added first version

app.py

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+import requests
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from datetime import datetime
+
+# GPT-2 setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_name = "gpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+# 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:
+        # 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:  # Avoid duplicate titles
+                    seen_titles.add(title)
+                    trending_topics.append({
+                        "title": title,
+                        "description": article["description"] if article["description"] else "No description available.",
+                        "url": article["url"],
+                        "publishedAt": article["publishedAt"],
+                    })
+
+            if not trending_topics:
+                return [{"title": "No news available", "description": "", "url": "", "publishedAt": ""}]
+                
+            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 a max limit on tokens
+    inputs = tokenizer(input_text, return_tensors="pt").to(device)
+    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)
+
+    return analysis
+
+# 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 = []
+    
+    for topic in trending_topics:
+        if topic["title"] not in ["Error fetching news", "No news available"]:
+            analysis = generate_analysis(topic)
+            topic_analysis.append({
+                "title": topic["title"],
+                "description": topic["description"],
+                "analysis": analysis,
+                "url": topic["url"],
+                "publishedAt": topic["publishedAt"],
+            })
+        else:
+            topic_analysis.append({
+                "title": topic["title"],
+                "description": topic["description"],
+                "analysis": "Unable to retrieve or analyze data.",
+                "url": topic["url"],
+                "publishedAt": topic["publishedAt"],
+            })
+    
+    # Limit the results to the specified page size
+    return topic_analysis[:page_size]  # Ensure only the specified number of articles are returned
+
+# 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/>
+        <i>{news_item['publishedAt']}</i><br/><br/>
+        {news_item['description']}<br/><br/>
+        <a href='{news_item['url']}' target='_blank'>Read more</a><br/><br/>
+        <b>Analysis:</b> {news_item['analysis']}<br/><br/>
+        </div>
+        """
+    display += "</div>"
+
+    return display
+
+# Gradio UI with Header, Search Option, and Submit Button
+def gradio_interface():
+    with gr.Blocks() as demo:
+        # Header with background colour
+        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")
+
+        # 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()
+
+        # 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()