Update health_tracker_agent.py

health_tracker_agent.py

@@ -1,38 +1,11 @@
-import pandas as pd
-import openai
-
-health_data = {
-    "blood_pressure": [],
-    "weight": [],
-    "heart_rate": []
-}
-
-def log_symptom(symptom: str, value: str):
-    if symptom not in health_data:
-        health_data[symptom] = []
-    health_data[symptom].append(value)
-    return f"Symptom {symptom} with value {value} logged successfully."
-
-def get_health_report():
-    trend_data = pd.DataFrame({
-        "Date": ["2023-04-01", "2023-04-05", "2023-04-10"],
-        "Blood Pressure": ["130/85", "135/88", "140/90"],
-        "Heart Rate": [72, 75, 78]
-    })
-    return trend_data
-
-def track_health_stat(stat_type: str):
-    if stat_type in health_data:
-        return health_data[stat_type]
-    return "Stat type not found."
-
-def personalized_health_advice(symptom: str, value: str = ""):
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[{
-            "role": "user",
-            "content": f"Provide personalized health advice for someone experiencing {symptom} with severity: {value}."
-        }],
-        max_tokens=150
-    )
-    return response['choices'][0]['message']['content'].strip()
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+def track_health_status(health_input):
+    model_name = "m42-health/med42-70b"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    prompt = f"Based on the following health input, track possible issues or advice: {health_input}."
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=150)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)