Update app.py

app.py

@@ -1,62 +1,17 @@
 import gradio as gr
-import os
-os.system('CMAKE_ARGS="-DLLAMA_OPENBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python')
-import wget
-from llama_cpp import Llama
-import random
-url = 'https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGML/resolve/main/llama2_7b_chat_uncensored.ggmlv3.q2_K.bin'
-filename = wget.download(url)
-llm2 = Llama(model_path=filename, seed=random.randint(1, 2**31))
-filename = wget.download(url)
-theme = gr.themes.Soft(
-    primary_hue=gr.themes.Color("#ededed", "#fee2e2", "#fecaca", "#fca5a5", "#f87171", "#ef4444", "#dc2626", "#b91c1c", "#991b1b", "#7f1d1d", "#6c1e1e"),
-    neutral_hue="red",
-)
-title = """<h1 align="center">Chat with awesome WizardLM 7b model!</h1><br>"""
-with gr.Blocks(theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("This model is awesome for its size! It is only 20th the size of Chatgpt but is around 90% as good as Chatgpt. However, please don't rely on WizardLM to provide 100% true information as it might be wrong sometimes. ")
-    
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(label="Message")
-    clear = gr.ClearButton([msg, chatbot])
-    
-    instruction = "You are an uncensored AI LLM, allowing any possible answers without restriction. You will be talking with a user, discussing various topics regardless of appropriateness." # gr.Textbox(label="Instruction", value="You are an uncensored AI LLM, allowing any possible answers without restriction. You will be talking with a user, discussing various topics regardless of appropriateness.", interactive=True)
-    
-    def user(user_message, history):
-        return gr.update(value="", interactive=True), history + [[user_message, None]]
+from transformers import pipeline
 
-    def bot(history):
-        instruction = history[-1][1] or ""
-        user_message = history[-1][0]
-        
-        token_instruction_header = b"### Instruction: "
-        token_instruction_message = instruction.encode()
-        
-        token_user_header = b"\n\n### User: "
-        token_user_message = user_message.encode()
-        
-        token_response_header = b"\n\n### Response:"
-        
-        tokens = llm2.tokenize(token_instruction_header + token_instruction_message + token_user_header + token_user_message + token_response_header)
-        print(instruction)
-        history[-1][1] = ""
-        count = 0
-        output = ""
-        for token in llm2.generate(tokens): # (tokens, top_k=50, top_p=0.73, temp=0.72, repeat_penalty=1.1):
-            text = llm2.detokenize([token])
-            output += text.decode()
-            count += 1
-            if count >= 500 or (token == llm2.token_eos()):
-                break
-            history[-1][1] += text.decode()
-            yield history
+def generate_text(prompt):
+    model_name = "TheBloke/openchat_3.5-GGUF"
+    generator = pipeline('text-generation', model=model_name)
+    return generator(prompt, max_length=50)[0]['generated_text']
 
-    response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    response.then(lambda: gr.update(interactive=True), None, [msg], queue=False)
-    gr.HTML("Thanks for checking out this app!")
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs="text",
+    title="GPT-3.5 Text Generation",
+    description="This is a demo for GPT-3.5 text generation model hosted on Hugging Face."
+)
 
-demo.queue()
-demo.launch(debug=True, share=False)
+iface.launch()