Update app.py

app.py

@@ -1,25 +1,25 @@
-import gradio
-from transformers import pipeline
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Function to generate chatbot responses
-def generate_chatbot_response(prompt):
-    chatbot = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
-    chatbot_response = chatbot(prompt, max_length=50, num_return_sequences=1)[0]["generated_text"]
-    return chatbot_response
+model_name = "gpt-3.5-turbo"
 
-# Function to handle the web app logic
-def chatbot_app(prompt):
-    chatbot_response = generate_chatbot_response(prompt)
-    return {"Chatbot Response": chatbot_response}
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-# Create the Gradio interface
-iface = gradio.Interface(
-    fn=chatbot_app,
-    inputs="text",
-    outputs="text",
-    title="Chatbot App",
-    description="Enter a prompt to generate a chatbot response."
-)
+# Generate a response
+def generate_response(prompt):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    response = model.generate(inputs, max_length=100, num_return_sequences=1)
+    return tokenizer.decode(response[0], skip_special_tokens=True)
 
-# Run the Gradio app
+# Gradio interface
+def chatbot_interface(prompt):
+    response = generate_response(prompt)
+    return response
+
+# Create a Gradio interface
+iface = gr.Interface(fn=chatbot_interface, inputs="text", outputs="text")
+
+# Launch the interface
 iface.launch()