Update fastapi_app/main.py

fastapi_app/main.py

@@ -1,91 +1,91 @@
-from fastapi import FastAPI, Request
-from fastapi.responses import HTMLResponse
-from fastapi.staticfiles import StaticFiles
-from pydantic import BaseModel
-import uvicorn
-import os, sys
-
-# Add the root directory to sys.path
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-from model_pipeline.model_predict import load_model, predict as initial_predict
-from llama_pipeline.llama_predict import predict as llama_predict
-from db_connection import insert_db
-from logging_config.logger_config import get_logger
-
-# Initialize the FastAPI app
-app = FastAPI()
-
-# Initialize the logger
-logger = get_logger(__name__)
-
-# Load the latest model at startup
-model = load_model()
-
-# Mount the static files directory
-app.mount("/static", StaticFiles(directory="fastapi_app/static"), name="static")
-
-@app.get("/", response_class=HTMLResponse)
-def read_root():
-    with open("fastapi_app/static/index.html") as f:
-        html_content = f.read()
-    return HTMLResponse(content=html_content, status_code=200)
-
-@app.get("/health")
-def health_check():
-    logger.info("Health check endpoint accessed.")
-    return {"status": "ok"}
-
-class TextInput(BaseModel):
-    text: str
-
-class PredictionInput(BaseModel):
-    text: str
-    initial_prediction: str
-    llama_category: str
-    llama_explanation: str
-    user_rating: int
-
-@app.post("/predict_sentiment")
-def predict_sentiment(input_data: TextInput):
-    logger.info(f"Prediction request received with text: {input_data.text}")
-    
-    # Initial model prediction
-    initial_prediction = initial_predict(input_data.text, model = model)
-    
-    # LLaMA 3 prediction
-    llama_prediction = llama_predict(input_data.text)
-    
-    # Prepare response
-    response = {
-        "text": input_data.text,
-        "initial_prediction": initial_prediction,
-        "llama_category": llama_prediction['Category'],
-        "llama_explanation": llama_prediction['Explanation']
-    }
-    
-    logger.info(f"Prediction response: {response}")
-    return response
-
-@app.post("/submit_interaction")
-def submit_interaction(data: PredictionInput):
-    logger.info(f"Received interaction data: {data}")
-    logger.info(f"Received text: {data.text}")
-    logger.info(f"Received initial_prediction: {data.initial_prediction}")
-    logger.info(f"Received llama_category: {data.llama_category}")
-    logger.info(f"Received llama_explanation: {data.llama_explanation}")
-    logger.info(f"Received user_rating: {data.user_rating}")
-
-    interaction_data = {
-        "Input_text": data.text,
-        "Model_prediction": data.initial_prediction,
-        "Llama_3_Prediction": data.llama_category,
-        "Llama_3_Explanation": data.llama_explanation,
-        "User Rating": data.user_rating,
-    }
-    
-    response = insert_db(interaction_data)
-    logger.info(f"Database response: {response}")
-    return {"status": "success", "response": response}
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+import uvicorn
+import os, sys
+
+# Add the root directory to sys.path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from model_pipeline.model_predict import load_model, predict as initial_predict
+from llama_pipeline.llama_predict import predict as llama_predict
+from db_connection import insert_db
+from logging_config.logger_config import get_logger
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Initialize the logger
+logger = get_logger(__name__)
+
+# Load the latest model at startup
+model = load_model()
+
+# Mount the static files directory
+app.mount("/static", StaticFiles(directory="fastapi_app/static"), name="static")
+
+@app.get("/", response_class=HTMLResponse)
+def read_root():
+    with open("fastapi_app/static/index.html") as f:
+        html_content = f.read()
+    return HTMLResponse(content=html_content, status_code=200)
+
+@app.get("/health")
+def health_check():
+    logger.info("Health check endpoint accessed.")
+    return {"status": "ok"}
+
+class TextInput(BaseModel):
+    text: str
+
+class PredictionInput(BaseModel):
+    text: str
+    initial_prediction: str
+    llama_category: str
+    llama_explanation: str
+    user_rating: int
+
+@app.post("/predict_sentiment")
+def predict_sentiment(input_data: TextInput):
+    logger.info(f"Prediction request received with text: {input_data.text}")
+    
+    # Initial model prediction
+    initial_prediction = initial_predict(input_data.text, model = model)
+    
+    # LLaMA 3 prediction
+    llama_prediction = llama_predict(input_data.text)
+    
+    # Prepare response
+    response = {
+        "text": input_data.text,
+        "initial_prediction": initial_prediction,
+        "llama_category": llama_prediction['Category'],
+        "llama_explanation": llama_prediction['Explanation']
+    }
+    
+    logger.info(f"Prediction response: {response}")
+    return response
+
+@app.post("/submit_interaction")
+def submit_interaction(data: PredictionInput):
+    logger.info(f"Received interaction data: {data}")
+    logger.info(f"Received text: {data.text}")
+    logger.info(f"Received initial_prediction: {data.initial_prediction}")
+    logger.info(f"Received llama_category: {data.llama_category}")
+    logger.info(f"Received llama_explanation: {data.llama_explanation}")
+    logger.info(f"Received user_rating: {data.user_rating}")
+
+    interaction_data = {
+        "Input_text": data.text,
+        "Model_prediction": data.initial_prediction,
+        "Llama_3_Prediction": data.llama_category,
+        "Llama_3_Explanation": data.llama_explanation,
+        "User Rating": data.user_rating,
+    }
+    
+    response = insert_db(interaction_data)
+    logger.info(f"Database response: {response}")
+    return {"status": "success", "response": response}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)