Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import torch
+import random
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 # ---------------- PAGE CONFIG ----------------
@@ -112,13 +113,18 @@ elif st.session_state.page == "main":
             bmi_status = bmi_category(bmi)
             equipment_list = ", ".join(equipment)
 
-            # ---------------- UPDATED PROMPT ----------------
+            # Add randomness token to force variation
+            random_token = random.randint(1, 1000000)
+
             prompt = f"""
 You are a certified professional fitness trainer.
 
+Random Seed: {random_token}
+
 Generate a structured 5-day workout plan based on the following user profile.
 
 User Profile:
+- Name: {name}
 - Gender: {gender}
 - BMI: {bmi:.2f} ({bmi_status})
 - Goal: {goal}
@@ -126,26 +132,15 @@ User Profile:
 - Available Equipment: {equipment_list}
 
 Instructions:
-1. Divide the plan clearly into Day 1, Day 2, Day 3, Day 4, and Day 5.
+1. Divide the plan clearly into Day 1 to Day 5.
 2. Under each day, list 4-6 exercises.
 3. For each exercise include:
    - Exercise Name
    - Sets
    - Reps
    - Rest Time
-4. Keep exercises appropriate for the user’s fitness level.
-5. Avoid medical or dietary advice.
-6. Keep the output clean and structured.
-7. Do NOT include explanations outside the workout plan.
-
-Format Example:
-
-Day 1: Upper Body Strength
-
-1. Push-Ups
-   Sets: 3
-   Reps: 12
-   Rest: 60 seconds
+4. Keep exercises appropriate for fitness level.
+5. Do NOT include explanations outside workout plan.
 
 Only return the workout plan.
 """
@@ -156,12 +151,13 @@ Only return the workout plan.
 
                 outputs = model.generate(
                     **inputs,
-                    max_new_tokens=350,
-                    temperature=0.7,
+                    max_new_tokens=400,
                     do_sample=True,
-                    top_p=0.9,
-                    repetition_penalty=1.8,
-                    no_repeat_ngram_size=3
+                    temperature=0.9,        # Increased randomness
+                    top_p=0.95,
+                    top_k=50,
+                    repetition_penalty=1.5,
+                    num_beams=1             # Important: disables deterministic beam search
                 )
 
                 result = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()