Update app.py

app.py

@@ -1,7 +1,7 @@
 import requests
 import torch
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 from datetime import datetime
 
 # GPT-2 setup
@@ -10,21 +10,27 @@ 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
 
 def fetch_trending_topics(search_term="artificial intelligence OR machine learning", page=1, page_size=9):
     try:
-        url = f"https://newsapi.org/v2/everything?q={search_term}&sortBy=publishedAt&pageSize={page_size + 5}&page={page}&language=en&apiKey={news_api_key}"
+        # Fetch AI and Machine Learning related news from NewsAPI with search term
+        url = f"https://newsapi.org/v2/everything?q={search_term}&sortBy=publishedAt&pageSize={page_size + 5}&page={page}&language=en&apiKey={news_api_key}"  # Fetch extra to avoid duplicates
         response = requests.get(url)
         data = response.json()
         
+        # Check for valid response
         if response.status_code == 200 and "articles" in data:
+            # Collect articles without duplicates
             trending_topics = []
             seen_titles = set()
             for article in data["articles"]:
                 title = article["title"]
-                if title not in seen_titles:
+                if title not in seen_titles:  # Avoid duplicate titles
                     seen_titles.add(title)
                     trending_topics.append({
                         "title": title,
@@ -38,24 +44,27 @@ def fetch_trending_topics(search_term="artificial intelligence OR machine learni
                 
             return trending_topics
         else:
+            print(f"Error: {data.get('message', 'No articles found')}")
             return [{"title": "No news available", "description": "", "url": "", "publishedAt": ""}]
     except Exception as e:
+        print(f"Error fetching news: {e}")
         return [{"title": "Error fetching news", "description": "", "url": "", "publishedAt": ""}]
 
 # Analyze the trending topic using GPT-2
 def generate_analysis(trending_topic):
     input_text = f"Provide a concise analysis about the following topic: '{trending_topic['title']}'. Please summarize its significance in the AI and Machine Learning field."
 
-    # Tokenize and generate text with generation config
+    # Tokenize and generate text with a max limit on tokens
     inputs = tokenizer(input_text, return_tensors="pt").to(device)
-    generation_config = GenerationConfig(max_length=80, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95)
+    outputs = model.generate(**inputs, max_length=80, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95)
 
-    outputs = model.generate(**inputs, generation_config=generation_config)
     analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     return analysis
 
-def analyze_trends(search_term="artificial intelligence OR machine learning", page=1, page_size=9):
+# Combine both functions for Gradio
+def analyze_trends(page=1, page_size=9):
+    search_term = "artificial intelligence OR machine learning"  # Fixed search term
     trending_topics = fetch_trending_topics(search_term=search_term, page=page, page_size=page_size)
     topic_analysis = []
     
@@ -78,16 +87,20 @@ def analyze_trends(search_term="artificial intelligence OR machine learning", pa
                 "publishedAt": topic["publishedAt"],
             })
     
-    return topic_analysis[:page_size]
+    # Limit the results to the specified page size
+    return topic_analysis[:page_size]  # Ensure only the specified number of articles are returned
 
-def display_news_cards(search_term="artificial intelligence OR machine learning", page=1, page_size=9):
-    analysis_results = analyze_trends(search_term=search_term, page=page, page_size=page_size)
-    current_date = datetime.now().strftime("%d-%m-%Y")
+# Gradio UI with 3 Columns Layout for Displaying News
+def display_news_cards(page=1, page_size=9):
+    analysis_results = analyze_trends(page=page, page_size=page_size)
+    current_date = datetime.now().strftime("%d-%m-%Y")  # Format: DD-MM-YYYY
 
     display = f"### **AI & Machine Learning News for {current_date}**\n\n"
     
+    # Create a 3-column layout
     display += "<div style='display:flex; flex-wrap:wrap; justify-content:space-between;'>"
     for news_item in analysis_results:
+        # Each news box in a flex box with equal width
         display += f"""
         <div style='flex: 1 1 30%; border:1px solid black; margin:10px; padding:10px; box-sizing:border-box;'>
         <b>{news_item['title']}</b><br/>
@@ -101,20 +114,30 @@ def display_news_cards(search_term="artificial intelligence OR machine learning"
 
     return display
 
+# Gradio UI with Header, Search Option, and Submit Button
 def gradio_interface():
     with gr.Blocks() as demo:
+        # Header with background color
         gr.Markdown("""<h1 style='text-align:center; color:white; background-color:#007BFF; padding:20px; border-radius:10px;'>AI & Machine Learning News Analyzer</h1>""", elem_id="header")
 
-        search_term = gr.Textbox(label="Search for News", placeholder="Search 'AI' or 'Machine Learning'", value="artificial intelligence OR machine learning")
+        # Fixed search term displayed to the user
+        gr.Markdown("<p style='text-align:center;'>Search term: <b>artificial intelligence OR machine learning</b></p>")
+
+        # Sliders for page number and news per page
         page = gr.Slider(minimum=1, maximum=5, step=1, label="Page Number", value=1)
         page_size = gr.Slider(minimum=6, maximum=15, step=3, label="News per Page", value=9)
 
+        # Button to fetch and analyze news
         analyze_button = gr.Button("Submit")
+
+        # Output area for displaying the news
         news_output = gr.HTML()
 
-        analyze_button.click(display_news_cards, inputs=[search_term, page, page_size], outputs=news_output)
+        # Link the button click to the display function
+        analyze_button.click(display_news_cards, inputs=[page, page_size], outputs=news_output)
 
     return demo
 
+# Launch the Gradio UI
 if __name__ == "__main__":
-    gradio_interface().launch()  # Remove share=True if you don't need public links
+    gradio_interface().launch(share=True)  # Set share=True for public link