Update app.py

app.py

@@ -1,39 +1,39 @@
-from flask import Flask, render_template, request
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-app = Flask(__name__)
-
-# Load fine-tuned model and tokenizer
-model_path = "./finetuned_codegen"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16)
-
-# Set padding token
-tokenizer.pad_token = tokenizer.eos_token
-
-# Move model to CPU
-device = torch.device("cpu")
-model.to(device)
-
-@app.route("/", methods=["GET", "POST"])
-def index():
-    generated_code = ""
-    prompt = ""
-    if request.method == "POST":
-        prompt = request.form["prompt"]
-        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
-        outputs = model.generate(
-            **inputs,
-            max_length=200,
-            num_return_sequences=1,
-            pad_token_id=tokenizer.eos_token_id,
-            do_sample=True,
-            temperature=0.7,
-            top_p=0.9
-        )
-        generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return render_template("index.html", generated_code=generated_code, prompt=prompt)
-
-if __name__ == "__main__":
+from flask import Flask, render_template, request
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+app = Flask(__name__)
+
+# Load fine-tuned model and tokenizer
+model_path = "./finetuned_codegen"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float32)
+
+# Set padding token
+tokenizer.pad_token = tokenizer.eos_token
+
+# Move model to CPU
+device = torch.device("cpu")
+model.to(device)
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    generated_code = ""
+    prompt = ""
+    if request.method == "POST":
+        prompt = request.form["prompt"]
+        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
+        outputs = model.generate(
+            **inputs,
+            max_length=200,
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+        generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return render_template("index.html", generated_code=generated_code, prompt=prompt)
+
+if __name__ == "__main__":
     app.run(debug=True)