Update app.py

app.py

@@ -8,46 +8,42 @@ import os
 import logging
 from datetime import datetime
 
-# --- Configuration ---
-# These paths are relative to the root of your Hugging Face Space repo
-MODEL_PATH = 'model/model.h5' 
-OUTPUT_DIR = 'output/'
-os.makedirs(OUTPUT_DIR, exist_ok=True) # Create output dir if it doesn't exist
-
-# --- Setup Logging ---
-# This will help you debug issues in the Hugging Face logs
+# --- IMPORTANT CHANGE: No folders needed ---
+# The code now assumes 'model.h5' is in the same root directory as this app.py file.
+MODEL_PATH = 'model.h5' 
+
+# --- Setup Basic Logging ---
+# This will print helpful info to the Hugging Face logs for debugging.
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # --- Load Model ---
 if not os.path.exists(MODEL_PATH):
-    error_msg = f"Model file not found at {MODEL_PATH}. Make sure you have uploaded your 'model.h5' to a 'model' directory in your Space."
+    error_msg = f"Model file not found at '{MODEL_PATH}'. Make sure you have uploaded your 'model.h5' to the root of your Space."
     logging.error(error_msg)
     raise FileNotFoundError(error_msg)
 
 model = tf.keras.models.load_model(MODEL_PATH)
 logging.info("AI model loaded successfully.")
 
-# We need to know which index corresponds to 'jumpscare'.
-# Based on your training code, LabelBinarizer sorts alphabetically.
-# "jumpscare" comes before "normal", so it's likely index 0.
-# If it's the other way, change this to 1.
+# Based on your training code, LabelBinarizer sorts class names alphabetically.
+# "jumpscare" comes before "normal", so the model's output for the "jumpscare" class
+# will be at index 0. If this is wrong, change this to 1.
 JUMPSCARE_CLASS_INDEX = 0 
-logging.info(f"Assuming 'jumpscare' is class index {JUMPSCARE_CLASS_INDEX}. If predictions are wrong, change this value.")
+logging.info(f"Using class index {JUMPSCARE_CLASS_INDEX} for 'jumpscare' probability.")
 
 
 def predict_frame_is_jumpscare(frame, threshold):
     """Analyzes a single video frame and predicts if it's a jumpscare."""
-    # Preprocess the frame for the model
+    # Preprocess the frame
     rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     resized_frame = cv2.resize(rgb_frame, (128, 128))
     img_array = np.array(resized_frame) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
     
-    # Make a prediction. This will return a list of probabilities, e.g., [[0.9, 0.1]]
+    # Get the model's prediction (e.g., [[0.9, 0.1]])
     prediction = model.predict(img_array, verbose=0)
     
-    #_CRITICAL_FIX_HERE_
-    # Instead of checking prediction[0][0], we get the probability for the specific "jumpscare" class.
+    # Get the specific probability for the 'jumpscare' class
     jumpscare_probability = prediction[0][JUMPSCARE_CLASS_INDEX]
     
     return jumpscare_probability > threshold
@@ -59,8 +55,8 @@ def generate_jumpscare_compilation(video_path, sensitivity, progress=gr.Progress
         # --- Initialization ---
         threshold = sensitivity / 100.0
         analysis_fps = 10
-        pre_scare_buffer = 1.0  # seconds to include before the scare
-        post_scare_buffer = 1.5 # seconds to include after the scare
+        pre_scare_buffer = 1.0  # seconds before the scare
+        post_scare_buffer = 1.5 # seconds after the scare
 
         logging.info(f"Starting analysis for video: {os.path.basename(video_path)}")
         logging.info(f"Settings: Sensitivity={sensitivity}, Threshold={threshold}")
@@ -78,35 +74,34 @@ def generate_jumpscare_compilation(video_path, sensitivity, progress=gr.Progress
             
             if predict_frame_is_jumpscare(frame, threshold):
                 jumpscare_times.append(current_time)
-                logging.info(f"Jumpscare detected at timestamp: {current_time:.2f}s")
 
-        # --- Segment Merging ---
         if not jumpscare_times:
             msg = "No jumpscares detected. Try a lower sensitivity value."
             logging.warning(msg)
             raise gr.Error(msg)
 
-        logging.info(f"Merging {len(jumpscare_times)} detected frames into continuous clips...")
+        # --- Merge close detections into continuous segments ---
+        logging.info(f"Merging {len(jumpscare_times)} detected frames into clips...")
         merged_segments = []
         if jumpscare_times:
             start_time = end_time = jumpscare_times[0]
             for t in jumpscare_times[1:]:
                 if t <= end_time + post_scare_buffer:
-                    end_time = t # Extend the segment
+                    end_time = t
                 else:
                     merged_segments.append((max(0, start_time - pre_scare_buffer), end_time + post_scare_buffer))
                     start_time = end_time = t
             merged_segments.append((max(0, start_time - pre_scare_buffer), end_time + post_scare_buffer))
 
-        # --- Video Creation ---
+        # --- Create Final Video ---
         progress(0.9, desc="Stitching clips together...")
         final_clips = [original_clip.subclip(start, min(end, original_clip.duration)) for start, end in merged_segments]
         
         final_video = concatenate_videoclips(final_clips, method="compose")
         
+        # Save the output video to the root with a unique name
         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        output_filename = f"jumpscare_compilation_{timestamp}.mp4"
-        output_path = os.path.join(OUTPUT_DIR, output_filename)
+        output_path = f"jumpscare_compilation_{timestamp}.mp4"
         
         logging.info(f"Writing final video to {output_path}")
         final_video.write_videofile(output_path, codec="libx264", audio_codec="aac")
@@ -121,7 +116,7 @@ def generate_jumpscare_compilation(video_path, sensitivity, progress=gr.Progress
         logging.error(f"An error occurred: {e}", exc_info=True)
         raise gr.Error(f"An unexpected error occurred. Check the logs for details. Error: {e}")
 
-# --- Gradio Interface ---
+# --- Gradio Interface (Simplified) ---
 iface = gr.Interface(
     fn=generate_jumpscare_compilation,
     inputs=[
@@ -131,7 +126,7 @@ iface = gr.Interface(
     ],
     outputs=gr.Video(label="Jumpscare Compilation"),
     title="🤖 AI FNAF Jumpscare Dump Generator",
-    description="Upload a video, and the AI will find all jumpscares and compile them. This app runs on a trained model.",
+    description="Upload a video, and the AI will find all jumpscares and compile them. All files are in the root directory.",
     allow_flagging="never"
 )