Update app.py

app.py

@@ -3,28 +3,21 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 from dotenv import load_dotenv
 import os
+
+# Load environment variables
 load_dotenv()
 
-#tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
-#model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+# Load the model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
 model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
-#hf_token=os.getenv("HF_TOKEN")
 
-# Load the model and tokenizer
-#model_name = "openai-community/gpt2"
-#tokenizer = AutoTokenizer.from_pretrained(model_name)
-#model = AutoModelForCausalLM.from_pretrained(model_name,token=hf_token)
 
 # 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
@@ -33,10 +26,9 @@ def generate_blog(topic, keywords):
     
     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)
+    input_ids = tokenizer(prompt_template, return_tensors="pt")
+    outputs = model.generate(**input_ids)
+    blog_content = tokenizer.decode(outputs[0])
     
     return blog_content
 
@@ -53,4 +45,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch(share=True)  # Set share=True to generate a public link