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ariankhalfani
commited on
Commit
•
ab8ff28
1
Parent(s):
b4df677
Update chatbot.py
Browse files- chatbot.py +6 -3
chatbot.py
CHANGED
@@ -211,7 +211,6 @@ def cleanup_response(response):
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response = response[answer_start + len("Answer:"):].strip()
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return response
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-
# Gradio interface for the chatbot
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def chatbot(audio, input_type, text):
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if input_type == "Voice":
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transcription = query_whisper(audio.name)
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@@ -221,15 +220,20 @@ def chatbot(audio, input_type, text):
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else:
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query = text
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details = extract_details_from_prompt(query)
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patient_history = get_aggregated_patient_history(patient_data, details)
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payload = {
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"inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {query}"
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}
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logging.debug(f"Raw input to the LLM: {payload['inputs']}")
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response = query_huggingface(payload)
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if isinstance(response, list):
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raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
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@@ -238,8 +242,7 @@ def chatbot(audio, input_type, text):
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logging.debug(f"Raw output from the LLM: {raw_response}")
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return clean_response, None
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# Gradio interface for generating voice response
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def generate_voice_response(tts_model, text_response):
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response = response[answer_start + len("Answer:"):].strip()
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return response
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def chatbot(audio, input_type, text):
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if input_type == "Voice":
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transcription = query_whisper(audio.name)
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else:
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query = text
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# Extract details from the prompt
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details = extract_details_from_prompt(query)
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+
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# Get aggregated patient history based on the extracted details
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patient_history = get_aggregated_patient_history(patient_data, details)
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# Create the payload with the patient history and the user's query
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payload = {
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"inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {query}"
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}
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logging.debug(f"Raw input to the LLM: {payload['inputs']}")
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# Query the Hugging Face model with the payload
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response = query_huggingface(payload)
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if isinstance(response, list):
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raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
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logging.debug(f"Raw output from the LLM: {raw_response}")
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return raw_response, None
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# Gradio interface for generating voice response
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def generate_voice_response(tts_model, text_response):
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