Added application file

app.py

@@ -0,0 +1,72 @@
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+model, tokenizer = (
+    AutoModelForCausalLM.from_pretrained("Abhishekcr448/Tiny-Hinglish-Chat-21M"),
+    AutoTokenizer.from_pretrained("Abhishekcr448/Tiny-Hinglish-Chat-21M"),
+)
+
+# Function to generate text (suggestions)
+def generate_text(prompt, output_length):
+    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
+    generated_output = model.generate(
+        inputs['input_ids'],
+        max_length=inputs['input_ids'].shape[-1] + output_length,  # Generate 10 more tokens
+        no_repeat_ngram_size=2,
+        temperature=0.8,
+        top_k=50,
+        top_p=0.9,
+        do_sample=True,
+    )
+    output_text = tokenizer.decode(generated_output[0], skip_special_tokens=True)
+    return output_text
+
+
+# Set up the Gradio interface
+with gr.Blocks() as demo:
+
+    # Add a chat interface above the text boxes
+    with gr.Column(scale=4):
+        chatbox = gr.Chatbot(label="Chat", type="messages")
+    
+    with gr.Row():
+        # Create a column for the two text boxes
+        with gr.Column(scale=3):
+            # Input text box for user input (first column)
+            input_text = gr.Textbox(label="Enter your message", interactive=True)
+            
+            # Response text box (second column)
+            response_text = gr.Textbox(label="Response", interactive=False)
+
+        # Create a separate column for the button
+        with gr.Column(scale=1):
+            # Button placed beside the text boxes
+            replace_button = gr.Button("Replace Text", elem_id="replace-btn")
+    
+    # Set up the interaction between input and output
+    def validate_and_generate(prompt, output_length=4):
+        if prompt.strip():
+            return generate_text(prompt, output_length)
+
+    input_text.input(validate_and_generate, inputs=input_text, outputs=response_text)
+    replace_button.click(lambda x: x, inputs=response_text, outputs=input_text)
+
+    def chat_interaction(prompt, history):
+        if prompt.strip():
+            response = generate_text(prompt, output_length=10)
+
+            # Exclude the input prompt text from the response
+            response = response[len(prompt):].strip()
+            history.append({"role": "user", "content": prompt})
+            history.append({"role": "assistant", "content": response})
+            
+            # Call validate_and_generate with the response
+            response_text_value = validate_and_generate(response, output_length=10)
+            return history, response_text_value[len(response):].strip(), ""
+
+    input_text.submit(chat_interaction, inputs=[input_text, chatbox], outputs=[chatbox, response_text, input_text])
+
+# Launch the interface
+demo.launch()