Update app.py

app.py

@@ -1,7 +1,52 @@
-from fastapi import FastAPI
+# from fastapi import FastAPI
+
+# app = FastAPI()
+
+# @app.get("/")
+# def greet_json():
+#     return {"Hello": "World!"}
+
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import sqlite3
+import torch
 
 app = FastAPI()
 
+# Load the DeepSeek model and tokenizer
+MODEL_NAME = "deepseek-ai/deepseek-coder-1.3b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to("cpu")  # Use "cuda" if available
+
+
+def generate_sql_query(user_input: str) -> str:
+    """
+    Generate an SQL query from a natural language query using the DeepSeek model.
+    """
+    inputs = tokenizer(user_input, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=100)
+    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return sql_query
+
+
+@app.post("/chat")
+def chat(request: ChatRequest):
+    user_input = request.message
+    
+    sql_query = generate_sql_query(user_input)
+    print(f"Generated SQL Query: {sql_query}")
+
+    return {"response": sql_query}
+
 @app.get("/")
-def greet_json():
-    return {"Hello": "World!"}
+def home():
+    return {"message": "DeepSeek SQL Query API is running"}
+
+
+# Run the API
+# if __name__ == "__main__":
+#     import uvicorn
+#     uvicorn.run(app, host="0.0.0.0", port=8000)
+