Update utils.py

utils.py

@@ -1,3 +1,5 @@
+# utils.py
+
 import os
 import re
 import json
@@ -13,10 +15,11 @@ import tiktoken
 from groq import Groq
 import numpy as np
 import torch
+import random
 
 class DialogueItem(BaseModel):
-    speaker: Literal["Jane", "John"]  # TTS voice
-    display_speaker: str = "Jane"  # For display in transcript
+    speaker: Literal["Jane", "John"]   # TTS voice
+    display_speaker: str = "Jane"      # For display in transcript
     text: str
 
 class Dialogue(BaseModel):
@@ -44,7 +47,8 @@ def truncate_text(text, max_tokens=2048):
 
 def extract_text_from_url(url):
     """
-    Fetches and extracts readable text from a given URL (stripping out scripts, styles, etc.).
+    Fetches and extracts readable text from a given URL
+    (stripping out scripts, styles, etc.).
     """
     print("[LOG] Extracting text from URL:", url)
     try:
@@ -81,7 +85,8 @@ def pitch_shift(audio: AudioSegment, semitones: int) -> AudioSegment:
 
 def is_sufficient(text: str, min_word_count: int = 500) -> bool:
     """
-    Checks if the fetched text meets our sufficiency criteria (e.g., at least 500 words).
+    Checks if the fetched text meets our sufficiency criteria
+    (e.g., at least 500 words).
     """
     word_count = len(text.split())
     print(f"[DEBUG] Aggregated word count: {word_count}")
@@ -93,7 +98,6 @@ def query_llm_for_additional_info(topic: str, existing_text: str) -> str:
     Appends it to our aggregated info if found.
     """
     print("[LOG] Querying LLM for additional information.")
-    
     system_prompt = (
         "You are an AI assistant with extensive knowledge up to 2023-10. "
         "Provide additional relevant information on the following topic based on your knowledge base.\n\n"
@@ -101,9 +105,7 @@ def query_llm_for_additional_info(topic: str, existing_text: str) -> str:
         f"Existing Information: {existing_text}\n\n"
         "Please add more insightful details, facts, and perspectives to enhance the understanding of the topic."
     )
-
     groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-    
     try:
         response = groq_client.chat.completions.create(
             messages=[{"role": "system", "content": system_prompt}],
@@ -111,22 +113,19 @@ def query_llm_for_additional_info(topic: str, existing_text: str) -> str:
             max_tokens=1024,
             temperature=0.7
         )
-        
-        additional_info = response.choices[0].message.content.strip()
-        print("[DEBUG] Additional information from LLM:")
-        print(additional_info)
-        return additional_info
-    
     except Exception as e:
         print("[ERROR] Groq API error during fallback:", e)
         return ""
+    additional_info = response.choices[0].message.content.strip()
+    print("[DEBUG] Additional information from LLM:")
+    print(additional_info)
+    return additional_info
 
 def research_topic(topic: str) -> str:
     """
     Gathers info from various RSS feeds and Wikipedia. If needed, queries the LLM
     for more data if the aggregated text is insufficient.
     """
-    
     sources = {
         "BBC": "https://feeds.bbci.co.uk/news/rss.xml",
         "CNN": "http://rss.cnn.com/rss/edition.rss",
@@ -138,458 +137,477 @@ def research_topic(topic: str) -> str:
         "Google News - Custom": f"https://news.google.com/rss/search?q={requests.utils.quote(topic)}&hl=en-IN&gl=IN&ceid=IN:en",
     }
 
