ariankhalfani commited on
Commit
ab8ff28
1 Parent(s): b4df677

Update chatbot.py

Browse files
Files changed (1) hide show
  1. chatbot.py +6 -3
chatbot.py CHANGED
@@ -211,7 +211,6 @@ def cleanup_response(response):
211
  response = response[answer_start + len("Answer:"):].strip()
212
  return response
213
 
214
- # Gradio interface for the chatbot
215
  def chatbot(audio, input_type, text):
216
  if input_type == "Voice":
217
  transcription = query_whisper(audio.name)
@@ -221,15 +220,20 @@ def chatbot(audio, input_type, text):
221
  else:
222
  query = text
223
 
 
224
  details = extract_details_from_prompt(query)
 
 
225
  patient_history = get_aggregated_patient_history(patient_data, details)
226
 
 
227
  payload = {
228
  "inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {query}"
229
  }
230
 
231
  logging.debug(f"Raw input to the LLM: {payload['inputs']}")
232
 
 
233
  response = query_huggingface(payload)
234
  if isinstance(response, list):
235
  raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
@@ -238,8 +242,7 @@ def chatbot(audio, input_type, text):
238
 
239
  logging.debug(f"Raw output from the LLM: {raw_response}")
240
 
241
- clean_response = cleanup_response(raw_response)
242
- return clean_response, None
243
 
244
  # Gradio interface for generating voice response
245
  def generate_voice_response(tts_model, text_response):
 
211
  response = response[answer_start + len("Answer:"):].strip()
212
  return response
213
 
 
214
  def chatbot(audio, input_type, text):
215
  if input_type == "Voice":
216
  transcription = query_whisper(audio.name)
 
220
  else:
221
  query = text
222
 
223
+ # Extract details from the prompt
224
  details = extract_details_from_prompt(query)
225
+
226
+ # Get aggregated patient history based on the extracted details
227
  patient_history = get_aggregated_patient_history(patient_data, details)
228
 
229
+ # Create the payload with the patient history and the user's query
230
  payload = {
231
  "inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {query}"
232
  }
233
 
234
  logging.debug(f"Raw input to the LLM: {payload['inputs']}")
235
 
236
+ # Query the Hugging Face model with the payload
237
  response = query_huggingface(payload)
238
  if isinstance(response, list):
239
  raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
 
242
 
243
  logging.debug(f"Raw output from the LLM: {raw_response}")
244
 
245
+ return raw_response, None
 
246
 
247
  # Gradio interface for generating voice response
248
  def generate_voice_response(tts_model, text_response):