app

app.py

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+import streamlit as st
+import requests
+from datetime import datetime
+from newspaper import Article
+from groq import Groq
+from diffusers import DiffusionPipeline
+from IPython.display import display
+
+# Your News API key and Groq API setup
+API_KEY = '446dc1fa183e4e859a7fb0daf64a6f2c'
+BASE_URL = 'https://newsapi.org/v2/everything'
+client = Groq(api_key="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")
+
+# Function to fetch news based on topic
+def get_news_by_topic(topic):
+    params = {
+        'q': topic,
+        'apiKey': API_KEY,
+        'language': 'en',
+        'sortBy': 'publishedAt',
+        'pageSize': 5
+    }
+
+    response = requests.get(BASE_URL, params=params)
+    news_list = []
+
+    if response.status_code == 200:
+        data = response.json()
+
+        if 'articles' in data:
+            for article in data['articles']:
+                title = article['title']
+                description = article['description']
+                published_at = article['publishedAt']
+                content = article.get('content', 'No full content available.')
+                url = article['url']
+
+                published_at = datetime.strptime(published_at, '%Y-%m-%dT%H:%M:%SZ')
+                formatted_time = published_at.strftime('%Y-%m-%d %H:%M:%S')
+
+                article_data = {
+                    'title': title,
+                    'description': description,
+                    'publishedAt': formatted_time,
+                    'content': content,
+                    'url': url
+                }
+
+                news_list.append(article_data)
+
+    return news_list
+
+# Function to fetch full article using Newspaper
+def fetch_full_article_with_newspaper(url):
+    try:
+        article = Article(url)
+        article.download()
+        article.parse()
+        return article.text
+    except Exception as e:
+        return f"Error occurred during parsing: {str(e)}"
+
+# Function to summarize an article using Groq's Llama 3 model
+def summarize_article(client, article_content, tone):
+    prompt = f"""
+    You are a professional News Summarizer.
+    Your task is to summarize the provided news article while retaining all key details.
+    Adjust the tone and style of the summary based on the user input (e.g., "formal," "conversational," or "humorous").
+    
+    # News Article:
+    {article_content}
+    
+    # Tone/Style:
+    {tone}
+    
+    Remove unwanted sentences in summary like "article not found" or anything unrelated to the user query.
+    """
+    
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3.1-8b-instant"
+        )
+
+        summary = chat_completion.choices[0].message.content.strip()
+        return summary
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# Function to generate social media post content
+def generate_social_media_post(summary, tone):
+    prompt = f"""
+    You are a professional social media content creator.
+    Your task is to create an engaging text post based on the provided news article summary while retaining all key details.
+    Ensure the tone matches the specified style provided.
+
+    News Article:
+    {summary}
+    
+    Provide the text post below:
+    """
+    
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3.1-8b-instant"
+        )
+        
+        social_media_post = chat_completion.choices[0].message.content.strip()
+        return social_media_post
+    except Exception as e:
+        return f"Error occurred while generating the post: {e}"
+
+# Generate an image from news summary description using Stable Diffusion
+def generate_image_from_description(description):
+    # Load the DiffusionPipeline for Stable Diffusion from the diffusers library
+    generator = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+    generator.to("cuda")
+
+    # Generate the image
+    image = generator(description).images[0]
+    return image
+
+# Streamlit UI
+def main():
+    st.title("News Summarizer and Social Media Post Generator")
+    st.subheader("Generate a social media post based on the latest news summary")
+
+    # Input fields for topic and tone
+    topic = st.text_input("Enter the topic you want news for:")
+    tone = st.selectbox("Select the tone of the summary:", ["formal", "conversational", "humorous"])
+
+    if st.button("Generate Social Media Post"):
+        if topic:
+            st.write(f"Fetching news about: {topic} in {tone} tone...")
+
+            # Fetch the latest news based on the topic
+            news_data = get_news_by_topic(topic)
+
+            if news_data:
+                combined_content = ""
+                for article in news_data:
+                    article_content = fetch_full_article_with_newspaper(article['url'])
+                    summary = summarize_article(client, article_content, tone)
+                    combined_content += summary
+                
+                # Generate the enhanced description for image generation
+                enhanced_prompt = f"""
+                You are a professional artist. Given the following news summary, create a detailed and vivid description that can be used to generate an image:
+                {combined_content}
+
+                The description should capture the mood, setting, actions, and emotions in a way that a model can visually interpret. Include details such as time of day, character appearance, atmosphere, and background elements.
+                """
+
+                chat_completion = client.chat.completions.create(
+                    messages=[{"role": "user", "content": enhanced_prompt}],
+                    model="llama3-8b-8192"
+                )
+
+                enhanced_description = chat_completion.choices[0].message.content
+                st.write("### Enhanced Description for Image Generation:")
+                st.write(enhanced_description)
+
+                # Generate an image based on the enhanced description
+                image = generate_image_from_description(enhanced_description)
+                
+                # Display the generated image
+                st.image(image, caption="Generated Image based on News Summary")
+
+                # Generate a social media post based on the summary
+                social_media_post = generate_social_media_post(combined_content, tone)
+
+                st.write("### Generated Social Media Post:")
+                st.write(social_media_post)
+
+                # Allow user to download the post as a text file
+                post_filename = f"news_summary_{topic.replace(' ', '_')}.txt"
+                st.download_button("Download Post as Text File", data=social_media_post, file_name=post_filename)
+
+            else:
+                st.write("No news articles found for this topic.")
+        else:
+            st.write("Please enter a topic to search for news.")
+
+if __name__ == "__main__":
+    main()