Update app.py

app.py

@@ -257,53 +257,7 @@ chain_neo4j = (
     | StrOutputParser()
 )
 
-# Define a function to select between Pinecone and Neo4j
-# def generate_answer(message, choice, retrieval_mode):
-#     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
-
-#     prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-
-#     if retrieval_mode == "VDB":
-#         qa_chain = RetrievalQA.from_chain_type(
-#             llm=chat_model,
-#             chain_type="stuff",
-#             retriever=retriever,
-#             chain_type_kwargs={"prompt": prompt_template}
-#         )
-#         response = qa_chain({"query": message})
-#         logging.debug(f"Vector response: {response}")
-#         return response['result'], extract_addresses(response['result'])
-#     elif retrieval_mode == "KGF":
-#         response = chain_neo4j.invoke({"question": message})
-#         logging.debug(f"Knowledge-Graph response: {response}")
-#         return response, extract_addresses(response)
-#     else:
-#         return "Invalid retrieval mode selected.", []
-
-# def bot(history, choice, tts_choice, retrieval_mode):
-#     if not history:
-#         return history
-    
-#     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
-#     history[-1][1] = ""
-
-#     with concurrent.futures.ThreadPoolExecutor() as executor:
-#         if tts_choice == "Alpha":
-#             audio_future = executor.submit(generate_audio_elevenlabs, response)
-#         elif tts_choice == "Beta":
-#             audio_future = executor.submit(generate_audio_parler_tts, response)
-#         elif tts_choice == "Gamma":
-#             audio_future = executor.submit(generate_audio_mars5, response)
-
-#         for character in response:
-#             history[-1][1] += character
-#             time.sleep(0.05)
-#             yield history, None
-
-#         audio_path = audio_future.result()
-#         yield history, audio_path
 
-#     history.append([response, None])  # Ensure the response is added in the correct format
 
 
 
@@ -354,38 +308,7 @@ def generate_answer(message, choice, retrieval_mode):
 
 
 
-# def bot(history, choice, tts_choice, retrieval_mode):
-#     if not history:
-#         return history
-    
-#     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
-#     history[-1][1] = ""
-
-#     # Detect if the response is from Yelp (i.e., HTML formatted response)
-#     if "<table>" in response:
-#         for chunk in response.splitlines():
-#             history[-1][1] += chunk + "\n"
-#             time.sleep(0.1)  # Adjust the delay as needed
-#             yield history, None
-#         return
-
-#     with concurrent.futures.ThreadPoolExecutor() as executor:
-#         if tts_choice == "Alpha":
-#             audio_future = executor.submit(generate_audio_elevenlabs, response)
-#         elif tts_choice == "Beta":
-#             audio_future = executor.submit(generate_audio_parler_tts, response)
-#         elif tts_choice == "Gamma":
-#             audio_future = executor.submit(generate_audio_mars5, response)
-
-#         for character in response:
-#             history[-1][1] += character
-#             time.sleep(0.05)
-#             yield history, None
-
-#         audio_path = audio_future.result()
-#         yield history, audio_path
-
-#     history.append([response, None])  # Ensure the response is added in the correct format
+
 
 def bot(history, choice, tts_choice, retrieval_mode):
     if not history:
@@ -603,44 +526,7 @@ def show_map_if_details(history, choice):
     else:
         return gr.update(visible(False), "")
 
-# def generate_audio_elevenlabs(text):
-#     XI_API_KEY = os.environ['ELEVENLABS_API']
-#     VOICE_ID = 'd9MIrwLnvDeH7aZb61E9'
-#     tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream"
-#     headers = {
-#         "Accept": "application/json",
-#         "xi-api-key": XI_API_KEY
-#     }
-#     data = {
-#         "text": str(text),
-#         "model_id": "eleven_multilingual_v2",
-#         "voice_settings": {
-#             "stability": 1.0,
-#             "similarity_boost": 0.0,
-#             "style": 0.60,
-#             "use_speaker_boost": False
-#         }
-#     }
-#     response = requests.post(tts_url, headers=headers, json=data, stream=True)
-#     if response.ok:
-#         audio_segments = []
-#         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
-#             for chunk in response.iter_content(chunk_size=1024):
-#                 if chunk:
-#                     f.write(chunk)
-#                     audio_segments.append(chunk)
-#             temp_audio_path = f.name
-
-#         # Combine all audio chunks into a single file
-#         combined_audio = AudioSegment.from_file(temp_audio_path, format="mp3")
-#         combined_audio_path = os.path.join(tempfile.gettempdir(), "elevenlabs_combined_audio.mp3")
-#         combined_audio.export(combined_audio_path, format="mp3")
-
-#         logging.debug(f"Audio saved to {combined_audio_path}")
-#         return combined_audio_path
-#     else:
-#         logging.error(f"Error generating audio: {response.text}")
-#         return None
+
 
 
 def preprocess_for_tts(text):