Update app.py

app.py

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+import pandas as pd
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Step 1: Load the dataset (adjust the dataset path if necessary)
+df = pd.read_json("hf://datasets/theprint/mindfulness-alpaca/alpaca_data_export.json")
+
+# Check the dataset structure to understand how the data is formatted
+print(df.head())  # Ensure it contains columns like 'health_issue' or related info
+
+# Step 2: Load the pre-trained BERT model and tokenizer
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Function to predict the health issue based on user input (using BERT)
+def predict_health_issue(question):
+    inputs = tokenizer(question, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=-1).item()
+    return predicted_class
+
+# Step 3: Define a mapping of health issues to suggested cures or workouts
+health_cures = {
+    "fatigue": "Try regular cardio workouts and improving your sleep habits.",
+    "joint pain": "Consider low-impact exercises like swimming or yoga.",
+    "stress": "Meditate regularly and try mindfulness exercises.",
+    "headaches": "Stay hydrated and manage your screen time.",
+    "weight gain": "Focus on a balanced diet and regular exercise like walking or jogging.",
+    "insomnia": "Develop a consistent bedtime routine and try relaxation techniques."
+}
+
+# Step 4: Ask the user a series of health-related questions and process responses
+questions = [
+    "Do you feel constant fatigue?",
+    "Have you been experiencing any joint pain?",
+    "Do you have difficulty sleeping?",
+    "Are you feeling stressed frequently?",
+    "Have you been gaining weight unexpectedly?",
+    "Do you experience frequent headaches?"
+]
+
+responses = []
+
+print("Please answer the following questions to help identify your health issues:")
+
+# Ask the questions and record responses
+for question in questions:
+    response = input(question + " (Yes/No/Other): ")
+    responses.append(response)
+
+# Step 5: Predict health issues and suggest cures
+print("\nHealth Issue Analysis:")
+for response in responses:
+    predicted_issue = predict_health_issue(response)
+    health_issue = df['health_issue'].iloc[predicted_issue]  # Map prediction to a health issue
+    print(f"Predicted Health Issue: {health_issue}")
+
+    if health_issue in health_cures:
+        print(f"Suggested cure for {health_issue}: {health_cures[health_issue]}")
+    else:
+        print(f"No suggestion available for {health_issue}. Please consult a healthcare professional.")
+