-   summary_parts = []  # Wikipedia summary
-   wiki_summary = fetch_wikipedia_summary(topic)
-   
-   if wiki_summary:
-       summary_parts.append(f"From Wikipedia: {wiki_summary}")
-
-   # For each RSS feed
-   for name, feed_url in sources.items():
-       try:
-           items = fetch_rss_feed(feed_url)
-           if not items:
-               continue
-
-           title, desc, link = find_relevant_article(items, topic, min_match=2)
-
-           if link:
-               article_text = fetch_article_text(link)
-               if article_text:
-                   summary_parts.append(f"From {name}: {article_text}")
-               else:
-                   summary_parts.append(f"From {name}: {title} - {desc}")
-
-       except Exception as e:
-           print(f"[ERROR] Error fetching from {name} RSS feed:", e)
-           continue
-
-   aggregated_info = " ".join(summary_parts)
-
-   print("[DEBUG] Aggregated info from primary sources:")
-   print(aggregated_info)
-
-   # If not enough data, fallback to LLM
-   if not is_sufficient(aggregated_info):
-       print("[LOG] Insufficient info from primary sources. Fallback to LLM.")
-       additional_info = query_llm_for_additional_info(topic, aggregated_info)
-
-       if additional_info:
-           aggregated_info += " " + additional_info
-       else:
-           print("[ERROR] Failed to retrieve additional info from LLM.")
-
-   if not aggregated_info:
-       return f"Sorry, I couldn't find recent information on '{topic}'."
-
-   return aggregated_info
+    summary_parts = []
+
+    # Wikipedia summary
+    wiki_summary = fetch_wikipedia_summary(topic)
+    if wiki_summary:
+        summary_parts.append(f"From Wikipedia: {wiki_summary}")
+
+    # For each RSS feed
+    for name, feed_url in sources.items():
+        try:
+            items = fetch_rss_feed(feed_url)
+            if not items:
+                continue
+            title, desc, link = find_relevant_article(items, topic, min_match=2)
+            if link:
+                article_text = fetch_article_text(link)
+                if article_text:
+                    summary_parts.append(f"From {name}: {article_text}")
+                else:
+                    summary_parts.append(f"From {name}: {title} - {desc}")
+        except Exception as e:
+            print(f"[ERROR] Error fetching from {name} RSS feed:", e)
+            continue
+
+    aggregated_info = " ".join(summary_parts)
+    print("[DEBUG] Aggregated info from primary sources:")
+    print(aggregated_info)
+
+    # If not enough data, fallback to LLM
+    if not is_sufficient(aggregated_info):
+        print("[LOG] Insufficient info from primary sources. Fallback to LLM.")
+        additional_info = query_llm_for_additional_info(topic, aggregated_info)
+        if additional_info:
+            aggregated_info += " " + additional_info
+        else:
+            print("[ERROR] Failed to retrieve additional info from LLM.")
+
+    if not aggregated_info:
+        return f"Sorry, I couldn't find recent information on '{topic}'."
+
+    return aggregated_info
 
 def fetch_wikipedia_summary(topic: str) -> str:
-   """
-   Fetch a quick Wikipedia summary of the topic via the official Wikipedia API.
-   """
-   print("[LOG] Fetching Wikipedia summary for:", topic)
-   
-   try:
-       search_url = (
-           f"https://en.wikipedia.org/w/api.php?action=opensearch&search={requests.utils.quote(topic)}"
-           "&limit=1&namespace=0&format=json"
-       )
-
-       resp = requests.get(search_url)
-       if resp.status_code != 200:
-           print(f"[ERROR] Failed to fetch Wikipedia search results for {topic}")
-           return ""
-
-       data = resp.json()
-       if len(data) > 1 and data[1]:
-           title = data[1][0]
-           summary_url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{requests.utils.quote(title)}"
-           s_resp = requests.get(summary_url)
-           
-           if s_resp.status_code == 200:
-               s_data = s_resp.json()
-               if "extract" in s_data:
-                   print("[LOG] Wikipedia summary fetched successfully.")
-                   return s_data["extract"]
-       return ""
-
-   except Exception as e:
-       print(f"[ERROR] Exception during Wikipedia summary fetch: {e}")
-       return ""
+    """
+    Fetch a quick Wikipedia summary of the topic via the official Wikipedia API.
+    """
+    print("[LOG] Fetching Wikipedia summary for:", topic)
+    try:
+        search_url = (
+            f"https://en.wikipedia.org/w/api.php?action=opensearch&search={requests.utils.quote(topic)}"
+            "&limit=1&namespace=0&format=json"
+        )
+        resp = requests.get(search_url)
+        if resp.status_code != 200:
+            print(f"[ERROR] Failed to fetch Wikipedia search results for {topic}")
+            return ""
+        data = resp.json()
+        if len(data) > 1 and data[1]:
+            title = data[1][0]
+            summary_url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{requests.utils.quote(title)}"
+            s_resp = requests.get(summary_url)
+            if s_resp.status_code == 200:
+                s_data = s_resp.json()
+                if "extract" in s_data:
+                    print("[LOG] Wikipedia summary fetched successfully.")
+                    return s_data["extract"]
+        return ""
+    except Exception as e:
+        print(f"[ERROR] Exception during Wikipedia summary fetch: {e}")
+        return ""
 
