Update app.py

app.py

@@ -5,7 +5,9 @@ import torch
 import random
 import os
 import numpy as np
+import subprocess
 
+import requests
 
 random.seed(4)
 np.random.seed(4)
@@ -13,34 +15,16 @@ torch.manual_seed(4)
 np.random.seed(4)
 
 
-from fastapi import FastAPI, HTTPException
-import os
-from starlette.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-import uvicorn
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-rob_chem_model = ClassificationModel('roberta', 'seyonec/SMILES_tokenized_PubChem_shard00_160k',use_cuda=False ,args={'evaluate_each_epoch':True , 'evaluate_during_training_verbose':True, 'seed':4})
+def start_fastapi():
+    subprocess.Popen(["uvicorn", "fastapi_app:app", "--host", "0.0.0.0", "--port", "5566"])
+
+# Start the FastAPI server
+start_fastapi()
+
+
+
 
-class Query(BaseModel):
-    query :str
 
-@app.post("/ToxicityPrediction")
-async def c(query:Query):
-    try:
-        predictions, raw_outputs = rob_chem_model.predict([str(query.query)])
-        st.write("Received request")
-        return {"prediction":predictions[0]}
-    except Exception as e:
-        raise HTTPException(detail = str(e) , status_code = 500)
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=5566)
 
 # Set the Streamlit app title
 st.title("Molecule Toxicity Predictions")
@@ -50,9 +34,9 @@ path = 'ToxicityPrediction/Models/transformers/checkpoint-149-epoch-1'
 
 # Load the model from the stage
 #loaded_model = ClassificationModel('roberta', path, use_cuda = False)
-#rob_chem_model = ClassificationModel('roberta', 'seyonec/SMILES_tokenized_PubChem_shard00_160k',use_cuda=False ,args={'evaluate_each_epoch':True , 'evaluate_during_training_verbose':True, 'seed':4})
+#----rob_chem_model = ClassificationModel('roberta', 'seyonec/SMILES_tokenized_PubChem_shard00_160k',use_cuda=False ,args={'evaluate_each_epoch':True , 'evaluate_during_training_verbose':True, 'seed':4})
 # Predict based on the input
-rob_chem_model.model.eval()
+#rob_chem_model.model.eval() ----
 #target_name= st.text_input('Enter a SMILES string:')
 target_name = st.text_area("Enter smiles (one per line):", "")