Create app.py

app.py

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+import cv2
+import gradio as gr
+import google.generativeai as genai
+import os
+import PIL.Image
+
+# Configure the API key for Google Generative AI
+genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))
+
+# Define the Generative AI model
+model = genai.GenerativeModel('gemini-1.5-flash')
+
+# Function to capture frames from a video
+def frame_capture(video_path, num_frames=5):
+    vidObj = cv2.VideoCapture(video_path)
+    frames = []
+    total_frames = int(vidObj.get(cv2.CAP_PROP_FRAME_COUNT))
+    frame_step = max(1, total_frames // num_frames)
+    count = 0
+    
+    while len(frames) < num_frames:
+        vidObj.set(cv2.CAP_PROP_POS_FRAMES, count)
+        success, image = vidObj.read()
+        if not success:
+            break
+        frames.append(image)
+        count += frame_step
+    
+    vidObj.release()
+    return frames
+
+# Function to generate text descriptions for frames
+def generate_descriptions_for_frames(video_path):
+    frames = frame_capture(video_path)
+    images = [PIL.Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in frames]
+    
+    prompt = "Describe what is happening in each of these frames."
+    images_with_prompt = [prompt] + images
+    
+    responses = model.generate_content(images_with_prompt)
+    descriptions = [response.text for response in responses]
+    
+    formatted_description = format_descriptions(descriptions)
+    return formatted_description
+
+# Helper function to format descriptions
+def format_descriptions(descriptions):
+    return ' '.join(descriptions).strip()
+
+# Define Gradio interface
+video_input = gr.Video(label="Upload or Record Video", source="upload", type="filepath")
+output_text = gr.Textbox(label="Video Analysis")
+
+# Create Gradio app
+gr.Interface(fn=generate_descriptions_for_frames, inputs=video_input, outputs=output_text, title="Video Content Detection System").launch()