 def fetch_rss_feed(feed_url: str) -> list:
-   """
-   Pulls RSS feed data from a given URL and returns items.
-   """
-   print("[LOG] Fetching RSS feed:", feed_url)
-
-   try:
-       resp = requests.get(feed_url)
-       if resp.status_code != 200:
-           print(f"[ERROR] Failed to fetch RSS feed: {feed_url}")
-           return []
-
-       soup = BeautifulSoup(resp.content, "xml")
-       items = soup.find_all("item")
-       return items
-
-   except Exception as e:
-       print(f"[ERROR] Exception fetching RSS feed {feed_url}: {e}")
-       return []
+    """
+    Pulls RSS feed data from a given URL and returns items.
+    """
+    print("[LOG] Fetching RSS feed:", feed_url)
+    try:
+        resp = requests.get(feed_url)
+        if resp.status_code != 200:
+            print(f"[ERROR] Failed to fetch RSS feed: {feed_url}")
+            return []
+        soup = BeautifulSoup(resp.content, "xml")
+        items = soup.find_all("item")
+        return items
+    except Exception as e:
+        print(f"[ERROR] Exception fetching RSS feed {feed_url}: {e}")
+        return []
 
 def find_relevant_article(items, topic: str, min_match=2) -> tuple:
-   """
-   Check each article in the RSS feed for mention of the topic by counting 
-   the number of keyword matches.
-   """
-   print("[LOG] Finding relevant articles...")
-   
-   keywords = re.findall(r'\w+', topic.lower())
-
-   for item in items:
-       title = item.find("title").get_text().strip() if item.find("title") else ""
-       description = item.find("description").get_text().strip() if item.find("description") else ""
-       
-       text = (title + " " + description).lower()
-       
-       matches = sum(1 for kw in keywords if kw in text)
-
-       if matches >= min_match:
-           link = item.find("link").get_text().strip() if item.find("link") else ""
-           print(f"[LOG] Relevant article found: {title}")
-           return title, description, link
-
-   return None, None, None
+    """
+    Check each article in the RSS feed for mention of the topic
+    by counting the number of keyword matches.
+    """
+    print("[LOG] Finding relevant articles...")
+    keywords = re.findall(r'\w+', topic.lower())
+    for item in items:
+        title = item.find("title").get_text().strip() if item.find("title") else ""
+        description = item.find("description").get_text().strip() if item.find("description") else ""
+        text = (title + " " + description).lower()
+        matches = sum(1 for kw in keywords if kw in text)
+        if matches >= min_match:
+            link = item.find("link").get_text().strip() if item.find("link") else ""
+            print(f"[LOG] Relevant article found: {title}")
+            return title, description, link
+    return None, None, None
 
 def fetch_article_text(link: str) -> str:
-   """
-   Fetch the article text from the given link (first 5 paragraphs).
-   """
-   print("[LOG] Fetching article text from:", link)
-
-   if not link:
-       print("[LOG] No link provided for article text.")
-       return ""
-
-   try:
-       resp = requests.get(link)
-       
-       if resp.status_code != 200:
-           print(f"[ERROR] Failed to fetch article from {link}")
-           return ""
-
-       soup = BeautifulSoup(resp.text, 'html.parser')
-       
-       paragraphs = soup.find_all("p")
-       
-       text = " ".join(p.get_text() for p in paragraphs[:5])  # first 5 paragraphs
-
-       print("[LOG] Article text fetched successfully.")
-       
-       return text.strip()
-
-   except Exception as e:
-       print(f"[ERROR] Error fetching article text: {e}")
-       return ""
+    """
+    Fetch the article text from the given link (first 5 paragraphs).
+    """
+    print("[LOG] Fetching article text from:", link)
+    if not link:
+        print("[LOG] No link provided for article text.")
+        return ""
+    try:
+        resp = requests.get(link)
+        if resp.status_code != 200:
+            print(f"[ERROR] Failed to fetch article from {link}")
+            return ""
+        soup = BeautifulSoup(resp.text, 'html.parser')
+        paragraphs = soup.find_all("p")
+        text = " ".join(p.get_text() for p in paragraphs[:5])  # first 5 paragraphs
+        print("[LOG] Article text fetched successfully.")
+        return text.strip()
+    except Exception as e:
+        print(f"[ERROR] Error fetching article text: {e}")
+        return ""
 
