Update app.py

app.py

@@ -1,24 +1,21 @@
-from torch import nn
+import gradio as gr
+from transformers import pipeline
 
-class Generator(nn.Module):
-    # Refer to the link below for explanations about nc, nz, and ngf
-    # https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html#inputs
-    def __init__(self, nc=4, nz=100, ngf=64):
-        super(Generator, self).__init__()
-        self.network = nn.Sequential(
-            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),
-            nn.BatchNorm2d(ngf * 4),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),
-            nn.BatchNorm2d(ngf * 2),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),
-            nn.BatchNorm2d(ngf),
-            nn.ReLU(True),
-            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
-            nn.Tanh(),
-        )
+# Load GPT-2 model
+generator = pipeline('text-generation', model='gpt2')
 
-    def forward(self, input):
-        output = self.network(input)
-        return output
+# Define function to generate text using GPT-2
+def generate_text(prompt):
+    return generator(prompt, max_length=50, do_sample=True)[0]['generated_text']
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=generate_text,
+    inputs="text",
+    outputs="text",
+    title="Text Generator AI",
+    description="Generate text using GPT-2 from Hugging Face"
+)
+
+# Launch the interface
+interface.launch()