Create app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import re
+
+app = FastAPI()
+
+# Load model and tokenizer from Hugging Face
+model_id = "misalsathsara/phi1.5-js-codegen"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Your system prompt
+system_prompt = """
+You are a smart javascript assistant that only generates only the best simple javascript functions without any comments like this:
+function transform(row) {
+    row['Latitude'] = row['Location'].split(',')[0];
+    row['Longitude'] = row['Location'].split(',')[1];
+    return row;
+}
+when user gives a prompt like "convert the location field into separate latitude and longitude fields".
+Generate simple javascript functions that should take a single row of data as input and the generated function name is always transform.
+The user may use the words column, item or field to mean each column.
+Guard against null and undefined for items in the row.
+${fieldList}
+Field names are case sensitive.
+For parsing something into a date, assume a function called parseAnyDate is available.
+If the code requires some numeric calculation - ensure the value is converted to a number first. Don't assume its always the correct data type.
+When doing any string comparison, make it case insensitive.
+When replacing characters in a string, make sure to use the correct replacement literal. For example, to replace hyphens with spaces, use: .replace(/-/g, ' ')
+The function should not include a single comment before or after the function.
+Don't add any text except for the function code.
+Don't add any markdown block markers either.
+Every function must end with return row;
+"""
+
+# Define the expected request body
+class RequestData(BaseModel):
+    instruction: str
+
+# POST endpoint
+@app.post("/generate")
+def generate_code(data: RequestData):
+    instruction = data.instruction
+    full_prompt = system_prompt + f"\n### Instruction:\n{instruction}\n\n### Response:\n"
+    
+    # Tokenize input
+    input_ids = tokenizer(full_prompt, return_tensors="pt").input_ids.to(device)
+    
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            max_new_tokens=200,
+            temperature=0.3,
+            top_k=50,
+            top_p=0.95,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    generated_text = tokenizer.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)
+
+    # Extract clean JS function
+    match = re.search(r"function\s*\(.*?\)\s*{.*?return row;\s*}", generated_text, re.DOTALL)
+    clean_output = match.group(0).strip() if match else generated_text.strip()
+
+    return {"result": clean_output}