Update app.py

app.py

@@ -1,60 +1,60 @@
-# app.py
-import gradio as gr
-from transformers import pipeline, set_seed
-import torch
-
-# Load generator (will download GPT-2 the first time)
-device = 0 if torch.cuda.is_available() else -1
-generator = pipeline("text-generation", model="gpt2", device=device)
-set_seed(42)
-
-def generate_blog(title, keywords, max_length, temperature, num_return_sequences):
-    # Build a short prompt for the model
-    prompt = f"Blog Title: {title}\nKeywords: {keywords}\n\nWrite a clear, friendly blog post about the above:"
-    # Generate text
-    outputs = generator(
-        prompt,
-        max_length=max_length,
-        temperature=temperature,
-        do_sample=True,
-        top_k=50,
-        top_p=0.95,
-        num_return_sequences=num_return_sequences,
-    )
-
-    # Extract generated text and remove the prompt prefix
-    posts = []
-    for out in outputs:
-        text = out["generated_text"]
-        # remove prompt part if present
-        if text.startswith(prompt):
-            text = text[len(prompt):].strip()
-        posts.append(text.strip())
-
-    # If only one sequence requested, return string; otherwise return joined with separators
-    if len(posts) == 1:
-        return posts[0]
-    return "\n\n-----\n\n".join(posts)
-
-# Build a minimal Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Simple GPT-2 Blog Generator")
-    with gr.Row():
-        title_input = gr.Textbox(label="Blog Title", placeholder="Enter your blog title...", lines=1)
-        keywords_input = gr.Textbox(label="Keywords / Short brief", placeholder="e.g., sustainable travel, packing tips", lines=1)
-    with gr.Row():
-        max_len = gr.Slider(label="Max tokens (approx.)", minimum=50, maximum=800, step=10, value=250)
-        temp = gr.Slider(label="Temperature (creativity)", minimum=0.1, maximum=1.5, step=0.1, value=0.8)
-    with gr.Row():
-        nseq = gr.Slider(label="Number of outputs", minimum=1, maximum=3, step=1, value=1)
-    generate_btn = gr.Button("Generate Blog Post")
-    output = gr.Textbox(label="Generated Blog Post", lines=20)
-
-    generate_btn.click(
-        fn=generate_blog,
-        inputs=[title_input, keywords_input, max_len, temp, nseq],
-        outputs=output,
-    )
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", share=False)
+# app.py
+import gradio as gr
+from transformers import pipeline, set_seed
+import torch
+
+# Load generator (will download GPT-2 the first time)
+device = 0 if torch.cuda.is_available() else -1
+generator = pipeline("text-generation", model="gpt2", device=device)
+set_seed(42)
+
+def generate_blog(title, keywords, max_length, temperature, num_return_sequences):
+    # Build a short prompt for the model
+    prompt = f"Blog Title: {title}\nKeywords: {keywords}\n\nWrite a clear, friendly blog post about the above:"
+    # Generate text
+    outputs = generator(
+        prompt,
+        max_length=max_length,
+        temperature=temperature,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=num_return_sequences,
+    )
+
+    # Extract generated text and remove the prompt prefix
+    posts = []
+    for out in outputs:
+        text = out["generated_text"]
+        # remove prompt part if present
+        if text.startswith(prompt):
+            text = text[len(prompt):].strip()
+        posts.append(text.strip())
+
+    # If only one sequence requested, return string; otherwise return joined with separators
+    if len(posts) == 1:
+        return posts[0]
+    return "\n\n-----\n\n".join(posts)
+
+# Build a minimal Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Simple GPT-2 Blog Generator")
+    with gr.Row():
+        title_input = gr.Textbox(label="Blog Title", placeholder="Enter your blog title...", lines=1)
+        keywords_input = gr.Textbox(label="Keywords / Short brief", placeholder="e.g., sustainable travel, packing tips", lines=1)
+    with gr.Row():
+        max_len = gr.Slider(label="Max tokens (approx.)", minimum=50, maximum=800, step=10, value=250)
+        temp = gr.Slider(label="Temperature (creativity)", minimum=0.1, maximum=1.5, step=0.1, value=0.8)
+    with gr.Row():
+        nseq = gr.Slider(label="Number of outputs", minimum=1, maximum=3, step=1, value=1)
+    generate_btn = gr.Button("Generate Blog Post")
+    output = gr.Textbox(label="Generated Blog Post", lines=20)
+
+    generate_btn.click(
+        fn=generate_blog,
+        inputs=[title_input, keywords_input, max_len, temp, nseq],
+        outputs=output,
+    )
+    
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", share=False)