Update app.py

app.py

@@ -12,7 +12,7 @@ nltk.download('stopwords')
 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-id-en")
 terjemah = pipeline("translation", model="Helsinki-NLP/opus-mt-en-id")
 pipe1 = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-pipe2 = pipeline("text-generation", model="SumayyaAli/tiny-llama-1.1b-chat-medical")
+#pipe2 = pipeline("text-generation", model="SumayyaAli/tiny-llama-1.1b-chat-medical")
 
 # Pra-pemrosesan teks menggunakan NLTK
 def pra_pemrosesan_teks(teks):
@@ -31,7 +31,7 @@ jenis_kelamin = st.selectbox("Masukkan jenis kelamin Anda:", ["Laki-laki", "Pere
 gejala_id = st.text_area("Masukkan gejala Anda:")
 
 if st.button("Diagnosa"):
-    if gejala_id and usia and jenis_kelamin:
+    if gejala_id:
         # Terjemahkan gejala dari Indonesia ke Inggris
         terjemahan = translator(gejala_id, max_length=100)
         gejala_en = terjemahan[0]["translation_text"]
@@ -40,7 +40,20 @@ if st.button("Diagnosa"):
 
         # Pesan untuk model
         pesan = [
-            {"role": "system", "content": "You are a doctor who needs to diagnose a patient's illness. Provide one diagnosis that you believe is most confident."},
+            {"role": "system",
+     "content": """
+You are a doctor diagnosing patients based on symptoms.
+
+Example 1:
+Patient symptoms: High fever, headache, nausea
+Diagnosis: Dengue fever
+
+Example 2:
+Patient symptoms: Shortness of breath, chest pain, cough with phlegm
+Diagnosis: Pneumonia.
+
+
+"""},
             {"role": "user", "content": f"Based on your assessment, {gabungan}, what illness could it be?"}
         ]
 
@@ -54,18 +67,18 @@ if st.button("Diagnosa"):
         asisten_konten1 = get_assistant_content(response1)
 
         # Dapatkan respon dari pipe2 (model SumayyaAli)
-        response2 = pipe2(pesan, num_return_sequences=1, truncation=True)
-        asisten_konten2 = get_assistant_content(response2)
+        #response2 = pipe2(pesan, num_return_sequences=1, truncation=True)
+        #asisten_konten2 = get_assistant_content(response2)
 
         # Gabungkan hasil dari pipe1 dan pipe2 untuk pertanyaan akhir
-        pertanyaan_akhir = [{'role': 'user', 'content': f"{asisten_konten1}. {asisten_konten2}. Based on these two sentences, what is your final conclusion of my current symptom? Please provide a brief answer with one diagnosis."}]
+        #pertanyaan_akhir = [{'role': 'user', 'content': f"{asisten_konten1}. {asisten_konten2}. Based on these two sentences, what is your final conclusion of my current symptom? Please provide a brief answer with one diagnosis."}]
 
         # Dapatkan hasil akhir diagnosis
-        hasil_diagnosis = pipe1(pertanyaan_akhir)
-        asisten_konten3 = get_assistant_content(hasil_diagnosis)
+        #hasil_diagnosis = pipe1(pertanyaan_akhir)
+        #asisten_konten3 = get_assistant_content(hasil_diagnosis)
 
         # Terjemahkan hasil akhir ke bahasa Indonesia
-        terjemahan_hasil = terjamah(asisten_konten3, max_length=100)
+        terjemahan_hasil = terjamah(asisten_konten1, max_length=100)
         diagnosa_terjemahan = terjemahan_hasil[0]["translation_text"]
 
         # Tampilkan hasil ke Streamlit