application deployed

app.py

@@ -9,25 +9,19 @@
 #######################
 
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-# Model ID (7B is safer for free-tier Space)
 model_id = "codellama/CodeLlama-7b-Instruct-hf"
 
-# Load tokenizer and model
+# Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id, 
-    device_map="auto", 
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
 
-# Ensure pad token is set to eos token to avoid padding warnings
-tokenizer.pad_token = tokenizer.eos_token
+# Use text-generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
+# Define the function to handle the conversion
 def convert_python_to_r(python_code):
-    # Prompt construction
     prompt = f"""### Task:
 Convert the following Python code to equivalent R code.
 
@@ -35,40 +29,22 @@ Convert the following Python code to equivalent R code.
 {python_code}
 
 ### R code:"""
-
-    # Tokenize input with truncation
-    input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids
-
-    # Move input to GPU if available
-    if torch.cuda.is_available():
-        input_ids = input_ids.to("cuda")
-
-    # Generate output
-    outputs = model.generate(
-        input_ids,
-        max_length=1024,
-        do_sample=True,
-        temperature=0.2,
-        pad_token_id=tokenizer.eos_token_id,
-        num_return_sequences=1
+    #result = generator(prompt, max_length=1024, temperature=0.2, num_return_sequences=1)
+    result = generator(
+     prompt, 
+     max_length=1024, 
+     temperature=0.2, 
+     num_return_sequences=1, 
+     do_sample=True,                          # <-- Add this!
+     pad_token_id=tokenizer.eos_token_id      # <-- Safely set pad_token_id
     )
-
-    # Decode the generated output
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # Remove the prompt part from output, keep only R code
-    # Optional: Try to split from "### R code:"
-    if "### R code:" in generated_text:
-        generated_text = generated_text.split("### R code:")[-1].strip()
-
-    return generated_text
+    return result[0]['generated_text']
 
 # Gradio Interface
 gr.Interface(
     fn=convert_python_to_r,
     inputs=gr.Textbox(lines=10, placeholder="Paste your Python code here..."),
     outputs="text",
-    title="Python to R Code Converter using CodeLlama 7B Instruct",
-    description="Enter Python code below, and the tool will convert it to R code using the CodeLlama 7B Instruct model."
+    title="Python to R Code Converter using CodeLlama",
+    description="Enter Python code, and this tool will convert it into equivalent R code using CodeLlama Instruct model."
 ).launch()
-