initial commit

requirements.txt

@@ -1,3 +1,8 @@
 altair
 pandas
-streamlit
+streamlit
+torch
+transformers
+sentence-transformers
+datasets
+openai

src/requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+openai>=1.2.0
+sentence-transformers
+torch

src/runtime.txt

@@ -0,0 +1 @@
+python-3.10.12

src/streamlit_app.py

@@ -1,40 +1,145 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+
+# ✅ Set all relevant cache directories to a writable location
+os.environ["HF_HOME"] = "/tmp/cache"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/cache/transformers"
+os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/cache/sentence_transformers"
+os.environ["HF_DATASETS_CACHE"] = "/tmp/cache/hf_datasets"
+os.environ["TORCH_HOME"] = "/tmp/cache/torch"
+
+# ✅ Create the directories if they don't exist
+for path in [
+    "/tmp/cache",
+    "/tmp/cache/transformers",
+    "/tmp/cache/sentence_transformers",
+    "/tmp/cache/hf_datasets",
+    "/tmp/cache/torch"
+]:
+    os.makedirs(path, exist_ok=True)
+import json
+import torch
+import openai
+import os
+from sentence_transformers import SentenceTransformer, util
 import streamlit as st
+from pathlib import Path
+
+# === CONFIG ===
+# Set the API key
+client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+#openai.api_key = os.getenv("OPENAI_API_KEY")
+# REMEDI_PATH = "ReMeDi-base.json"
+BASE_DIR = Path(__file__).parent
+REMEDI_PATH = BASE_DIR / "ReMeDi-base.json"
+
+# Check if file exists
+if not REMEDI_PATH.exists():
+    raise FileNotFoundError(f"❌ File not found: {REMEDI_PATH}")
+
+# Load the file
+with open(REMEDI_PATH, "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+# === LOAD MODEL ===
+@st.cache_resource
+def load_model():
+    return SentenceTransformer("all-MiniLM-L6-v2")
+    #return model
+
+@st.cache_resource
+def load_data():
+    with open(REMEDI_PATH, "r", encoding="utf-8") as f:
+        data = json.load(f)
+    dialogue_pairs = []
+    for conversation in data:
+        turns = conversation["information"]
+        for i in range(len(turns)-1):
+            if turns[i]["role"] == "patient" and turns[i+1]["role"] == "doctor":
+                dialogue_pairs.append({
+                    "patient": turns[i]["sentence"],
+                    "doctor": turns[i+1]["sentence"]
+                })
+    return dialogue_pairs
+
+@st.cache_data
+def build_embeddings(dialogue_pairs, _model):
+    patient_sentences = [pair["patient"] for pair in dialogue_pairs]
+    embeddings = _model.encode(patient_sentences, convert_to_tensor=True)
+    return embeddings
+
+# === TRANSLATE USING GPT ===
+def translate_to_english(chinese_text):
+    prompt = f"Translate the following Chinese medical response to English:\n\n{chinese_text}"
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.2
+        )
+        return response.choices[0].message.content
+
+        #return response.choices[0].message["content"].strip()
+    except Exception as e:
+        return f"Translation failed: {str(e)}"
+
+def gpt_direct_response(user_input):
+    prompt = f"You are a knowledgeable and compassionate medical assistant. Answer the following patient question clearly and concisely:\n\n{user_input}"
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4",  # or "gpt-3.5-turbo" to save credits
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.5
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"GPT response failed: {str(e)}"
+
+
+# === CHATBOT FUNCTION ===
+def chatbot_response(user_input, _model, dialogue_pairs, patient_embeddings, top_k=1):
+    user_embedding = _model.encode(user_input, convert_to_tensor=True)
+    similarities = util.cos_sim(user_embedding, patient_embeddings)[0]
+    top_idx = torch.topk(similarities, k=top_k).indices[0].item()
+
+    match = dialogue_pairs[top_idx]
+    translated = translate_to_english(match["doctor"])
+
+    return {
+        "matched_question": match["patient"],
+        "original_response": match["doctor"],
+        "translated_response": translated
+    }
+
+# === MAIN APP ===
+st.set_page_config(page_title="Dr_Q_bot", layout="centered")
+st.title("🩺 Dr_Q_bot - Medical Chatbot")
+st.write("Ask about a symptom and get an example doctor response (translated from Chinese).")
+
+# Load resources
+model = load_model()
+dialogue_pairs = load_data()
+patient_embeddings = build_embeddings(dialogue_pairs, model)
+
+# Chat UI
+user_input = st.text_input("Describe your symptom:")
+
+if st.button("Submit") and user_input:
+    with st.spinner("Thinking..."):
+        result = chatbot_response(user_input, model, dialogue_pairs, patient_embeddings)
+        gpt_response = gpt_direct_response(user_input)
+
+        st.markdown("### 🧑‍⚕️ Closest Patient Question")
+        st.write(result["matched_question"])
+
+        st.markdown("### 🇨🇳 Original Doctor Response (Chinese)")
+        st.write(result["original_response"])
+
+        st.markdown("### 🌐 Translated Doctor Response (English)")
+        st.success(result["translated_response"])
+
+        st.markdown("### 💬 GPT Doctor Response (AI-generated)")
+        st.info(gpt_response)
+
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        st.markdown("---")
+        st.warning("This chatbot uses real dialogue data for research and educational use only. Not a substitute for professional medical advice.")