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PE.py

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+import os
+from tqdm import tqdm
+import time
+# Input files and directories
+
+typ = "abstract"
+
+input_file = "abstract_prompts.txt"
+
+#selected_videos_nums = [0,20,40,60,80,120,140,150] #,,80,120,140,150
+
+selected_videos_nums = [10,50,100] #,,80,120,140,150
+tags = [1]
+for selected_videos_num in selected_videos_nums:
+    for tag in tags:
+        rag_prompt_dir = f"rag_cot_prompt_ai_{typ}_rag_{selected_videos_num}_tags_{tag}_testset/" # rag_cot_prompt_ai_abstract_rag_50_tags_2_testset
+        output_dir = f"creation_rag_cot_prompt_ai_{typ}_rag_{selected_videos_num}_tags_{tag}_testset/"
+        script_file = "pipline.py"
+        # Create directories if they do not exist
+        os.makedirs(rag_prompt_dir, exist_ok=True)
+        os.makedirs(output_dir, exist_ok=True)
+        # Read UserPrompts.txt
+        with open(input_file, "r", encoding="utf-8") as f:
+            prompts = f.readlines()
+
+        # # Process each prompt
+        for i, prompt in enumerate(tqdm(prompts,desc="Generating Prompts")):
+            prompt = prompt.strip()
+            output_path = os.path.join(rag_prompt_dir, f"{prompt}.txt")
+            if not prompt or os.path.exists(output_path):  # Skip empty lines
+                continue
+
+            safe_filename = f"{prompt[:].replace(' ', '_').replace('/', '_')}.txt"
+            rag_prompt_file = os.path.join(rag_prompt_dir, safe_filename)
+
+            # Generate RAG Prompt
+            command = f"python {script_file} --USER_PROMPT \'{prompt}\' --OUTPUT_DIR {rag_prompt_dir} --MODE generate --MODEL llama --VID_NUM {selected_videos_num} --TAGS_NUM {tag}"
+            result = os.system(command)  # Run the command
+            if result == 0:
+                print(f"Generated RAG prompt for: {prompt[:50]} -> Saved to {rag_prompt_file}")
+            else:
+                print(f"Error generating RAG prompt for: {prompt}")
+                continue
+
+# Process all RAG prompt files
+# Run inference
+        command = f"python {script_file} --INPUT_DIR {rag_prompt_dir} --OUTPUT_DIR {output_dir} --MODE infer --MODEL llama --VID_NUM {selected_videos_num} --TAGS_NUM {tag}"
+        result = os.system(command)  # Run the command
+
+
+print("All prompts have been processed and saved.")