", "text": content})
else:
filepath = content[0]
file_type = get_file_type(filepath)
current_user_content.append({"type": file_type, file_type: filepath})
return messages
@spaces.GPU(duration=120)
@torch.inference_mode()
def generate(message: dict, history: list[dict]) -> Iterator[str]:
print(f"Debug - Generate called with message: {message}")
print(f"Debug - Message keys: {message.keys()}")
if not validate_media_constraints(message):
print("Debug - Media constraints validation failed")
yield "Sorry, there was an issue with the uploaded files. Please check the file types and try again."
return
messages = []
system_prompt = "You are a helpful AI assistant. You can analyze images, transcribe audio, describe videos, and answer questions. Provide detailed, accurate, and helpful responses."
if system_prompt:
messages.append({"role": "system", "content": [{"type": "text", "text": system_prompt}]})
messages.extend(process_history(history))
try:
user_content = process_new_user_message(message)
print(f"Debug - Processed user content: {user_content}")
messages.append({"role": "user", "content": user_content})
except Exception as e:
print(f"Debug - Error processing user message: {e}")
yield f"Sorry, there was an error processing your message: {str(e)}"
return
try:
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
)
n_tokens = inputs["input_ids"].shape[1]
if n_tokens > MAX_INPUT_TOKENS:
gr.Warning(
f"Input too long. Max {MAX_INPUT_TOKENS} tokens. Got {n_tokens} tokens. This limit is set to avoid CUDA out-of-memory errors in this Space."
)
yield "Sorry, your input is too long. Please try with shorter text or fewer files."
return
inputs = inputs.to(device=model.device, dtype=torch.bfloat16)
streamer = TextIteratorStreamer(processor, timeout=30.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
inputs,
streamer=streamer,
max_new_tokens=700,
do_sample=False,
disable_compile=True,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
output = ""
for delta in streamer:
output += delta
yield output
except Exception as e:
print(f"Debug - Error during generation: {e}")
yield f"Sorry, there was an error generating the response: {str(e)}"
def chat_fn(message, history):
"""Main chat function that handles multimodal input and generates responses"""
if not message:
return ""
# Handle multimodal input from MultimodalTextbox
if isinstance(message, dict):
text = message.get("text", "")
files = message.get("files", [])
else:
text = str(message)
files = []
if not text.strip() and not files:
return ""
# Create message dict for processing
message_dict = {
"text": text,
"files": [f.name if hasattr(f, 'name') else f for f in files] if files else []
}
# Generate streaming response
for chunk in generate(message_dict, history):
yield chunk
# Create the ChatInterface - pure Gradio with no custom CSS
demo = gr.ChatInterface(
fn=chat_fn,
multimodal=True,
type="messages",
textbox=gr.MultimodalTextbox(
placeholder="Message Gemma...",
container=False,
scale=7,
file_types=list(IMAGE_FILE_TYPES + VIDEO_FILE_TYPES + AUDIO_FILE_TYPES),
file_count="multiple",
show_label=False
),
title="Gemma",
description=None,
examples=None,
cache_examples=False,
theme=gr.themes.Soft(
primary_hue="emerald",
secondary_hue="slate",
neutral_hue="slate",
font=gr.themes.GoogleFont("Inter")
),
fill_height=True,
delete_cache=(100, 100), # Keep some conversation history
show_progress="minimal",
concurrency_limit=10,
autofocus=True
)
if __name__ == "__main__":
demo.launch(share=True, show_error=True) |