Spaces:
Sleeping
Sleeping
File size: 3,736 Bytes
6d79528 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
import os
import gradio as gr
from fastapi import FastAPI, HTTPException
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import uvicorn
# ✅ Load Model Configuration
MODEL_NAME = "hpyapali/tinyllama-workout"
HF_TOKEN = os.getenv("HF_TOKEN", "your_huggingface_api_key") # Replace with your actual Hugging Face API key
app = FastAPI()
try:
print("🔄 Loading Model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, token=HF_TOKEN)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
print("✅ Model Loaded Successfully!")
except Exception as e:
print(f"❌ Error loading model: {e}")
pipe = None
# ✅ AI Function - Generates Structured Workout Recommendations
def recommend_next_workout(last_workouts: str):
"""
Analyzes and ranks workouts based on intensity and heart rate drop.
Provides a recommendation for the next workout.
"""
if pipe is None:
return "❌ AI model not loaded."
instruction = (
"You are a fitness AI assistant specializing in analyzing workout effectiveness. "
"Based on the last 7 workouts, rank them from the most to least effective based on:\n"
"- Heart rate drop after workout (faster drop = better recovery)\n"
"- Workout intensity (higher effort = more impact)\n"
"- Duration (longer workouts generally contribute more)\n"
"- Calories burned (higher calories = higher impact)\n"
"- Variability (mixing workout types is important)\n\n"
"### Last 7 Workouts:\n"
)
full_prompt = instruction + last_workouts + "\n\n### Ranking (Best to Least Effective):\n"
try:
print(f"🧐 AI Processing: {full_prompt}")
result = pipe(
full_prompt,
max_new_tokens=150, # 🔼 Increased token limit for full ranking
do_sample=True, # 🔼 Enabled sampling for variability
temperature=0.7, # 🔼 Slight randomness for better insights
top_p=0.9 # 🔼 Limits unlikely outputs while keeping diversity
)
print(f"🔍 Raw AI Output: {result}")
if not result or not result[0]["generated_text"].strip():
return "❌ AI did not generate any output."
response_text = result[0]["generated_text"].strip()
# ✅ Remove repeated prompt if AI echoes it
if full_prompt in response_text:
response_text = response_text.replace(full_prompt, "").strip()
print(f"✅ AI Recommendation: {response_text}")
return response_text
except Exception as e:
print(f"❌ AI Processing Error: {e}")
return "❌ Error generating workout recommendation."
# ✅ FastAPI Route - Returns AI Response Directly
@app.post("/gradio_api/call/predict")
async def predict(data: dict):
try:
last_workouts = data.get("data", [""])[0]
if not last_workouts:
raise HTTPException(status_code=400, detail="Invalid input")
ai_response = recommend_next_workout(last_workouts)
return {"data": [ai_response]} # ✅ Directly returning structured response
except Exception as e:
return {"error": str(e)}
# ✅ Gradio UI (Optional for Testing)
iface = gr.Interface(
fn=recommend_next_workout,
inputs="text",
outputs="text",
title="TinyLlama Workout Recommendations",
description="Enter workout data to receive AI-powered recommendations."
)
# ✅ Ensure Proper Gradio Launch
iface.launch(server_name="0.0.0.0", server_port=7860)
# ✅ FastAPI Server Execution
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
|