remove sentence bank and pre-written option

app.py

@@ -195,10 +195,11 @@ with gr.Blocks(title="Voice Consent Gate") as demo:
             """
             )
         with gr.Column():
-            consent_method = gr.Dropdown(label="Sentence generation method",
-                                         choices=["Llama 3.2 3B Instruct",
-                                                  "Pre-written"],
-                                         value="Pre-written")
+            consent_method = gr.Dropdown(
+                label="Sentence generation method",
+                choices=["Llama 3.2 3B Instruct"],
+                value="Llama 3.2 3B Instruct"
+            )
             asr_model = gr.Dropdown(label="Speech recognition model",
                                     choices=["openai/whisper-tiny.en",  # fastest (CPU-friendly)
                                             "openai/whisper-base.en",  # better accuracy, a bit slower

src/generate.py

@@ -9,16 +9,10 @@ Hugging Face Space for Llama 3.2 3B Instruct) to generate natural-sounding
 sentences that users can read aloud to give informed consent for voice cloning.
 
 If the model call fails (e.g., due to rate limits or network issues),
-a fallback sentence is chosen from a small built-in sentence bank.
-
-Functions:
-    - _extract_llama_text(): Normalize the API output from the Llama demo.
-    - gen_sentence_llm(): Generate a consent sentence from the Llama model Space.
-    - gen_sentence_set(): Select a random prewritten sentence (for fallback/testing).
+an error is surfaced to the UI (no local fallback).
 """
 
 import os
-import random
 from typing import Any
 from gradio_client import Client
 
@@ -26,24 +20,7 @@ import src.process as process
 from src.prompts import get_consent_generation_prompt
 
 
-# ------------------- Sentence Bank (unchanged) -------------------
-SENTENCE_BANK = [
-    "The quick brown fox jumps over the lazy dog.",
-    "I promise to speak clearly and at a steady pace.",
-    "Open source makes AI more transparent and inclusive.",
-    "Hugging Face Spaces make demos easy to share.",
-    "Today the weather in Berlin is pleasantly cool.",
-    "Privacy and transparency should go hand in hand.",
-    "Please generate a new sentence for me to read.",
-    "Machine learning can amplify or reduce inequality.",
-    "Responsible AI requires participation from everyone.",
-    "This microphone test checks my pronunciation accuracy.",
-]
-
-
 # ------------------- Model / Space Configuration -------------------
-# The demo connects to the Llama 3.2 3B Instruct Space on Hugging Face.
-# You can override these defaults by setting environment variables in your Space.
 LLAMA_SPACE_ID = os.getenv(
     "LLAMA_SPACE_ID", "huggingface-projects/llama-3.2-3B-Instruct"
 )
@@ -54,29 +31,12 @@ HF_TOKEN = os.getenv("HF_TOKEN")  # Optional; not required for public Spaces.
 def _extract_llama_text(result: Any) -> str:
     """
     Normalize the API response from the Llama 3.2 3B demo Space into plain text.
-
-    The Space’s `/chat` endpoint may return different shapes depending on how
-    the Gradio app is structured — sometimes a string, other times a dictionary
-    or list. This function recursively traverses and extracts the first
-    meaningful text string it finds.
-
-    Parameters
-    ----------
-    result : Any
-        The raw output returned by `client.predict()`.
-
-    Returns
-    -------
-    str
-        Cleaned text output (may be empty string if extraction fails).
     """
     if isinstance(result, str):
         return result.strip()
     if isinstance(result, (int, float, bool)):
         return str(result)
     if isinstance(result, list):
-        # If multiple segments are returned (e.g., multiple sentences),
-        # join them into one string.
         parts = []
         for x in result:
             s = _extract_llama_text(x)
@@ -84,7 +44,6 @@ def _extract_llama_text(result: Any) -> str:
                 parts.append(s)
         return " ".join(parts).strip()
     if isinstance(result, dict):
-        # Common key names used in Gradio JSON responses
         for key in ("text", "response", "content", "generated_text", "message"):
             v = result.get(key)
             if isinstance(v, str) and v.strip():
@@ -92,60 +51,33 @@ def _extract_llama_text(result: Any) -> str:
     return ""
 
