Create app.py

app.py

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+import os
+import gradio as gr
+from google import genai
+from google.genai import types
+from google.genai.types import Part
+from tenacity import retry, stop_after_attempt, wait_random_exponential
+
+# Retrieve API key from environment variable
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+if not GOOGLE_API_KEY:
+    raise ValueError("Please set the GOOGLE_API_KEY environment variable.")
+
+# Initialize the Gemini API client via AI Studio
+client = genai.Client(api_key=GOOGLE_API_KEY)
+
+# Use the Gemini 2.0 Flash model as required
+MODEL_NAME = "gemini-2.0-flash-001"
+
+@retry(wait=wait_random_exponential(multiplier=1, max=60), stop=stop_after_attempt(3))
+def call_gemini(video_url: str, prompt: str) -> str:
+    """
+    Call the Gemini model with the provided video URL and prompt.
+    The video is referenced by its URI (expecting a publicly accessible URL) and passed as a Part.
+    """
+    response = client.models.generate_content(
+        model=MODEL_NAME,
+        contents=[
+            Part.from_uri(file_uri=video_url, mime_type="video/webm"),
+            prompt,
+        ],
+    )
+    return response.text
+
+def analyze_video(video_url: str) -> str:
+    """
+    Perform iterative, agentic video analysis.
+    In each iteration, the Gemini model refines its analysis based on previous output.
+    """
+    analysis = ""
+    num_iterations = 3
+
+    for i in range(num_iterations):
+        if i == 0:
+            prompt = (
+                "You are a video analysis agent focusing on security and surveillance. "
+                "Provide a detailed summary of the video, highlighting any key events, suspicious activities, or anomalies."
+            )
+        else:
+            prompt = (
+                f"Based on the previous analysis: \"{analysis}\". "
+                "Provide further elaboration and refined insights, focusing on potential security threats, anomalous events, "
+                "and any details that may help a security team understand the situation better."
+            )
+        try:
+            analysis = call_gemini(video_url, prompt)
+        except Exception as e:
+            analysis += f"\n[Error during iteration {i+1}: {e}]"
+            break  # Exit if an error occurs
+    return analysis
+
+def gradio_interface(video_url: str) -> str:
+    """
+    Gradio interface function that takes a video URL and returns the analysis.
+    """
+    if not video_url:
+        return "Please provide a valid video URL."
+    return analyze_video(video_url)
+
+# Define and launch the Gradio interface
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(label="Video URL (publicly accessible, e.g., YouTube link)"),
+    outputs=gr.Textbox(label="Security & Surveillance Analysis"),
+    title="AI Video Analysis and Summariser Agent",
+    description=(
+        "This agentic video analysis tool uses Google's Gemini 2.0 Flash model via AI Studio "
+        "to iteratively analyze a video for security and surveillance insights. It makes repeated "
+        "LLM calls to refine its analysis of the video content."
+    )
+)
+
+if __name__ == "__main__":
+    iface.launch()