-
Notifications
You must be signed in to change notification settings - Fork 0
/
server.py
134 lines (96 loc) · 3.77 KB
/
server.py
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
from fastapi import FastAPI, HTTPException, UploadFile, File
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse # To send JSON responses
import base64
import io
from PIL import Image # For handling image files
import cv2 # For image processing with OpenCV
import new2 # Importing the logic from the `new2.py` file
import google.generativeai as genai
# Create an instance of the FastAPI class
app = FastAPI()
def setCameraUrl(camera_ip, camera_port):
# URL of the camera feed
return f"http://{camera_ip}:{camera_port}/video"
global frame_saved
# Configure CORS
origins = [
"http://localhost",
"http://localhost:5173", # Add the origin of your React app
# Add more origins as needed
]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["GET", "POST"],
allow_headers=["*"],
)
def generateResponse(result):
# If CombinedImg is included, encode it to base64
response_data = {"result": result["result"], "probability": result["probability"]}
if result["CombinedImg"] is not None:
combined_img_base64 = new2.encode_image_to_base64(result["CombinedImg"])
response_data["combined_img_base64"] = combined_img_base64
print("not none")
return response_data
# Define a route handler for the root endpoint
@app.get("/")
async def read_root():
return {"message": "Hello, World"}
@app.get("/invokePython")
async def Cam(camera_ip: str, camera_port: str):
print("reached")
cap = cv2.VideoCapture(setCameraUrl(camera_ip, camera_port))
if not cap.isOpened():
raise HTTPException(status_code=500, detail="Error: Could not open camera.")
while True:
ret, frame = cap.read()
if not ret:
raise HTTPException(
status_code=500, detail="Error: Could not read frame from camera."
)
break
cv2.imshow("Camera Feed", frame)
if cv2.waitKey(1) & 0xFF == ord("s"):
frame_saved = frame
break
cap.release()
cv2.destroyAllWindows()
result = new2.handler(frame_saved)
response_data = generateResponse(result)
return JSONResponse(content=response_data)
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
contents = await file.read()
img = Image.open(io.BytesIO(contents))
result = new2.handler(img)
response_data = generateResponse(result)
return JSONResponse(content=response_data)
except Exception as e:
raise HTTPException(status_code=422, detail=str(e))
@app.post("/insights")
async def insights(insight_data: dict):
text = insight_data.get("insight_text", "")
prompt=f'''
You are a medical AI under professional medical use, tasked with assisting a medical professional by analyzing the following X-ray description and providing the possible medical conditions.
Desciption: "{text}. Specify the possible conditions"
Generate **no more than four** concise insights:
1. Summarize key findings from the X-ray.
2. Specify the potential medical conditions based on the findings.
3. Recommend further diagnostic steps or medical evaluations where necessary.
'''
# insight_response = f"Insight based on the text: {text}"
genai.configure(api_key="AIzaSyCs3jLFvt_cGWwQg1n1P9zxcpHKUK8xmik")
model = genai.GenerativeModel(model_name="gemini-1.5-flash")
insight_response = model.generate_content(text)
generatedResponse = insight_response.text
lines = generatedResponse.splitlines()
insight_output = "\n".join(lines[1:])
return {"insight": insight_output}
# Run the server with uvicorn
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="127.0.0.1", port=8000)