 def generate_script(
-   system_prompt: str,
-   input_text: str,
-   tone: str,
-   target_length: str,
-   host_name: str = "Jane",
-   guest_name: str = "John",
-   sponsor_style: str = "Separate Break",
-   sponsor_provided=None  # Accept sponsor_provided parameter
+    system_prompt: str,
+    input_text: str,
+    tone: str,
+    target_length: str,
+    host_name: str = "Jane",
+    guest_name: str = "John",
+    sponsor_style: str = "Separate Break",
+    sponsor_provided=None  # Accept sponsor_provided parameter
 ):
-   
-   print("[LOG] Generating script with tone:", tone, "and length:", target_length)
-
-   groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-   words_per_minute = 150
-   numeric_minutes = 3
-
-   match = re.search(r"(\d+)", target_length)
-   
-   if match:
-       numeric_minutes = int(match.group(1))
-
-   min_words = max(50, numeric_minutes * 100)
-   
-   max_words = numeric_minutes * words_per_minute
-
-   tone_map = {
-      "Humorous": "funny and exciting, makes people chuckle",
-      "Formal": "business-like, well-structured, professional",
-      "Casual": "like a conversation between close friends, relaxed and informal",
-      "Youthful": "like how teenagers might chat, energetic and lively"
-   }
-
-# Determine sponsor instructions based on sponsor_provided and sponsor_style
-if sponsor_provided:
-
-if sponsor_style == "Separate Break":
-sponsor_instructions = (
-"If sponsor content is provided, include it in a separate ad break (~30 seconds). "
-"Use phrasing like 'Now a word from our sponsor...' and end with 'Back to the show' or similar."
-)
-
-else:
-
-sponsor_instructions = (
-"If sponsor content is provided, blend it naturally (~30 seconds) into the conversation. "
-"Avoid abrupt transitions."
-)
-
-else:
-
-sponsor_instructions="" # No sponsor instructions if sponsor_provided is empty
-
-prompt=(
-
-f"{system_prompt}\n"
-
-f"TONE:{chosen_tone}\n"
-
-f"TARGET LENGTH:{target_length} (~{min_words}-{max_words} words)\n"
-
-f"INPUT TEXT:{input_text}\n\n"
-
-f"# Sponsor Style Instruction:\n{sponsor_instructions}\n\n"
-
-"Please provide the output in the following JSON format without any additional text:\n\n"
-
-"{\n"
-
-' "dialogue":[\n'
-
-' {\n'
-
-' "speaker":"Jane",\n'
-
-' "text":"..."\n'
-
-' },\n'
-
-' {\n'
-
-' "speaker":"John",\n'
-
-' "text":"..."\n'
-
-' }\n'
-
-" ]\n"
-
-"}"
-)
-
-print("[LOG] Sending prompt to Groq:")
-print(prompt)
-
-try:
-
-response=groq_client.chat.completions.create(
-
-messages=[{"role":"system","content":prompt}],
-
-model="llama-3.3-70b-versatile",
-
-max_tokens=2048,
-
-temperature=0.7
-
-
-
-except Exception as e:
-
-print("[ERROR] Groq API error:", e)
-
-raise ValueError(f"Error communicating with Groq API:{str(e)}")
-
-raw_content=response.choices[0].message.content.strip()
-
-start_index=raw_content.find('{')
-
-end_index=raw_content.rfind('}')
-
-if start_index==-1 or end_index==-1:
-
-raise ValueError("Failed to parse dialogue:No JSON found.")
-
-json_str=raw_content[start_index:end_index+1].strip()
-
-try:
-
-data=json.loads(json_str)
-
-dialogue_list=data.get("dialogue",[])
-
-for d in dialogue_list:
-
-raw_speaker=d.get("speaker","Jane")
-
-if raw_speaker.lower()==host_name.lower():
-
-d["speaker"]="Jane"
-
-d["display_speaker"]=host_name
-
-elif raw_speaker.lower()==guest_name.lower():
-
-d["speaker"]="John"
-
-d["display_speaker"]=guest_name
-
-else:
-
-d["speaker"]="Jane"
-
-d["display_speaker"]=raw_speaker
-
-new_dialogue_items=[]
-
-for d in dialogue_list:
-
-if “display_speaker” not in d:
-
-d[“display_speaker”]=d[“speaker”]
-
-new_dialogue_items.append(DialogueItem(**d))
-
-return Dialogue(dialogue=new_dialogue_items)
-
-except json.JSONDecodeError as e:
-
-print("[ERROR] JSON decoding(format) failed:", e)
-
-raise ValueError(f"Failed to parse dialogue:{str(e)}")
-
-except Exception as e:
-
-print("[ERROR] JSON decoding failed:", e)
-
-raise ValueError(f"Failed to parse dialogue:{str(e)}")
-
-def transcribe_youtube_video(video_url:str)->str:
-
-print("[LOG] Transcribing YouTube video via RapidAPI:", video_url)
-
-video_id_match=re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", video_url)
-
-if not video_id_match:
-
-raise ValueError(f"Invalid YouTube URL:{video_url},cannot extract video ID.")
-
-video_id=video_id_match.group(1)
-
-print("[LOG] Extracted video ID:", video_id)
-
-base_url="https://youtube-transcriptor.p.rapidapi.com/transcript"
-
-params={
-
-"video_id":video_id,
-
-"lang":"en"
-}
-
-headers={
-
-"x-rapidapi-host":"youtube-transcriptor.p.rapidapi.com",
-
-"x-rapidapi-key":os.environ.get("RAPIDAPI_KEY")
-
-}
-
-try:
-
-response=requests.get(base_url,headers=headers,params=params,timeouot=30)
-
-print("[LOG] RapidAPI Response Status Code:",response.status_code)
-
-print("[LOG] RapidAPI Response Body:",response.text)
-
-if response.status_code!=200:
-
-raise ValueError(f"RapidAPI transcription error:{response.status_code},{response.text}")
-
-data=response.json()
+    print("[LOG] Generating script with tone:", tone, "and length:", target_length)
+    groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
 
