start changes

lens_app/core.py

@@ -1,17 +1,17 @@
-
 import json
 import os
 from datetime import datetime
 
-import markdown
-
+import pypandoc
 from bs4 import BeautifulSoup
 from dotenv import load_dotenv
 from fhirpathpy import evaluate
 from groq import Groq
 from ollama import Client
 from openai import OpenAI
 
+from lens_app.prompts import PROMPT_LIST
+
 load_dotenv()
 
 SERVER_URL = os.getenv("SERVER_URL")
@@ -176,7 +176,7 @@ def transform_fhir_epi(epi):
 
 
 def format_response(res):
-    # print("Raw response: " + res)
+    print("Raw response: " + res)
 
     # response_markdown = res.strip()
 
@@ -197,18 +197,12 @@ def summarize_no_personalization(language, epi, model):
 
     lang = LANGUAGE_MAP[language]
     print("++++" * 40, lang)
-    prompt = (
-        "Write a concise summary of the following text delimited by triple backquotes. Return your response in bullet points which covers the key points of the text. "
-        + "You must only use the information provided in the text and avoid introducing any new information. "
-        + "The summary should be clear, accurate, and comprehensive, highlighting the main ideas and key details of the text. "
-        + "It is of extreme importance that you summarize the document in "
-        + lang
-        + " and this is totally mandatory. Otherwise the reader will not understand. \n"
-        + "```"
-        + epi_text
-        + "```"
-        + "\nBULLET POINT SUMMARY:"
-    )
+    prompt = PROMPT_LIST[lang]
+
+    prompt += "```"
+    +epi_text
+    +"```"
+    +"\nBULLET POINT SUMMARY:"
     print("the prompt will be:" + prompt)
 
     systemMessage = (
@@ -419,7 +413,7 @@ def summarize2(
             ],
             stream=False,
             keep_alive="-1m",
-            options={"seed": 1234, "temperature": 0}
+            options={"seed": 1234, "temperature": 0},
         )
 
         response = format_response(result["message"]["content"])
@@ -489,3 +483,67 @@ def summarize2(
         "model": model,
         "lens": "summarize2",
     }
+
+
+def summarize3(language, epi, gender, age, diagnostics, medications, model):
+    epi_text = transform_fhir_epi(epi)
+
+    lang = LANGUAGE_MAP[language]
+    print("++++" * 40, lang)
+    prompt = (
+        "Write a concise summary of the following text delimited by triple backquotes. Return the 5 most important questions about it and answer them. "
+        + "You must only use the information provided in the text and avoid introducing any new information. "
+        + "It is of extreme importance that you summarize the document in "
+        + lang
+        + " and this is totally mandatory. Otherwise the reader will not understand. \n"
+        + "It should be written in a way that a person of gender: "
+        + gender
+        + " and with "
+        + str(age)
+        + " of age should be able to understand."
+        "You must reference highlight something that relates with the following topics and terms:"
+        + "|".join(diagnostics)
+        + "|".join(medications)
+        + "```"
+        + epi_text
+        + "```"
+        + "\nBULLET POINT SUMMARY:"
+    )
+    print("the prompt will be:" + prompt)
+
+    if model in [
+        "llama-3.1-70b-Versatile",
+        "Mixtral-8x7b-32768",
+        "Llama3-70b-8192",
+        "Llama3-8b-8192",
+        "Llama-3.2-90b-Text-Preview",
+    ]:
+        chat_completion = groqclient.chat.completions.create(
+            messages=[
+                {"role": "user", "content": prompt},
+            ],
+            model=model,
+            temperature=0.05,
+        )
+
+        response = format_response(chat_completion.choices[0].message.content)
+    else:
+        result = client.chat(
+            model=model,
+            messages=[
+                {"content": prompt, "role": "assistant"},
+            ],
+            stream=False,
+            keep_alive="-1m",
+            options={"seed": 1234, "temperature": 0},
+        )
+
+        response = format_response(result["message"]["content"])
+
+    return {
+        "response": response,
+        "prompt": prompt,
+        "datetime": datetime.now(),
+        "model": model,
+        "lens": "summarize",
+    }