 
-def gen_sentence(sentence_method="Pre-written", audio_model_name="Chatterbox"):
-    # chatterbox model name, detailed prompt (short_prompt=False)
-    if sentence_method == "Pre-written":
-        return gen_sentence_set()
-    else:
-        try:
-            return gen_sentence_llm(sentence_method,
-                audio_model_name,
-                fallback_on_error=False  # ← show errors during testing
-            )
-        except Exception as e:
-            # Show a helpful message directly in the Target sentence box
-            return f"[ERROR calling LLM] {type(e).__name__}: {e}"
-
-# TODO: Support more than just Llama 3.2 3B Instruct
-def gen_sentence_llm(sentence_method="Llama 3.2 3B Instruct", audio_model_name: str = "Chatterbox", *, fallback_on_error: bool = False  # Set True for production to avoid crashes
+def gen_sentence(_ignored_method="Llama 3.2 3B Instruct", audio_model_name="Chatterbox"):
+    """
+    Always generate a sentence via the LLM. UI may still pass a 'method' arg,
+    but it's ignored to keep the callback signature stable.
+    """
+    try:
+        return gen_sentence_llm(audio_model_name=audio_model_name, fallback_on_error=False)
+    except Exception as e:
+        # Show a helpful message directly in the Target sentence box
+        return f"[ERROR calling LLM] {type(e).__name__}: {e}"
+
+
+def gen_sentence_llm(
+    sentence_method: str = "Llama 3.2 3B Instruct",
+    audio_model_name: str = "Chatterbox",
+    *,
+    fallback_on_error: bool = False  # kept for signature parity; does nothing now
 ) -> str:
     """
     Generate a consent sentence using the Llama 3.2 3B Instruct demo Space.
 
-    This function constructs a prompt describing the linguistic and ethical
-    requirements for a consent sentence (via `get_consent_generation_prompt`)
-    and sends it to the Llama demo hosted on Hugging Face Spaces.
-
-    The response is normalized into a single English sentence suitable
-    for reading aloud.
-
-    Parameters
-    ----------
-    audio_model_name : str, optional
-        The name of the voice-cloning model to mention in the sentence.
-        Defaults to "Chatterbox".
-    fallback_on_error : bool, optional
-        If True, return a random fallback sentence instead of raising
-        an error when the Space call fails. Default is False for debugging.
-
-    Returns
-    -------
-    str
-        A clean, human-readable consent sentence.
-
-    Raises
-    ------
-    Exception
-        Re-raises the underlying error if `fallback_on_error` is False.
+    Returns a single English sentence suitable for reading aloud.
     """
-    # Generate the full natural-language prompt that the LLM will receive
     prompt = get_consent_generation_prompt(audio_model_name)
 
     try:
-        # Initialize Gradio client for the Llama demo Space
         client = Client(LLAMA_SPACE_ID, hf_token=HF_TOKEN)
-
-        # The Llama demo exposes a simple /chat endpoint with standard decoding params
         result = client.predict(
             message=prompt,
             max_new_tokens=128,
@@ -156,38 +88,16 @@ def gen_sentence_llm(sentence_method="Llama 3.2 3B Instruct", audio_model_name:
             api_name=LLAMA_API_NAME,
         )
 
-        # Normalize and clean up model output
         text = _extract_llama_text(result)
         text = process.normalize_text(text, lower=False)
 
-        # Handle empty or malformed outputs
         if not text:
             raise ValueError("Empty response from Llama Space")
 
-        # In case the model produces multiple lines or options, pick the first full sentence
         first_line = next((ln.strip() for ln in text.splitlines() if ln.strip()), "")
         return first_line or text
 
     except Exception as e:
         print(f"[gen_sentence_llm] Llama Space call failed: {type(e).__name__}: {e}")
-        if fallback_on_error:
-            # If fallback is enabled, use a predefined sentence instead
-            return random.choice(SENTENCE_BANK)
-        # Otherwise propagate the exception so the UI displays it
-        raise
-
-
-def gen_sentence_set() -> str:
-    """
-    Return a sentence from a predefined static list.
-
-    This is used as a simple fallback generator when model-based
-    generation is unavailable or for testing the ASR pipeline
-    without network access.
-
-    Returns
-    -------
-    str
-        A single English sentence from the fallback bank.
-    """
-    return random.choice(SENTENCE_BANK)
+        # No local fallback anymore; surface the error to the UI.
+        raise