application deployed

app.py

@@ -1,7 +1,42 @@
+#import gradio as gr
+
+#def greet(name):
+#    return "Hello " + name + "!!"
+
+#demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+#demo.launch()
+
+#######################
+
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+model_id = "codellama/CodeLlama-7b-Instruct-hf"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
+
+# 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 = f"""### Task:
+Convert the following Python code to equivalent R code.
+
+### Python code:
+{python_code}
 
-def greet(name):
-    return "Hello " + name + "!!"
+### R code:"""
+    result = generator(prompt, max_length=1024, temperature=0.2, num_return_sequences=1)
+    return result[0]['generated_text']
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# 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",
+    description="Enter Python code, and this tool will convert it into equivalent R code using CodeLlama Instruct model."
+).launch()

requirements.txt

@@ -0,0 +1,14 @@
+#transformers
+#torch
+#requests
+#Pillow
+#open_clip_torch
+#ftfy
+
+transformers
+torch
+gradio
+accelerate
+
+# This is only needed for local deployment
+gradio