Update app.py

app.py

@@ -5,89 +5,94 @@ from fastapi import FastAPI,Response
 from sklearn.metrics import accuracy_score, f1_score
 import prometheus_client as prom
 import pandas as pd
-# from transformers import pipeline
-
+import uvicorn
+from transformers import VisionEncoderDecoderModel,pipeline
+import torch
 
 
 #model
-save_file_name="xgboost-model.pkl"
-loaded_model = pickle.load(open(save_file_name, 'rb'))
 
-app=FastAPI()
+# loaded_model = pickle.load(open(save_file_name, 'rb'))
+
+# app=FastAPI()
+
 
-# username="ashwml"
-# repo_name="prometheus_model"
-# model=username+'/'+repo_name
-test_data=pd.read_csv("test.csv")
+# test_data=pd.read_csv("test.csv")
 
 
-f1_metric = prom.Gauge('death_f1_score', 'F1 score for test samples')
+# f1_metric = prom.Gauge('death_f1_score', 'F1 score for test samples')
 
 # Function for updating metrics
-def update_metrics():
-    test = test_data.sample(20)
-    X = test.iloc[:, :-1].values
-    y = test['DEATH_EVENT'].values
+# def update_metrics():
+#     test = test_data.sample(20)
+#     X = test.iloc[:, :-1].values
+#     y = test['DEATH_EVENT'].values
     
-    # test_text = test['Text'].values
-    test_pred = loaded_model.predict(X)
-    #pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
+#     # test_text = test['Text'].values
+#     test_pred = loaded_model.predict(X)
+#     #pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
+
+#     f1 = f1_score( y , test_pred).round(3)
+
+#     #f1 = f1_score(test['labels'], pred_labels).round(3)
+
+#     f1_metric.set(f1)
 
-    f1 = f1_score( y , test_pred).round(3)
+vitgpt_processor = AutoImageProcessor.from_pretrained("model")
+vitgpt_model = VisionEncoderDecoderModel.from_pretrained("model")
+vitgpt_tokenizer = AutoTokenizer.from_pretrained("model")
 
-    #f1 = f1_score(test['labels'], pred_labels).round(3)
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    f1_metric.set(f1)
+vitgpt_model.to(device)
+
+def generate_caption(processor, model, image, tokenizer=None):
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    
+    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
+
+    if tokenizer is not None:
+        generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    else:
+        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+   
+    return generated_caption
+
+def predict_event(input):
+    
 
 
+    caption_vitgpt = generate_caption(vitgpt_processor, vitgpt_model, image, vitgpt_tokenizer)
 
-def predict_death_event(age,	anaemia,	creatinine_phosphokinase	,diabetes	,ejection_fraction,	high_blood_pressure	,platelets	,serum_creatinine,	serum_sodium,	sex	,smoking	,time):
-   input=[[age,	anaemia,	creatinine_phosphokinase	,diabetes	,ejection_fraction,	high_blood_pressure	,platelets	,serum_creatinine,	serum_sodium,	sex	,smoking	,time]]
-   result=loaded_model.predict(input)
+    return caption_vitgpt
 
-   if result[0]==1:
-      return 'Positive'
-   else:
-      return 'Negative'
-   return result
 
 
-@app.get("/metrics")
-async def get_metrics():
-    update_metrics()
-    return Response(media_type="text/plain", content= prom.generate_latest())
 
+# @app.get("/metrics")
+# async def get_metrics():
+#     update_metrics()
+#     return Response(media_type="text/plain", content= prom.generate_latest())
 
 
-title = "Patient Survival Prediction"
-description = "Predict survival of patient with heart failure, given their clinical record"
 
-out_response = gr.components.Textbox(type="text", label='Death_event')
+title = "capstone"
+description = "final capstone"
 
-iface = gr.Interface(fn=predict_death_event,      
-        inputs=[
-        gr.Slider(18, 100, value=20, label="Age"),
-        gr.Slider(0, 1, value=1, label="anaemia"),
-        gr.Slider(100, 2000, value=20,  label="creatinine_phosphokinase"),
-        gr.Slider(0, 1, value=1, label="diabetes"),
-        gr.Slider(18, 100, value=20, label="ejection_fraction"),
-        gr.Slider(0, 1, value=1, label="high_blood_pressure"),
-        gr.Slider(18, 400000, value=20, label="platelets"),
-        gr.Slider(1, 10, value=20, label="serum_creatinine"),
-        gr.Slider(100, 200, value=20, label="serum_sodium"),
-        gr.Slider(0, 1, value=1, label="sex"),
-        gr.Slider(0, 1, value=1, label="smoking"),
-        gr.Slider(1, 10, value=20, label="time"),
-        ],
-    outputs = [out_response])
+out_response = gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")
 
+iface = gr.Interface(fn=predict_event, 
+                         inputs=gr.inputs.Image(type="pil"),
+                         outputs=out_response,
+                         enable_queue=True)
+    
 
-app = gr.mount_gradio_app(app, iface, path="/")
 
-# iface.launch(server_name = "0.0.0.0", server_port = 8001)
+# app = gr.mount_gradio_app(app, iface, path="/")
 
+iface.launch(server_name = "0.0.0.0", server_port = 8001)
 
-if __name__ == "__main__":
+# if __name__ == "__main__":
     # Use this for debugging purposes only
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8001)
+ 
+    # uvicorn.run(app, host="0.0.0.0", port=8001)