Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+model_name = "meta-llama/Meta-Llama-Guard-2-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate blog content
+def generate_blog(topic, keywords):
+    prompt_template = f"""
+    You are a technical content writer. Write a detailed and informative blog on the following topic.
+
+    Topic: {topic}
+
+    Keywords: {keywords}
+
+    Make sure the blog covers the following sections:
+    1. Introduction
+    2. Detailed Explanation
+    3. Examples
+    4. Conclusion
+    
+    Blog:
+    """
+
+    inputs = tokenizer(prompt_template, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(inputs.input_ids, max_length=800, num_return_sequences=1, temperature=0.7)
+    blog_content = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return blog_content
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_blog,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter the blog topic", label="Blog Topic"),
+        gr.Textbox(lines=2, placeholder="Enter keywords (comma-separated)", label="Keywords")
+    ],
+    outputs=gr.Textbox(label="Generated Blog Content"),
+    title="Technical Blog Generator",
+    description="Generate a detailed technical blog by providing a topic and relevant keywords."
+)
+
+if __name__ == "__main__":
+    iface.launch()