Update app.py

app.py

@@ -1,17 +1,27 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Initialize the text generation pipeline
-generator = pipeline('text-generation', model='gpt2')
+model_name = 'openai-community/gpt2-large'
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
 
-def generate_blogpost(topic, max_length=500):
+def generate_blogpost(topic, max_length=500, temperature=0.7):
     prompt = f"Write a blog post about {topic}:\n\n"
-    
-    # Generate the blog post
-    generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)[0]['generated_text']
+
+    # Encode input:
+    inputs_encoded = tokenizer(prompt, return_tensors='pt')
+    # Model Output:
+    model_output = model.generate(
+        inputs_encoded["input_ids"],
+        max_new_tokens=max_length,
+        do_sample=True,
+        temperature=temperature
+    )[0]
+    # Decode the output
+    output = tokenizer.decode(model_output, skip_special_tokens=True)
     
     # Remove the prompt from the generated text
-    blog_post = generated_text[len(prompt):].strip()
+    blog_post = output[len(prompt):].strip()
     
     return blog_post
 
@@ -20,7 +30,8 @@ iface = gr.Interface(
     fn=generate_blogpost,
     inputs=[
         gr.Textbox(lines=1, placeholder="Enter the blog post topic here..."),
-        gr.Slider(minimum=100, maximum=1000, step=50, label="Max Length", value=500)
+        gr.Slider(minimum=100, maximum=1000, step=50, label="Max Length", value=500),
+        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Temperature", value=0.7)
     ],
     outputs="text",
     title="Blog Post Generator",
@@ -28,4 +39,4 @@ iface = gr.Interface(
 )
 
 # Launch the app
-iface.launch()
+iface.launch()