33333

app.py

@@ -1,7 +1,7 @@
 import argparse
-import gradio as gr
 import torch
 from transformers import pipeline
+from interpreter import interpreter
 
 # Load the model
 generator = pipeline("text-generation", model="gpt-neo-2.7B")
@@ -10,9 +10,13 @@ generator = pipeline("text-generation", model="gpt-neo-2.7B")
 def generate_text(prompt):
     return generator(prompt, max_length=100, do_sample=True)[0]["generated_text"]
 
+# Define a function to handle input using OpenInterpreter
+def generate_interpreter_response(prompt):
+    return interpreter.chat(prompt)
+
 # CLI interface using argparse
 def cli_interface():
-    parser = argparse.ArgumentParser(description="Command-line interaction with the text generation model.")
+    parser = argparse.ArgumentParser(description="Command-line interaction with the text generation model and OpenInterpreter.")
     parser.add_argument("--task", type=str, help="The prompt or command to generate text for")
     args = parser.parse_args()
 
@@ -23,11 +27,19 @@ def cli_interface():
     result = generate_text(task)
     print(result)
 
+    # Process task using OpenInterpreter
+    response = generate_interpreter_response(task)
+    print("OpenInterpreter Response:", response)
+
 if __name__ == "__main__":
     cli_interface()
 
-# Set up Gradio interface
-interface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
-
-# Launch the app
-interface.launch()
+# Additional information about the OpenInterpreter project
+# OpenInterpreter allows LLMs to execute code (including Python, JavaScript, Shell commands, and more) in a local environment.
+# It provides a natural-language interface to your computer's general-purpose capabilities, such as creating and editing files,
+# controlling a web browser, and analyzing large datasets.
+# To get started, install OpenInterpreter using:
+# pip install open-interpreter
+# After installation, start the interpreter with the command:
+# $ interpreter
+# For more information, visit: https://github.com/OpenInterpreter