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Update app.py
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app.py
CHANGED
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@@ -14,6 +14,8 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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import PyPDF2
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import traceback
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import os
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import shutil
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from pathlib import Path
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@@ -50,6 +52,7 @@ model = None
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tokenizer = None
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generation_config = None
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def test_llm_generation():
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try:
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test_prompt = "Hello, how are you today?"
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@@ -67,7 +70,7 @@ def test_llm_generation():
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except Exception as e:
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add_log(f"❌ LLM quick test failed: {e}")
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-
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def initialize_model():
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global model, tokenizer, generation_config
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@@ -167,7 +170,7 @@ class PodcastGenerator:
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error_msg = f"❌ PDF extraction failed: {str(e)}"
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add_log(error_msg)
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raise Exception(error_msg)
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-
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async def postprocess_conversation(self, raw_text: str) -> str:
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"""Run LLM again to enforce strict Speaker 1/2 format"""
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prompt = f"""
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@@ -345,7 +348,8 @@ Now format the following:
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add_log(f"✅ Fallback podcast created with {len(podcast_lines)} lines")
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return result
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async def generate_script(self, prompt: str, language: str, file_obj=None, progress=None) -> Dict:
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"""Improved script generation with better error handling"""
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if not model_loaded or not self.model or not self.tokenizer:
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import PyPDF2
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import traceback
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import os
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pip install spaces
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from spaces import GPU
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import shutil
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from pathlib import Path
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tokenizer = None
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generation_config = None
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@spaces.GPU
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def test_llm_generation():
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try:
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test_prompt = "Hello, how are you today?"
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except Exception as e:
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add_log(f"❌ LLM quick test failed: {e}")
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@spaces.GPU
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def initialize_model():
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global model, tokenizer, generation_config
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error_msg = f"❌ PDF extraction failed: {str(e)}"
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add_log(error_msg)
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raise Exception(error_msg)
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@spaces.GPU
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async def postprocess_conversation(self, raw_text: str) -> str:
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"""Run LLM again to enforce strict Speaker 1/2 format"""
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prompt = f"""
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add_log(f"✅ Fallback podcast created with {len(podcast_lines)} lines")
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return result
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@spaces.GPU
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async def generate_script(self, prompt: str, language: str, file_obj=None, progress=None) -> Dict:
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"""Improved script generation with better error handling"""
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if not model_loaded or not self.model or not self.tokenizer:
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