Create app.py

app.py

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+import os
+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain_tavily import TavilySearch
+
+# Load API keys from Hugging Face Secrets
+groq_api_key = os.environ.get("GROQ_API_KEY")
+tavily_api_key = os.environ.get("TAVILY_API_KEY")
+
+# Initialize LLM
+llm = ChatGroq(
+    model_name="openai/gpt-oss-120b",
+    temperature=0,
+    groq_api_key=groq_api_key
+)
+
+# Initialize Search
+search = TavilySearch(
+    max_results=5,
+    tavily_api_key=tavily_api_key
+)
+
+def google_style_search(query):
+    if not query.strip():
+        return "Please enter a search query."
+
+    res = search.invoke(query)
+    context = "\n".join([r["content"] for r in res.get("results", [])])
+
+    prompt = f"""
+    Using the following web search information:
+    {context}
+
+    Question: {query}
+    Answer in a clear, user-friendly manner:
+    """
+
+    response = llm.invoke(prompt)
+    return response.content.strip()
+
+google_css = """
+body {
+    background-color: #ffffff;
+    font-family: Arial, sans-serif;
+}
+
+.gradio-container {
+    max-width: 800px !important;
+    margin-top: 60px;
+}
+
+#logo {
+    font-size: 48px;
+    font-weight: 700;
+    text-align: center;
+    margin-bottom: 30px;
+}
+
+#logo span:nth-child(1) { color: #4285F4; }
+#logo span:nth-child(2) { color: #DB4437; }
+#logo span:nth-child(3) { color: #F4B400; }
+#logo span:nth-child(4) { color: #4285F4; }
+#logo span:nth-child(5) { color: #0F9D58; }
+#logo span:nth-child(6) { color: #DB4437; }
+"""
+
+with gr.Blocks(css=google_css) as demo:
+    gr.Markdown("""
+    <div id="logo">
+        <span>G</span><span>o</span><span>o</span><span>g</span><span>l</span><span>e</span>
+    </div>
+    """)
+
+    query_input = gr.Textbox(
+        placeholder="Search anything...",
+        lines=1,
+        show_label=False
+    )
+
+    search_button = gr.Button("Search")
+
+    answer_output = gr.Textbox(
+        label="Search Result",
+        lines=12,
+        interactive=False
+    )
+
+    search_button.click(
+        fn=google_style_search,
+        inputs=query_input,
+        outputs=answer_output
+    )
+
+demo.launch()