Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Function to load the model and tokenizer
+def load_model(model_name):
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        return pipeline("text-generation", model=model, tokenizer=tokenizer)
+    except Exception as e:
+        return f"Error loading model: {str(e)}"
+
+# Function to generate a response from the model
+def chat(model_name, user_input, chat_history):
+    if model_name.strip() == "":
+        return "Please enter a valid model name.", chat_history
+
+    # Load the model
+    generator = load_model(model_name)
+    if isinstance(generator, str):  # If there was an error loading the model
+        return generator, chat_history
+
+    # Generate a response
+    try:
+        response = generator(user_input, max_length=50, num_return_sequences=1)[0]['generated_text']
+        chat_history.append((user_input, response))
+        return "", chat_history
+    except Exception as e:
+        return f"Error generating response: {str(e)}", chat_history
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Chat with Any Hugging Face Model")
+    
+    with gr.Row():
+        model_name = gr.Textbox(label="Enter Hugging Face Model Name", placeholder="e.g., gpt2, facebook/opt-125m")
+    
+    chatbot = gr.Chatbot(label="Chat")
+    user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+    clear_button = gr.Button("Clear Chat")
+
+    # Define the chat function
+    user_input.submit(chat, [model_name, user_input, chatbot], [user_input, chatbot])
+    clear_button.click(lambda: [], None, chatbot, queue=False)
+
+# Launch the app
+demo.launch()