File size: 1,041 Bytes
ce10f9a
5b4c169
a3ae69c
4ffc5f1
ce10f9a
 
07fca4f
d43b4cf
ce10f9a
604d57b
a06cfd4
 
47f2ac0
 
49c5437
 
47f2ac0
962bd20
49c5437
 
 
 
 
 
 
 
 
 
1917b0b
 
ce8a810
1917b0b
ce8a810
 
1917b0b
a3ae69c
94461c6
a3ae69c
 
 
ce10f9a
a3ae69c
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
import os 
import requests
import gradio as gr 

api_token = os.environ.get("TOKEN")

API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
headers = {"Authorization": f"Bearer {api_token}"}

def query(payload):
        response = requests.post(API_URL, headers=headers, json=payload)
        return response.json()




def analyze_sentiment(text):
    output = query({
    "inputs":   f'''<|begin_of_text|>
    <|start_header_id|>system<|end_header_id|>
    You'll only answer in English.
    <|eot_id|>
    <|start_header_id|>user<|end_header_id|>
    {text}
    <|eot_id|>
    <|start_header_id|>assistant<|end_header_id|>
    '''
})

    # Assurez-vous de gérer correctement la sortie de l'API
    if isinstance(output, list) and len(output) > 0:
        return output[0].get('generated_text', 'Erreur: Réponse inattendue')
    else:
        return "Erreur: Réponse inattendue de l'API"

demo = gr.Interface(
    fn = analyze_sentiment,
    inputs=["text"],
    outputs=["text"],
)

demo.launch()