Update app.py

app.py

@@ -1,41 +1,47 @@
+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 import openai
 import secrets
 
-print(secrets.OPENAI_API_KEY)
-
 # Set up the OpenAI API credentials
 openai.api_key = secrets.OPENAI_API_KEY
 
-# Define a function that takes a user's input code as a prompt and uses the OpenAI API to generate a corrected version of the code
-def correct_code(prompt):
-    # Set up the OpenAI API parameters
-    model_engine = "text-davinci-002"
-    temperature = 0.7
-    max_tokens = 100
+# Load the Hugging Face model and tokenizer
+model_name = "Helsinki-NLP/opus-mt-python-en"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-    # Generate the corrected code using the OpenAI API
+# Define a function that takes a user's input code as a prompt and uses the OpenAI API and Hugging Face model to generate a corrected version of the code
+def correct_code(prompt):
+    # Use the OpenAI API to generate suggestions for fixing syntax errors in the code
     response = openai.Completion.create(
-        engine=model_engine,
+        engine="davinci-codex",
         prompt=prompt,
-        temperature=temperature,
-        max_tokens=max_tokens
+        max_tokens=1024,
+        n=1,
+        stop=None,
+        temperature=0.5,
     )
 
     # Extract the corrected code from the API response
     corrected_code = response.choices[0].text.strip()
 
+    # Use the Hugging Face model to generate a more natural-sounding version of the corrected code
+    input_ids = tokenizer.encode(corrected_code, return_tensors="pt")
+    outputs = model.generate(input_ids)
+    corrected_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
     return corrected_code
 
-def main():
-    while True:
-        # Prompt the user for input code
-        prompt = input("Enter code to correct: ")
+# Define a Gradio interface for the code assistant
+input_text = gr.inputs.Textbox(lines=10, label="Input Code")
+output_text = gr.outputs.Textbox(label="Corrected Code")
 
-        # Generate a corrected version of the code using the OpenAI API
-        corrected_code = correct_code(prompt)
+def generate_code(input_text):
+    corrected_code = correct_code(input_text)
+    return corrected_code
 
-        # Print the corrected code
-        print(corrected_code)
+interface = gr.Interface(fn=generate_code, inputs=input_text, outputs=output_text, title="AI Code Assistant", description="Enter your code and click submit to generate a corrected version.")
 
-if __name__ == '__main__':
-    main()
+# Run the Gradio interface
+interface.launch()