Update app.py

app.py

@@ -10,9 +10,6 @@ model_name = "gpt2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
 
-# Set pad_token_id to avoid errors
-model.config.pad_token_id = model.config.eos_token_id
-
 # NewsAPI Setup (Replace with your own API key)
 news_api_key = "35cbd14c45184a109fc2bbb5fff7fb1b"  # Replace with your NewsAPI key
 
@@ -56,17 +53,7 @@ def generate_analysis(trending_topic):
 
     # Tokenize and generate text with a max limit on tokens
     inputs = tokenizer(input_text, return_tensors="pt").to(device)
-
-    # Create a generation configuration
-    generation_config = {
-        "max_length": 80,
-        "num_return_sequences": 1,
-        "do_sample": True,
-        "top_k": 50,
-        "top_p": 0.95,
-    }
-
-    outputs = model.generate(**inputs, **generation_config)
+    outputs = model.generate(**inputs, max_length=80, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95)
 
     analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
@@ -102,6 +89,10 @@ def analyze_trends(page=1, page_size=9):
 
 # Gradio UI with 3 Columns Layout for Displaying News
 def display_news_cards(page=1, page_size=9):
+    # Show loading message first
+    loading_message = "Please wait while we fetch the latest news..."
+    
+    # Fetch analysis results
     analysis_results = analyze_trends(page=page, page_size=page_size)
     current_date = datetime.now().strftime("%d-%m-%Y")  # Format: DD-MM-YYYY
 
@@ -150,4 +141,4 @@ def gradio_interface():
 
 # Launch the Gradio UI
 if __name__ == "__main__":
-    gradio_interface().launch()  # Removed share=True since it is not supported
+    gradio_interface().launch(share=True)  # Launch with share=True to allow public access