Upload app.py

app.py

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+# Importation  
+
+%matplotlib inline 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn import metrics
+import torch
+from torch.utils.data import Dataset, DataLoader
+from transformers import AutoModel, AutoTokenizer
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+import gradio as gr
+from gradio.components import Label
+
+path = "./weights"
+model = AutoModel.from_pretrained(path, trust_remote_code=True)
+class CamembertClass(torch.nn.Module):
+    def __init__(self):
+        super(CamembertClass, self).__init__()
+        self.l1 = model
+        self.dropout = torch.nn.Dropout(0.1)
+        self.pre_classifier = torch.nn.Linear(1024, 1024)
+        self.classifier = torch.nn.Linear(1024, 3)
+
+    def forward(self, input_ids, attention_mask, token_type_ids):
+        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+        hidden_state = output_1[0]
+        pooler = hidden_state[:, 0]
+        pooler = self.pre_classifier(pooler)
+        pooler = torch.nn.ReLU()(pooler)
+        pooler = self.dropout(pooler)
+        output = self.classifier(pooler)
+        return output
+    
+    #model_gradio = CamembertClass()
+path = "./pytorch_model.bin"
+model = torch.load(path, map_location="cpu")
+path_tokenizer = "./"
+tokenizer = AutoTokenizer.from_pretrained(path_tokenizer)
+
+model.eval()  # Mettez votre modèle en mode évaluation
+
+# Fonction d'inférence pour Gradio
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    
+    # Extract necessary inputs for the model
+    input_ids = inputs['input_ids']
+    attention_mask = inputs['attention_mask']
+    token_type_ids = inputs.get('token_type_ids', None)  # Some models do not use segment IDs
+    
+    # Make prediction
+    with torch.no_grad():
+    # Directly use outputs if your model returns logits directly
+        logits = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+
+ 
+    # Convert logits to probabilities
+    probabilities = torch.softmax(logits, dim=1).detach().cpu().numpy()[0]
+    # Replace the following with your actual classes
+    classes = ['Negative Sentiment', 'Positive Sentiment']
+    return {classes[i]: float(probabilities[i]) for i in range(len(classes))}
+
+# Création de l'interface Gradio
+iface = gr.Interface(fn=predict,
+                     inputs=gr.components.Textbox(placeholder="Enter your text here..."),
+                     outputs=gr.components.Label(num_top_classes=2))
+iface.launch(share=True)