Update chatbot.py

chatbot.py

@@ -167,12 +167,8 @@ readable_patient_data = transform_patient_data(patient_data)
 # Function to extract details from the input prompt
 def extract_details_from_prompt(prompt):
     pattern = re.compile(r"(Glaucoma|Cataract) (\d+)", re.IGNORECASE)
-    match = pattern.search(prompt)
-    if match:
-        condition = match.group(1).capitalize()
-        patient_id = int(match.group(2))
-        return condition, patient_id
-    return None, None
+    matches = pattern.findall(prompt)
+    return [(match[0].capitalize(), int(match[1])) for match in matches]
 
 # Function to fetch specific patient data based on the condition and ID
 def get_specific_patient_data(patient_data, condition, patient_id):
@@ -191,18 +187,25 @@ def get_specific_patient_data(patient_data, condition, patient_id):
                 break
     return specific_data
 
-# Toggle visibility of input fields based on the selected input type
-def toggle_input_visibility(input_type):
+# Function to aggregate patient history for all mentioned IDs in the question
+def get_aggregated_patient_history(patient_data, details):
+    history = ""
+    for condition, patient_id in details:
+        history += get_specific_patient_data(patient_data, condition, patient_id) + "\n"
+    return history.strip()
+
+# Toggle visibility of input elements based on input type
+def toggle_visibility(input_type):
     if input_type == "Voice":
         return gr.update(visible=True), gr.update(visible=False)
     else:
         return gr.update(visible=False), gr.update(visible=True)
 
-# Function to clean up the response text
+# Cleanup response text
 def cleanup_response(response):
     # Extract only the part after "Answer:" and remove any trailing spaces
     answer_start = response.find("Answer:")
-    if (answer_start != -1):
+    if answer_start != -1:
         response = response[answer_start + len("Answer:"):].strip()
     return response
 
@@ -213,38 +216,23 @@ def chatbot(audio, input_type, text):
         if "error" in transcription:
             return "Error transcribing audio: " + transcription["error"], None
         query = transcription['text']
-        condition, patient_id = extract_details_from_prompt(query)
-        patient_history = ""
-        if condition and patient_id:
-            patient_history = get_specific_patient_data(patient_data, condition, patient_id)
-        payload = {
-            "inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {query}"
-        }
-        response = query_huggingface(payload)
-        if isinstance(response, list):
-            raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
-        else:
-            raw_response = response.get("generated_text", "Sorry, I couldn't generate a response.")
-
-        clean_response = cleanup_response(raw_response)
-        return clean_response, None
-
-    elif input_type == "Text":
-        condition, patient_id = extract_details_from_prompt(text)
-        patient_history = ""
-        if condition and patient_id:
-            patient_history = get_specific_patient_data(patient_data, condition, patient_id)
-        payload = {
-            "inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {text}"
-        }
-        response = query_huggingface(payload)
-        if isinstance(response, list):
-            raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
-        else:
-            raw_response = response.get("generated_text", "Sorry, I couldn't generate a response.")
+    else:
+        query = text
+
+    details = extract_details_from_prompt(query)
+    patient_history = get_aggregated_patient_history(patient_data, details)
+
+    payload = {
+        "inputs": f"role: ophthalmologist assistant patient history: {patient_history} question: {query}"
+    }
+    response = query_huggingface(payload)
+    if isinstance(response, list):
+        raw_response = response[0].get("generated_text", "Sorry, I couldn't generate a response.")
+    else:
+        raw_response = response.get("generated_text", "Sorry, I couldn't generate a response.")
 
-        clean_response = cleanup_response(raw_response)
-        return clean_response, None
+    clean_response = cleanup_response(raw_response)
+    return clean_response, None
 
 # Gradio interface for generating voice response
 def generate_voice_response(tts_model, text_response):