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 secrets from Hugging Face
+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 search_agent(query):
+    if not query.strip():
+        return "Please enter a valid query."
+
+    res = search.invoke(query)
+    context = "\n".join([r["content"] for r in res.get("results", [])])
+
+    prompt = f"""
+    Using the following information from web search:
+    {context}
+
+    Question: {query}
+    Answer clearly and concisely:
+    """
+
+    response = llm.invoke(prompt)
+    return response.content.strip()
+
+custom_css = """
+body {
+    background: linear-gradient(135deg, #0f2027, #203a43, #2c5364);
+}
+
+.gradio-container {
+    max-width: 900px !important;
+    margin: auto;
+}
+
+#title {
+    font-size: 36px;
+    font-weight: 700;
+    text-align: center;
+    color: white;
+}
+
+#subtitle {
+    text-align: center;
+    color: #cfd8dc;
+    margin-bottom: 20px;
+}
+
+.card {
+    background: #111827;
+    border-radius: 16px;
+    padding: 24px;
+    box-shadow: 0px 10px 30px rgba(0,0,0,0.4);
+}
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("<div id='title'>Nithi's Search Assistant</div>")
+    gr.Markdown("<div id='subtitle'>Nithi's Search Agent</div>")
+
+    with gr.Column(elem_classes="card"):
+        query_input = gr.Textbox(
+            label="Enter your question",
+            placeholder="Enter your question",
+            lines=2
+        )
+
+        search_btn = gr.Button("Search", variant="primary")
+
+        output_box = gr.Textbox(
+            label="AI Answer",
+            lines=10,
+            interactive=False
+        )
+
+    search_btn.click(
+        fn=search_agent,
+        inputs=query_input,
+        outputs=output_box
+    )
+
+demo.launch()