-if not isinstance(data,list) or not data:
+    words_per_minute = 150
+    numeric_minutes = 3
+    match = re.search(r"(\d+)", target_length)
+    if match:
+        numeric_minutes = int(match.group(1))
 
-raise ValueError(f"Unexpected transcript format or empty transcript:{data}")
+    min_words = max(50, numeric_minutes * 100)
+    max_words = numeric_minutes * words_per_minute
 
-transcript_as_text=data[0].get('transcriptionAsText','').strip()
+    tone_map = {
+        "Humorous": "funny and exciting, makes people chuckle",
+        "Formal": "business-like, well-structured, professional",
+        "Casual": "like a conversation between close friends, relaxed and informal",
+        "Youthful": "like how teenagers might chat, energetic and lively"
+    }
+    chosen_tone = tone_map.get(tone, "casual")
+
+    # Determine sponsor instructions based on sponsor_provided and sponsor_style
+    if sponsor_provided:
+        if sponsor_style == "Separate Break":
+            sponsor_instructions = (
+                "If sponsor content is provided, include it in a separate ad break (~30 seconds). "
+                "Use phrasing like 'Now a word from our sponsor...' and end with 'Back to the show' or similar."
+            )
+        else:
+            sponsor_instructions = (
+                "If sponsor content is provided, blend it naturally (~30 seconds) into the conversation. "
+                "Avoid abrupt transitions."
+            )
+    else:
+        sponsor_instructions = ""  # No sponsor instructions if sponsor_provided is empty
+
+    prompt = (
+        f"{system_prompt}\n"
+        f"TONE: {chosen_tone}\n"
+        f"TARGET LENGTH: {target_length} (~{min_words}-{max_words} words)\n"
+        f"INPUT TEXT: {input_text}\n\n"
+        f"# Sponsor Style Instruction:\n{sponsor_instructions}\n\n"
+        "Please provide the output in the following JSON format without any additional text:\n\n"
+        "{\n"
+        '    "dialogue": [\n'
+        '        {\n'
+        '            "speaker": "Jane",\n'
+        '            "text": "..." \n'
+        '        },\n'
+        '        {\n'
+        '            "speaker": "John",\n'
+        '            "text": "..." \n'
+        '        }\n'
+        "    ]\n"
+        "}"
+    )
+    print("[LOG] Sending prompt to Groq:")
+    print(prompt)
 
-if not transcript_as_text:
+    try:
+        response = groq_client.chat.completions.create(
+            messages=[{"role": "system", "content": prompt}],
+            model="llama-3.3-70b-versatile",
+            max_tokens=2048,
+            temperature=0.7
+        )
+    except Exception as e:
+        print("[ERROR] Groq API error:", e)
+        raise ValueError(f"Error communicating with Groq API: {str(e)}")
 
-raise ValueError("transcriptionAsText field is missing or empty.")
+    raw_content = response.choices[0].message.content.strip()
+    start_index = raw_content.find('{')
+    end_index = raw_content.rfind('}')
+    if start_index == -1 or end_index == -1:
+        raise ValueError("Failed to parse dialogue: No JSON found.")
 
