Update app.py

app.py

@@ -1,64 +1,93 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import streamlit as st
+import requests
+import os
+import json
+import boto3
+from dotenv import load_dotenv
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# API Key (Ensure security; do not share publicly)
+# API_KEY = "67bd857847938bebf6b0ff82a26ba45f251986f3"
 
+# Define the API endpoint
+# API_URL = "https://google.serper.dev/search"
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Load API keys from .env file
+load_dotenv()
+NEWS_API_KEY = "3d542da6aaa94a79ad7d2b852e944dac"
+SERPER_API_KEY = "67bd857847938bebf6b0ff82a26ba45f251986f3"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Claude 3 via AWS Bedrock
+bedrock = boto3.client("bedrock-runtime", region_name=os.getenv("AWS_REGION"))
 
-    messages.append({"role": "user", "content": message})
+# Streamlit app UI
+st.title("📰 News Summarizer Agent")
+topic = st.text_input("Enter a topic, company name, or keyword:")
 
-    response = ""
+# Fetch news from NewsAPI
+def fetch_newsapi_news(topic):
+    url = f"https://newsapi.org/v2/everything?q={topic}&pageSize=5&sortBy=publishedAt&apiKey={NEWS_API_KEY}"
+    response = requests.get(url)
+    articles = response.json().get("articles", [])
+    return [{"title": a["title"], "description": a["description"], "url": a["url"]} for a in articles]
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Fetch news from Serper.dev
+def fetch_serper_news(topic):
+    url = "https://google.serper.dev/news"
+    headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
+    data = {"q": topic}
+    response = requests.post(url, headers=headers, data=json.dumps(data))
+    results = response.json().get("news", [])[:5]
+    return [{"title": r["title"], "description": r.get("snippet", ""), "url": r["link"]} for r in results]
 
-        response += token
-        yield response
+# Summarize with Claude 3
+def summarize_with_claude(article):
+    messages = [
+        {
+            "role": "user",
+            "content": f"""You are a helpful news summarizer agent.
+Given the article title and description, generate:
+1. A more relevant headline based on the topic
+2. A 3-5 line clear and concise summary.
 
+Title: {article['title']}
+Description: {article['description']}
 
+Respond as:
+Headline: ...
+Summary: ...
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+        }
+    ]
+
+    body = {
+        "messages": messages,
+        "max_tokens": 300,
+        "temperature": 0.7,
+        "anthropic_version": "bedrock-2023-05-31"  # REQUIRED
+    }
+
+    response = bedrock.invoke_model(
+        modelId="anthropic.claude-3-sonnet-20240229-v1:0",
+        body=json.dumps(body),
+        contentType="application/json",
+        accept="application/json"
+    )
+
+    result = json.loads(response["body"].read())
+    return result["content"][0]["text"].strip()
+
+# Button and main logic
+if st.button("Get News"):
+    if not topic:
+        st.warning("Please enter a topic.")
+    else:
+        st.subheader("Fetching top news...")
+        news_items = fetch_newsapi_news(topic) + fetch_serper_news(topic)
+
+        for idx, article in enumerate(news_items[:5]):
+            st.markdown(f"### 🗞️ News #{idx+1}")
+            st.markdown(f"[Original Article]({article['url']})")
+
+            with st.spinner("Generating summary..."):
+                summary = summarize_with_claude(article)
+                st.markdown(summary)