Update app.py

app.py

@@ -10,6 +10,7 @@ from openai import OpenAI
 # Load AI models
 caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+caption_model = torch.quantization.quantize_dynamic(caption_model, {torch.nn.Linear}, dtype=torch.qint8)
 client = OpenAI(
   api_key="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"
 )
@@ -17,6 +18,17 @@ client = OpenAI(
 
 # Streamlit App UI
 st.title("🎥 AI-Powered Video Summarization")
+st.markdown(
+    """
+    <style>
+    .stApp {
+        background-image: url("https://www.canva.com/templates/EAFt1y1Ahb8-yellow-gradient-color-and-style-mobile-video-background/");
+        background-size: cover;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
 
 uploaded_file = st.file_uploader("📤 Upload a Video File", type=["mp4"])
 
@@ -67,7 +79,7 @@ if uploaded_file:
             image_path = os.path.join("frames", filename)
             captions.append(generate_caption(image_path))
 
-    st.write("📄 Generated Captions:", captions)
+    # st.write("📄 Generated Captions:", captions)
 
     # Summarization
     st.write("📖 Summarizing captions using AI...")