File size: 1,171 Bytes
3c3ff47
65f5aa2
3c3ff47
d720951
65f5aa2
3c3ff47
707c991
aa8764e
d720951
65f5aa2
707c991
aa8764e
 
65f5aa2
 
aa8764e
65f5aa2
 
3c3ff47
65f5aa2
 
3c3ff47
707c991
65f5aa2
3c3ff47
707c991
 
 
d720951
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
import gradio as gr
from transformers import pipeline

# Load sentiment analysis pipeline
sentiment_analysis = pipeline("sentiment-analysis", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")

# Function to analyze user's mood based on input
    def analyze_mood(user_input):
    # Analyze the mood from input text
    result = sentiment_analysis(user_input)[0]
    # Define mood and suggestion based on sentiment analysis
    score = result["score"]
    if result["label"] == "POSITIVE" and score > 0.85:
        mood = "Happy"
        suggestion = "Keep doing what you're doing! 😊"
    elif result["label"] == "NEGATIVE" and score > 0.85:
        mood = "Sad"
        suggestion = "Try to talk to someone, or take a break πŸ’‘"
    else:
        mood = "Neutral"
        suggestion = "You're doing okay! Stay calm 🌸"
    
    # Return mood and suggestion
    return "Your mood is: " + mood, suggestion

inputs = gr.Textbox(label="How are you feeling today?", placeholder="Type your thoughts here...")
outputs = gr.Textbox(label="Mood and Suggestion")

gr.Interface(fn=analyze_mood, inputs=inputs, outputs=outputs, title="Mood Analyzer").launch()