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Update app.py
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app.py
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@@ -25,7 +25,7 @@ class BasicAgent:
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self.qa_pipeline = pipeline("question-answering")
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self.ner_pipeline = pipeline("ner", aggregation_strategy="simple")
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self.embedding_model = pipeline("feature-extraction")
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def extract_named_entities(self, text):
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entities = self.ner_pipeline(text)
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return [e["word"] for e in entities if e["entity_group"] == "PER"]
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@@ -42,6 +42,7 @@ class BasicAgent:
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audio_path = "temp_audio.wav"
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video.audio.write_audiofile(audio_path)
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result = self.whisper_model.transcribe(audio_path)
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return result["text"]
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def search(self, question: str) -> str:
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@@ -61,9 +62,26 @@ class BasicAgent:
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except:
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return context # Fallback to context if QA fails
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def __call__(self, question: str, video_path: str = None) -> str:
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print(f"Agent received question: {question[:60]}...")
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if video_path:
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transcription = self.call_whisper(video_path)
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print(f"Transcribed video: {transcription[:100]}...")
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@@ -73,7 +91,7 @@ class BasicAgent:
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answer = self.answer_question(question, context)
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q_lower = question.lower()
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#
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if "who" in q_lower:
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people = self.extract_named_entities(context)
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return f"👤 Who: {', '.join(people) if people else 'No person found'}\n\n🧠 Answer: {answer}"
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@@ -92,7 +110,6 @@ class BasicAgent:
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else:
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return f"🧠 Answer: {answer}"
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# --- Submission Function ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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self.qa_pipeline = pipeline("question-answering")
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self.ner_pipeline = pipeline("ner", aggregation_strategy="simple")
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self.embedding_model = pipeline("feature-extraction")
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+
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def extract_named_entities(self, text):
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entities = self.ner_pipeline(text)
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return [e["word"] for e in entities if e["entity_group"] == "PER"]
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audio_path = "temp_audio.wav"
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video.audio.write_audiofile(audio_path)
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result = self.whisper_model.transcribe(audio_path)
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os.remove(audio_path)
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return result["text"]
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def search(self, question: str) -> str:
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except:
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return context # Fallback to context if QA fails
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def handle_logic_riddles(self, question: str) -> str | None:
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q = question.lower().strip()
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if re.search(r"opposite of the word ['\"]?left['\"]?", q):
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return "right"
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# Add more patterns here
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if re.match(r".*first letter of the alphabet.*", q):
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return "a"
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return None
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def __call__(self, question: str, video_path: str = None) -> str:
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print(f"Agent received question: {question[:60]}...")
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# Handle logic/riddle questions first
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logic_answer = self.handle_logic_riddles(question)
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if logic_answer is not None:
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return f"🧠 Logic Answer: {logic_answer}"
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if video_path:
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transcription = self.call_whisper(video_path)
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print(f"Transcribed video: {transcription[:100]}...")
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answer = self.answer_question(question, context)
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q_lower = question.lower()
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# Enhanced formatting based on question type
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if "who" in q_lower:
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people = self.extract_named_entities(context)
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return f"👤 Who: {', '.join(people) if people else 'No person found'}\n\n🧠 Answer: {answer}"
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else:
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return f"🧠 Answer: {answer}"
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# --- Submission Function ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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