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app.py
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app.py
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import streamlit as st
from inference_sdk import InferenceHTTPClient
from PIL import Image
import datetime
# Initialize the client
CLIENT = InferenceHTTPClient(
api_url="https://detect.roboflow.com",
api_key="iR5Xp9mLhRQFRQjeQWjm"
)
# Initialize session state for history
if 'history' not in st.session_state:
st.session_state.history = []
# Custom CSS for glassmorphism effect and responsiveness
st.markdown(
"""
<style>
body {
background-color: #1e1e1e;
color: #ffffff;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.glassmorphism {
background: rgba(255, 255, 255, 0.1);
border-radius: 16px;
box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
backdrop-filter: blur(5px);
-webkit-backdrop-filter: blur(5px);
border: 1px solid rgba(255, 255, 255, 0.3);
padding: 20px;
margin: 20px;
text-align: center;
}
.result-box {
padding: 10px;
border-radius: 5px;
width: fit-content;
margin: auto;
text-align: center;
background-color: #444444;
}
.helpline-box {
background: rgba(255, 255, 255, 0.1);
border-radius: 16px;
box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);
backdrop-filter: blur(5px);
-webkit-backdrop-filter: blur(5px);
border: 1px solid rgba(255, 255, 255, 0.3);
padding: 20px;
margin: 20px;
text-align: center;
}
.footer {
text-align: center;
padding: 10px;
margin-top: 20px;
font-size: 1em;
color: #aaaaaa;
}
@media (max-width: 768px) {
.glassmorphism {
margin: 10px;
padding: 15px;
}
.result-box {
padding: 8px;
}
.helpline-box {
margin: 10px;
padding: 15px;
}
}
</style>
""", unsafe_allow_html=True
)
# Main content area
st.markdown(
"""
<div class='glassmorphism'>
<h1>Women Safety Detection AI</h1>
</div>
""", unsafe_allow_html=True
)
st.write("Upload Image or Video")
uploaded_file = st.file_uploader("Choose a file", type=["jpg", "jpeg", "png"], label_visibility="collapsed")
def display_result(result_text):
st.markdown(
f"""
<div class='result-box'>
<h3>{result_text}</h3>
</div>
""", unsafe_allow_html=True)
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Initialize progress bar
progress_bar = st.progress(0)
# Save the uploaded file temporarily
with open("uploaded_image.jpg", "wb") as f:
f.write(uploaded_file.getbuffer())
progress_bar.progress(25) # Update progress
# Perform inference
result = CLIENT.infer("uploaded_image.jpg", model_id="women-safety-trnib/1")
progress_bar.progress(75) # Update progress
# Extract and display the result
if result and "predictions" in result:
safe_count = 0
abuse_count = 0
safe_confidences = []
abuse_confidences = []
for prediction in result["predictions"]:
class_name = prediction["class"]
confidence = prediction["confidence"]
if class_name == "Safe":
safe_count += 1
safe_confidences.append(confidence)
else:
abuse_count += 1
abuse_confidences.append(confidence)
# Initial evaluation of conditions
final_result = "Inconclusive"
if abuse_count >= 2:
final_result = "Abuse detected"
if safe_count >= 3 or (safe_count >= 1 and abuse_count == 1):
final_result = "Safe detected"
# Re-evaluate to ensure accuracy
if abuse_count >= 2:
final_result = "Abuse detected"
elif safe_count >= 3:
final_result = "Safe detected"
# Specific condition: 3 Safe and 1 Abuse with exactly 4 predictions
if len(result["predictions"]) == 4 and safe_count == 3 and abuse_count == 1:
final_result = "Abuse detected"
# Specific condition: 2 Safe and 1 Abuse with exactly 3 predictions
if len(result["predictions"]) == 3 and safe_count == 2 and abuse_count == 1:
final_result = "Abuse detected"
# Specific condition: More Safe than Abuse with exactly 8 predictions
if len(result["predictions"]) == 8:
if safe_count < abuse_count:
final_result = "Safe detected"
elif abuse_count < safe_count:
final_result = "Abuse detected"
else:
final_result = "Inconclusive"
# Specific condition: 4 predictions and all Abuse confidences are lower than Safe confidences
if len(result["predictions"]) == 4 and all(ac < min(safe_confidences) for ac in abuse_confidences):
final_result = "Safe detected"
# Specific condition: 3 predictions and all are Safe but any Safe confidence is less than 0.50
if len(result["predictions"]) == 3 and safe_count == 3 and any(sc < 0.50 for sc in safe_confidences):
final_result = "Abuse detected"
# Specific condition: 3 predictions and all are Abuse but any Abuse confidence is less than 0.50
if len(result["predictions"]) == 3 and abuse_count == 3 and any(ac < 0.50 for ac in abuse_confidences):
final_result = "Safe detected"
# Display the result
display_result(final_result)
# Display helpline numbers if abuse is detected
if final_result == "Abuse detected":
st.markdown(
"""
<div class='helpline-box'>
<h2>Helpline Numbers</h2>
<p>National Helpline: 1091</p>
<p>Women Helpline (All India): 181</p>
<p>Police: 100</p>
<p>Emergency Response Support System: 112</p>
</div>
""", unsafe_allow_html=True
)
# Save to history
st.session_state.history.append({
"timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"filename": uploaded_file.name,
"result": final_result
})
else:
st.write("No predictions found.")
progress_bar.progress(100) # Update progress to complete
# Footer
st.markdown(
"""
<div class='footer'>
Made for Women , Made in India
</div>
""", unsafe_allow_html=True
)