File size: 9,793 Bytes
509a107 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 |
from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse, StreamingResponse
from pydantic import BaseModel
from typing import Optional, List, Dict, Any
import asyncio
import json
import time
import os
import sys
import io
import zipfile
from datetime import datetime
import base64
from pathlib import Path
from PIL import Image as PILImage
from PIL import ImageDraw, ImageFont
# Add parent directory to path for imports
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models.fastvlm_model import FastVLMModel
from utils.screen_capture import ScreenCapture
from utils.automation import BrowserAutomation
from utils.logger import NDJSONLogger
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["http://localhost:5173", "http://localhost:5174"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
model = FastVLMModel()
screen_capture = ScreenCapture()
automation = BrowserAutomation()
logger = NDJSONLogger()
class AnalysisRequest(BaseModel):
capture_screen: bool = True
include_thumbnail: bool = False
image_data: Optional[str] = None # Base64 encoded image from browser
width: Optional[int] = None
height: Optional[int] = None
class AnalysisResponse(BaseModel):
summary: str
ui_elements: List[Dict[str, Any]]
text_snippets: List[str]
risk_flags: List[str]
timestamp: str
frame_id: Optional[str] = None
class DemoRequest(BaseModel):
url: str = "https://example.com"
text_to_type: str = "test"
@app.on_event("startup")
async def startup_event():
print("Loading FastVLM-7B model...")
await model.initialize(model_type="fastvlm") # Load FastVLM-7B with quantization
status = model.get_status()
if status["is_loaded"]:
print(f"Model loaded successfully: {status['model_name']} on {status['device']}")
else:
print(f"Model loading failed: {status['error']}")
print("Running in mock mode for development")
@app.get("/")
async def root():
model_status = model.get_status()
return {
"status": "FastVLM Screen Observer API is running",
"model": model_status
}
@app.get("/model/status")
async def get_model_status():
"""Get detailed model status"""
return model.get_status()
@app.post("/model/reload")
async def reload_model(model_type: str = "auto"):
"""Reload the model with specified type"""
try:
status = await model.reload_model(model_type)
return {
"success": status["is_loaded"],
"status": status
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/model/test")
async def test_model():
"""Test model with a sample image"""
try:
# Create a test image
test_image = PILImage.new('RGB', (640, 480), color='white')
draw = ImageDraw.Draw(test_image)
# Add some text and shapes to test
draw.rectangle([50, 50, 200, 150], fill='blue', outline='black')
draw.text((100, 100), "Test Button", fill='white')
draw.rectangle([250, 50, 400, 150], fill='green', outline='black')
draw.text((300, 100), "Submit", fill='white')
draw.text((50, 200), "Sample text for testing", fill='black')
draw.text((50, 250), "Another line of text", fill='black')
# Convert to bytes
img_byte_arr = io.BytesIO()
test_image.save(img_byte_arr, format='PNG')
img_byte_arr.seek(0)
# Analyze the test image
result = await model.analyze_image(img_byte_arr.getvalue())
return {
"test_image_size": "640x480",
"analysis_result": result,
"model_status": model.get_status()
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/analyze", response_model=AnalysisResponse)
async def analyze_screen(request: AnalysisRequest):
try:
timestamp = datetime.now().isoformat()
frame_id = f"frame_{int(time.time() * 1000)}"
# Check if image data was provided from browser
if request.image_data:
# Process base64 image from browser
try:
# Remove data URL prefix if present
if request.image_data.startswith('data:image'):
image_data = request.image_data.split(',')[1]
else:
image_data = request.image_data
# Decode base64 to bytes
import base64 as b64
screenshot = b64.b64decode(image_data)
if request.include_thumbnail:
thumbnail = screen_capture.create_thumbnail(screenshot)
logger.log_frame(frame_id, thumbnail, timestamp)
else:
logger.log_frame(frame_id, None, timestamp)
analysis = await model.analyze_image(screenshot)
# Include model info in response if available
summary = analysis.get("summary", "Browser screen captured and analyzed")
if analysis.get("mock_mode"):
summary = f"[MOCK MODE] {summary}"
response = AnalysisResponse(
summary=summary,
ui_elements=analysis.get("ui_elements", []),
text_snippets=analysis.get("text_snippets", []),
risk_flags=analysis.get("risk_flags", []),
timestamp=timestamp,
frame_id=frame_id
)
logger.log_analysis(response.dict())
return response
except Exception as e:
print(f"Error processing browser image: {e}")
return AnalysisResponse(
summary=f"Error processing browser screenshot: {str(e)}",
ui_elements=[],
text_snippets=[],
risk_flags=['PROCESSING_ERROR'],
timestamp=timestamp
)
elif request.capture_screen:
# Fallback to server-side capture
screenshot = screen_capture.capture()
if request.include_thumbnail:
thumbnail = screen_capture.create_thumbnail(screenshot)
logger.log_frame(frame_id, thumbnail, timestamp)
else:
logger.log_frame(frame_id, None, timestamp)
analysis = await model.analyze_image(screenshot)
response = AnalysisResponse(
summary=analysis.get("summary", ""),
ui_elements=analysis.get("ui_elements", []),
text_snippets=analysis.get("text_snippets", []),
risk_flags=analysis.get("risk_flags", []),
timestamp=timestamp,
frame_id=frame_id
)
logger.log_analysis(response.dict())
return response
else:
return AnalysisResponse(
summary="No screen captured",
ui_elements=[],
text_snippets=[],
risk_flags=[],
timestamp=timestamp
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/demo")
async def run_demo(request: DemoRequest, background_tasks: BackgroundTasks):
try:
background_tasks.add_task(
automation.run_demo,
request.url,
request.text_to_type
)
return {
"status": "Demo started",
"url": request.url,
"text": request.text_to_type
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/export")
async def export_logs():
try:
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zipf:
logs_path = Path("logs/logs.ndjson")
if logs_path.exists():
zipf.write(logs_path, "logs.ndjson")
frames_dir = Path("logs/frames")
if frames_dir.exists():
for frame_file in frames_dir.glob("*.png"):
zipf.write(frame_file, f"frames/{frame_file.name}")
zip_buffer.seek(0)
return StreamingResponse(
zip_buffer,
media_type="application/zip",
headers={
"Content-Disposition": f"attachment; filename=screen_observer_export_{int(time.time())}.zip"
}
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/logs/stream")
async def stream_logs():
async def log_generator():
last_position = 0
log_file = Path("logs/logs.ndjson")
while True:
if log_file.exists():
with open(log_file, "r") as f:
f.seek(last_position)
new_lines = f.readlines()
last_position = f.tell()
for line in new_lines:
yield f"data: {line}\n\n"
await asyncio.sleep(0.5)
return StreamingResponse(
log_generator(),
media_type="text/event-stream"
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |