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File size: 1,898 Bytes
96993d4 | 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 | import gradio as gr
import requests
API_URL = "http://127.0.0.1:8000/api/predict"
def analyze_text(text):
try:
response = requests.post(API_URL, json={"text": text})
if response.status_code == 200:
result = response.json()
sentiment = result.get("sentiment", "")
emotion = result.get("emotion", "")
return f"➡️ **Sentiment:** {sentiment}\n\n ➡️ **Emotion:** {emotion}"
else:
return f"API Error: {response.status_code}"
except Exception as e:
return f"Error connecting to API: {str(e)}"
custom_css = """
.gradio-container {
background: linear-gradient(135deg, #e9f1fc 0%, #fefefe 100%);
font-family: 'Segoe UI', Roboto, sans-serif;
}
h1 {
text-align: center !important;
font-size: 2.2rem !important;
color: #1f2937 !important;
font-weight: 600 !important;
}
textarea, .output_text {
font-size: 1.1rem !important;
line-height: 1.6 !important;
}
.output_text {
background: #f9fafb !important;
border-radius: 12px !important;
padding: 16px !important;
border: 1px solid #e5e7eb !important;
}
"""
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
gr.Markdown("# 💬 Emotion & Sentiment Analyzer")
gr.Markdown("### Type your text below to discover its emotional tone and sentiment ✨")
with gr.Row():
text_input = gr.Textbox(
label="Enter text here",
placeholder="e.g. I'm so excited to work on this project!",
lines=4,
scale=2
)
with gr.Row():
output_box = gr.Markdown(label="Results", elem_classes="output_text")
analyze_button = gr.Button("🔍 Analyze", variant="primary")
analyze_button.click(fn=analyze_text, inputs=text_input, outputs=output_box)
demo.launch()
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