Create app.py

app.py

@@ -0,0 +1,48 @@
+import os
+import gradio as gr
+from google import genai
+from google.genai.types import GenerateContentConfig, GoogleSearch, Tool
+
+# Initialize GenAI Client
+API_KEY = os.getenv("GOOGLE_API_KEY")  # Ensure to set this in Hugging Face Secrets
+client = genai.Client(api_key=API_KEY)
+MODEL_ID = "gemini-2.0-flash-exp"  # Replace with your desired model ID
+
+def google_search_query(question):
+    try:
+        # Define the Google Search Tool
+        google_search_tool = Tool(google_search=GoogleSearch())
+        
+        # Generate the response
+        response = client.models.generate_content(
+            model=MODEL_ID,
+            contents=question,
+            config=GenerateContentConfig(tools=[google_search_tool]),
+        )
+
+        # Extract AI response and search results
+        ai_response = response.text  # AI response as plain text
+        search_results = response.candidates[0].grounding_metadata.search_entry_point.rendered_content
+
+        # Parse and format search results to separate links
+        link_list = "\n".join([f"<a href='{entry.url}' target='_blank'>{entry.title}</a>" for entry in response.candidates[0].grounding_metadata.search_entry_point.entries])
+        return ai_response, link_list
+    except Exception as e:
+        return f"Error: {str(e)}", "No results found."
+
+# Gradio Interface
+app = gr.Interface(
+    fn=google_search_query,
+    inputs=gr.Textbox(lines=2, label="Ask a Question"),
+    outputs=[
+        gr.Textbox(label="AI Response"),
+        gr.HTML(label="Search Results"),
+    ],
+    title="Google Search with Gemini AI",
+    description="Ask a question, and the AI will use Google search tools to provide an answer along with contextual search results.",
+    theme="huggingface",
+    layout="vertical"
+)
+
+if __name__ == "__main__":
+    app.launch(share=True)