-print("[LOG] Transcript retrieval successful.")
+    json_str = raw_content[start_index:end_index+1].strip()
 
-print(f"[DEBUG] Transcript Length:{len(transcript_as_text)} characters.")
+    try:
+        data = json.loads(json_str)
+        dialogue_list = data.get("dialogue", [])
+
+        for d in dialogue_list:
+            raw_speaker = d.get("speaker", "Jane")
+            if raw_speaker.lower() == host_name.lower():
+                d["speaker"] = "Jane"
+                d["display_speaker"] = host_name
+            elif raw_speaker.lower() == guest_name.lower():
+                d["speaker"] = "John"
+                d["display_speaker"] = guest_name
+            else:
+                d["speaker"] = "Jane"
+                d["display_speaker"] = raw_speaker
+
+        new_dialogue_items = []
+        for d in dialogue_list:
+            if "display_speaker" not in d:
+                d["display_speaker"] = d["speaker"]
+            new_dialogue_items.append(DialogueItem(**d))
+
+        return Dialogue(dialogue=new_dialogue_items)
+    except json.JSONDecodeError as e:
+        print("[ERROR] JSON decoding (format) failed:", e)
+        raise ValueError(f"Failed to parse dialogue: {str(e)}")
+    except Exception as e:
+        print("[ERROR] JSON decoding failed:", e)
+        raise ValueError(f"Failed to parse dialogue: {str(e)}")
+
+def transcribe_youtube_video(video_url: str) -> str:
+    print("[LOG] Transcribing YouTube video via RapidAPI:", video_url)
+    video_id_match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", video_url)
+    if not video_id_match:
+        raise ValueError(f"Invalid YouTube URL: {video_url}, cannot extract video ID.")
+
+    video_id = video_id_match.group(1)
+    print("[LOG] Extracted video ID:", video_id)
+
+    base_url = "https://youtube-transcriptor.p.rapidapi.com/transcript"
+    params = {
+        "video_id": video_id,
+        "lang": "en"
+    }
+    headers = {
+        "x-rapidapi-host": "youtube-transcriptor.p.rapidapi.com",
+        "x-rapidapi-key": os.environ.get("RAPIDAPI_KEY")
+    }
 
-snippet=transcript_as_text[:200]+"..."if len(transcript_as_text)>200 else transcript_as_text
+    try:
+        response = requests.get(base_url, headers=headers, params=params, timeout=30)
+        print("[LOG] RapidAPI Response Status Code:", response.status_code)
+        print("[LOG] RapidAPI Response Body:", response.text)
 
-print(f"[DEBUG] Transcript Snippet:{snippet}")
+        if response.status_code != 200:
+            raise ValueError(f"RapidAPI transcription error: {response.status_code}, {response.text}")
 
-return transcript_as_text
+        data = response.json()
+        if not isinstance(data, list) or not data:
+            raise ValueError(f"Unexpected transcript format or empty transcript: {data}")
 
-except Exception as e:
+        transcript_as_text = data[0].get('transcriptionAsText', '').strip()
+        if not transcript_as_text:
+            raise ValueError("transcriptionAsText field is missing or empty.")
 