lens_app/prompts.py

@@ -0,0 +1,11 @@
+PROMPT_LIST = {
+    "en": "Write a concise summary of the following text delimited by triple backquotes. Return your response in bullet points which covers the key points of the text. "
+    + "You must only use the information provided in the text and avoid introducing any new information. "
+    + "The summary should be clear, accurate, and comprehensive, highlighting the main ideas and key details of the text.  \n",
+    "es": "Escribe un resumen conciso del siguiente texto delimitado por comillas triples. Devuelve tu respuesta en viñetas que cubran los puntos clave del texto. "
+    + "Debes usar únicamente la información proporcionada en el texto y evitar introducir información nueva. "
+    + "El resumen debe ser claro, preciso y completo, destacando las ideas principales y los detalles clave del texto. \n",
+    "da": "Skriv et kortfattet resumé af den følgende tekst, afgrænset af tredobbelte anførselstegn. Returner dit svar i punktform, der dækker tekstens vigtigste punkter."
+    + "Du må kun bruge de oplysninger, der er givet i teksten, og undgå at tilføje ny information."
+    + "Resuméet skal være klart, præcist og omfattende og fremhæve tekstens hovedideer og nøgleoplysninger. \n",
+}

lens_app/views.py

@@ -12,6 +12,7 @@
     process_ips,
     summarize,
     summarize2,
+    summarize3,
     summarize_no_personalization,
 )
 
@@ -47,6 +48,18 @@ def lens_app(bundleid=None):
     lenses = request.args.get("lenses", "")
     patientIdentifier = request.args.get("patientIdentifier", "")
     model = request.args.get("model", "")
+    if model not in [
+        "graviting-llama",
+        "mistral",
+        "llama3",
+        "llama3.1",
+        "llama-3.1-70b-Versatile",
+        "Mixtral-8x7b-32768",
+        "Llama3-70b-8192",
+        "Llama3-8b-8192",
+        "Llama-3.2-90b-Text-Preview",
+    ]:
+        return "Error: Unknown Model", 404
     print(lenses, patientIdentifier)
     if lenses not in ["lens-summary", "lens-summary-2"]:
         return "Error: lens not supported", 404
@@ -95,6 +108,7 @@ def lens_app(bundleid=None):
 
     # print(language, epi, gender, age, diagnostics, medications)
     if ips is None:
+        print("NO IPS")
         if model not in [
             "mistral",
             "llama3",
@@ -109,10 +123,23 @@ def lens_app(bundleid=None):
         else:
             response = summarize_no_personalization(language, epi, model)
     elif lenses == "lens-summary":
+        model = "llama3.1" if model == "graviting-llama" else model
         response = summarize(
             language, epi, gender, age, diagnostics, medications, model
         )
     elif lenses == "lens-summary-2":
+        model = "llama3.1" if model == "graviting-llama" else model
+        # to prevent change on the app 19/12/2024
+        response = summarize(
+            language, epi, gender, age, diagnostics, medications, model
+        )
+    elif lenses == "lens-summary-3":
+        model = "llama3.1" if model == "graviting-llama" else model
+        response = summarize3(
+            language, epi, gender, age, diagnostics, medications, model
+        )
+    elif lenses == "lens-summary-2-2":
+        model = "llama3.1" if model == "graviting-llama" else model
         response = summarize2(
             language, drug_name, gender, age, diagnostics, medications, model
         )

tests/test.py

@@ -14,12 +14,12 @@
 HOST = "http://localhost:5005/summary/"
 # HOST = "http://gravitate-health.lst.tfo.upm.es/ai/summary/"
 
-for e in EPI:
-    myreq = HOST + e + "?lenses=lens-summary-2&model=graviting-llama"
-    print(myreq)
-    response = requests.get(myreq)
-    print("ePI", e, response.status_code)
-    print(response.json())
+# for e in EPI:
+##    myreq = HOST + e + "?lenses=lens-summary-2&model=graviting-llama"
+#   print(myreq)
+##   response = requests.get(myreq)
+#   print("ePI", e, response.status_code)
+#  print(response.json())
 
 for p in PAT:
     for e in EPI: