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import torch
from evo_model import EvoTransformer
from transformers import AutoTokenizer
from rag_utils import extract_text_from_file
from search_utils import web_search
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
def load_model(model_path="evo_hellaswag.pt", device=None):
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
model = EvoTransformer()
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)
model.eval()
return model, device
evo_model, device = load_model()
def get_evo_response(query, file=None, enable_search=True):
context = ""
if file:
try:
context += extract_text_from_file(file)[:800]
except:
pass
if enable_search:
search_snippets = web_search(query)
context += "\n".join(search_snippets)
combined_prompt = f"{query}\nContext:\n{context}"
inputs = [
f"{combined_prompt} Option 1:",
f"{combined_prompt} Option 2:",
]
encoded = tokenizer(inputs, padding=True, truncation=True, return_tensors="pt").to(device)
with torch.no_grad():
outputs = evo_model(encoded["input_ids"]).squeeze(-1)
probs = torch.softmax(outputs, dim=0)
best = torch.argmax(probs).item()
return f"Option {best + 1} with {probs[best]:.2%} confidence.\n\nReasoning:\n{inputs[best]}"
def get_gpt_response(query, context=""):
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
prompt = f"{query}\nContext:\n{context}\nGive a thoughtful recommendation with reasons."
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
max_tokens=300,
temperature=0.7,
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"Error: {str(e)}"
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