Update app2.py

app2.py

@@ -1,80 +1,90 @@
-YOUR_API_KEY= "AIzaSyBjb6LLerzZE6JIIE0YBK6Wn0hqdO9E1Zk"
 import cv2
 import gradio as gr
-import hashlib
-import google.generativeai as genai
-
-genai.configure(api_key="YOUR_API_KEY")
-
-# Set up the model
-generation_config = {
-    "temperature": 0.4,
-    "top_p": 1,
-    "top_k": 32,
-    "max_output_tokens": 4096,
-}
-
-safety_settings = [
-    {
-        "category": "HARM_CATEGORY_HARASSMENT",
-        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-    },
-    {
-        "category": "HARM_CATEGORY_HATE_SPEECH",
-        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-    },
-    {
-        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
-        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-    },
-    {
-        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
-        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
-    },
-]
-
-model = genai.GenerativeModel(model_name="gemini-1.0-pro-vision-latest",
-                              generation_config=generation_config,
-                              safety_settings=safety_settings)
-
-uploaded_files = []
-
-# Function to upload image to Gemini Pro-Vision
-def upload_image_for_description(image):
-    image_encoded = cv2.imencode('.jpg', image)[1]
-    hash_id = hashlib.sha256(image_encoded).hexdigest()
-    uploaded_file = genai.upload_file(data=image_encoded.tobytes(), display_name=hash_id)
-    uploaded_files.append(uploaded_file)
-    return uploaded_file
 
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+# get the frames
 def frame_capture(video_path):
     # Function to extract frames
     vidObj = cv2.VideoCapture(video_path)
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+    # Used as counter variable
+    count = 0
     frames = []
 
-    while True:
+    # checks whether frames were extracted
+    success = 1
+
+    while success:
+        # vidObj object calls read
+        # function to extract frames
         success, image = vidObj.read()
-        if not success:
-            break
-        frames.append(image)
 
+        # Append the frame to the list
+        if success:
+            frames.append(image)
+            count += 1
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+    return frames
+
+def extract_frames(video):
+    frames = frame_capture(video)
     return frames
 
-def extract_frames_and_describe(video_path):
-    frames = frame_capture(video_path)
-    descriptions = []
-    for frame in frames:
-        uploaded_file = upload_image_for_description(frame)
-        try:
-            description = model.generate_content([uploaded_file, "describe this image"]).prompt[-1].text
-            descriptions.append(description)
-        except Exception as e:
-            print("Error:", e)
-    return descriptions
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 # Define the Gradio interface
 video_input = gr.Video(label="Upload Video", autoplay=True)
-output_text = gr.outputs.Textbox(label='Description')
+output_frames = gr.Gallery(label='Frame')
 
 # Create the Gradio app
-gr.Interface(fn=extract_frames_and_describe, inputs=video_input, outputs=output_text, title='Video Frame Describer').launch()
+gr.Interface(fn=extract_frames, inputs=video_input, outputs=output_frames, title='Video Frame Extractor').launch()