-print("[ERROR] RapidAPI transcription error:",e)
+        print("[LOG] Transcript retrieval successful.")
+        print(f"[DEBUG] Transcript Length: {len(transcript_as_text)} characters.")
+        snippet = transcript_as_text[:200] + "..." if len(transcript_as_text) > 200 else transcript_as_text
+        print(f"[DEBUG] Transcript Snippet: {snippet}")
 
-raise ValueError(f"Error transcribing YouTube video via RapidAPI:{str(e)}")
+        return transcript_as_text
 
-def generate_audio_mp3(text:str,speaker:str)->str:
-"""
-Calls Deepgram TTS with the text returning a path to a temp MP3 file.
-We also do some pre-processing for punctuation abbreviations,
-numeric expansions plus emotive expressions (ha sigh etc.).
-"""
-try:
+    except Exception as e:
+        print("[ERROR] RapidAPI transcription error:", e)
+        raise ValueError(f"Error transcribing YouTube video via RapidAPI: {str(e)}")
 
-print(f"[LOG] Generating audio for speaker:{speaker}")
+def generate_audio_mp3(text: str, speaker: str) -> str:
+    """
+    Calls Deepgram TTS with the text, returning a path to a temp MP3 file.
+    We also do some pre-processing for punctuation, abbreviations, numeric expansions, 
+    plus emotive expressions (ha, sigh, etc.).
+    """
+    try:
+        print(f"[LOG] Generating audio for speaker: {speaker}")
+        processed_text = _preprocess_text_for_tts(text, speaker)
 
-processed_text=_preprocess_text_for_tts(text,speaker)
+        deepgram_api_url = "https://api.deepgram.com/v1/speak"
+        params = {
+            "model": "aura-asteria-en",  # female by default
+        }
+        if speaker == "John":
+            params["model"] = "aura-zeus-en"
 
-deepgram_api_url="https://api.deepgram.com/v1/speak"
+        headers = {
+            "Accept": "audio/mpeg",
+            "Content-Type": "application/json",
+            "Authorization": f"Token {os.environ.get('DEEPGRAM_API_KEY')}"
+        }
+        body = {
+            "text": processed_text
+        }
 
-params={
+        response = requests.post(deepgram_api_url, params=params, headers=headers, json=body, stream=True)
+        if response.status_code != 200:
+            raise ValueError(f"Deepgram TTS error: {response.status_code}, {response.text}")
 
-"model":"aura-asteria-en", # female by default
-}
+        content_type = response.headers.get('Content-Type', '')
+        if 'audio/mpeg' not in content_type:
+            raise ValueError("Unexpected Content-Type from Deepgram.")
 
-if speaker=="John":
-params["model"]="aura-zeus-en"
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as mp3_file:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    mp3_file.write(chunk)
+            mp3_path = mp3_file.name
 
-headers={
+        # Normalize volume
+        audio_seg = AudioSegment.from_file(mp3_path, format="mp3")
+        audio_seg = effects.normalize(audio_seg)
 
-"Accept":"audio/mpeg",
+        final_mp3_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
+        audio_seg.export(final_mp3_path, format="mp3")
 
-"Content-Type":"application/json",
+        if os.path.exists(mp3_path):
+            os.remove(mp3_path)
 
-"Authorization":f"Token{os.environ.get('DEEPGRAM_API_KEY')}"
-}
+        return final_mp3_path
+    except Exception as e:
+        print("[ERROR] Error generating audio:", e)
+        raise ValueError(f"Error generating audio: {str(e)}")
 
-body={
-"text":processed_text
-}
+def transcribe_youtube_video_OLD_YTDLP(video_url: str) -> str:
+    pass
 
