Update app.py

app.py

@@ -1,64 +1,135 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+model_id = "GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens,
+#     temperature,
+#     top_p,
+# ):
+#     messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+#     for val in history:
+#         if val[0]:
+#             messages.append({"role": "user", "content": val[0]})
+#         if val[1]:
+#             messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+#     messages.append({"role": "user", "content": message})
 
-    response = ""
+#     response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+#     for message in client.chat_completion(
+#         messages,
+#         max_tokens=max_tokens,
+#         stream=True,
+#         temperature=temperature,
+#         top_p=top_p,
+#     ):
+#         token = message.choices[0].delta.content
 
-        response += token
-        yield response
+#         response += token
+#         yield response
 
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# demo = gr.ChatInterface(
+#     respond,
+#     additional_inputs=[
+#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+
+# Function to generate text
+def generate_text(prompt, max_length=100):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.7
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Gradio frontend
+def gradio_interface(prompt, max_length):
+    if not prompt.strip():
+        return "Please enter a prompt."
+    try:
+        output = generate_text(prompt, max_length=max_length)
+        return output
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Define Gradio components
+with gr.Blocks() as demo:
+    gr.Markdown("# LLaMA3 8B CPT Sahabatai Instruct")
+    gr.Markdown("Generate text using the **LLaMA3 8B CPT Sahabatai Instruct** model.")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(
+                label="Enter your prompt",
+                placeholder="Type something...",
+                lines=3,
+            )
+            max_length_slider = gr.Slider(
+                label="Max Length",
+                minimum=10,
+                maximum=200,
+                value=100,
+                step=10,
+            )
+            generate_button = gr.Button("Generate")
+        
+        with gr.Column():
+            output_text = gr.Textbox(
+                label="Generated Text",
+                lines=10,
+                interactive=False,
+            )
+    
+    # Link the button to the function
+    generate_button.click(
+        fn=gradio_interface,
+        inputs=[prompt_input, max_length_slider],
+        outputs=output_text,
+    )
 
+# Launch the Gradio app
 
 if __name__ == "__main__":
     demo.launch()