-response=requests.post(deepgram_api_url,param=params ,headers=headers,json=body ,stream=True)
+def _preprocess_text_for_tts(text: str, speaker: str) -> str:
+    """
+    Preprocesses the input text for TTS by handling punctuation, abbreviations,
+    and ensuring numeric sequences are passed directly.
+    """
+    # 1) "SaaS" => "sass"
+    text = re.sub(r"\b(?i)SaaS\b", "sass", text)
+
+    # 2) Insert periods for uppercase abbreviations (>=2 chars), then remove them
+    def insert_periods_for_abbrev(m):
+        abbr = m.group(0)
+        return ".".join(list(abbr)) + "."
+    text = re.sub(r"\b([A-Z0-9]{2,})\b", insert_periods_for_abbrev, text)
+    text = re.sub(r"\.\.", ".", text)
+    def remove_periods_for_tts(m):
+        return m.group(0).replace(".", " ").strip()
+    text = re.sub(r"[A-Z0-9]\.[A-Z0-9](?:\.[A-Z0-9])*\.", remove_periods_for_tts, text)
+
+    # 3) Replace hyphens with spaces
+    text = re.sub(r"-", " ", text)
+
+    # Removed numeric conversions to let TTS handle numbers naturally.
+    # No regex or num2words conversion for numbers here.
+
+    # 6) Emotive placeholders
+    text = re.sub(r"\b(ha(ha)?|heh|lol)\b", "(* laughs *)", text, flags=re.IGNORECASE)
+    text = re.sub(r"\bsigh\b", "(* sighs *)", text, flags=re.IGNORECASE)
+    text = re.sub(r"\b(groan|moan)\b", "(* groans *)", text, flags=re.IGNORECASE)
+
+    # 7) Insert filler words if speaker != "Jane"
+    if speaker != "Jane":
+        def insert_thinking_pause(m):
+            word = m.group(1)
+            if random.random() < 0.3:
+                filler = random.choice(['hmm,', 'well,', 'let me see,'])
+                return f"{word}..., {filler}"
+            else:
+                return f"{word}...,"
+        keywords_pattern = r"\b(important|significant|crucial|point|topic)\b"
+        text = re.sub(keywords_pattern, insert_thinking_pause, text, flags=re.IGNORECASE)
+
+        conj_pattern = r"\b(and|but|so|because|however)\b"
+        text = re.sub(conj_pattern, lambda m: f"{m.group()}...", text, flags=re.IGNORECASE)
+
+    # 8) Remove random fillers
+    text = re.sub(r"\b(uh|um|ah)\b", "", text, flags=re.IGNORECASE)
+
+    # 9) Capitalize sentence starts
+    def capitalize_match(m):
+        return m.group().upper()
+    text = re.sub(r'(^\s*\w)|([.!?]\s*\w)', capitalize_match, text)
+
+    return text.strip()
+
+
+def _spell_digits(d: str) -> str:
+    """
+    Convert individual digits '3' -> 'three'.
+    """
+    digit_map = {
+        '0': 'zero',
+        '1': 'one',
+        '2': 'two',
+        '3': 'three',
+        '4': 'four',
+        '5': 'five',
+        '6': 'six',
+        '7': 'seven',
+        '8': 'eight',
+        '9': 'nine'
+    }
+    return " ".join(digit_map[ch] for ch in d if ch in digit_map)
 
+def mix_with_bg_music(spoken: AudioSegment, custom_music_path=None) -> AudioSegment:
+    """
+    Mixes 'spoken' with a default bg_music.mp3 or user-provided custom music:
+    1) Start with 2 seconds of music alone before speech begins.
+    2) Loop the music if it's shorter than the final audio length.
+    3) Lower music volume so the speech is clear.
+    """
+    if custom_music_path:
+        music_path = custom_music_path
+    else:
+        music_path = "bg_music.mp3"
 
-if response.status_code!=200:
+    try:
+        bg_music = AudioSegment.from_file(music_path, format="mp3")
+    except Exception as e:
+        print("[ERROR] Failed to load background music:", e)
+        return spoken
 
-raise ValueError(f"Deepgram TTS error:{response.status_code},{response.text}")
+    bg_music = bg_music - 18.0
 
-content_type=response.headers.get('Content-Type','')
+    total_length_ms = len(spoken) + 2000
+    looped_music = AudioSegment.empty()
+    while len(looped_music) < total_length_ms:
+        looped_music += bg_music
 
-if 'audio/mpeg' not in content_type:
+    looped_music = looped_music[:total_length_ms]
+    final_mix = looped_music.overlay(spoken, position=2000)
+    return final_mix
 
-raise ValueError("Unexpected Content-Type from Deepgram.")
+# This function is new for short Q&A calls
+def call_groq_api_for_qa(system_prompt: str) -> str:
+    """
+    A minimal placeholder for your short Q&A LLM call.
+    Must return a JSON string, e.g.:
+    {"speaker": "John", "text": "Short answer here"}
+    """
+    groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+    try:
+        response = groq_client.chat.completions.create(
+            messages=[{"role": "system", "content": system_prompt}],
+            model="llama-3.3-70b-versatile",
+            max_tokens=512,
+            temperature=0.7
+        )
+    except Exception as e:
+        print("[ERROR] Groq API error:", e)
+        fallback = {"speaker": "John", "text": "I'm sorry, I'm having trouble answering right now."}
+        return json.dumps(fallback)
 
-with tempfile.NamedTemporaryFile(delete=False,suffix=".mp3
+    raw_content = response.choices[0].message.content.strip()
+    return raw_content
+