Upload app (3).py

app (3).py

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+
+import gradio as gr
+#from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 
+from ctransformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import time
+from datetime import datetime
+import shutil
+
+import os
+folder_path = "./models"  # Change the folder name if needed
+
+if not os.path.exists(folder_path):
+    os.makedirs(folder_path)
+    print(f"✅ Folder created: {folder_path}")
+else:
+    print(f"📂 Folder already exists: {folder_path}")
+
+# Define source file and destination folder
+source_file = "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
+destination_folder = "./models"
+
+# Copy the file
+shutil.copy(source_file, destination_folder)
+
+source_file = "config.json"
+# Copy the file
+shutil.copy(source_file, destination_folder)
+
+model_path = "./models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"  # Adjust if necessary
+
+if os.path.exists(model_path):
+    print(f"✅ Model found at: {model_path}")
+else:
+    print(f"❌ Model not found at: {model_path}")
+    print("🔍 Available files:", os.listdir("./models"))
+
+torch.random.manual_seed(0) 
+"""
+model = AutoModelForCausalLM.from_pretrained( 
+    "microsoft/Phi-3-mini-4k-instruct",
+    device_map="cpu",  
+    torch_dtype="auto",  
+    trust_remote_code=True,  
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") 
+"""
+
+"""
+model_name = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="cpu")
+"""
+
+torch.set_default_device("cpu")
+
+print("model")
+print(datetime.fromtimestamp(time.time()))
+
+#model_name = "TheBloke/TinyLlama-1.1B-Chat-GGUF"  # Change if using a different version
+#model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # Change if using a different version
+model_name = "microsoft/phi-1_5"  # Change if using a different version
+#model = AutoModelForCausalLM.from_pretrained(destination_folder, model_type="llama")
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+print("tokenizer")
+print(datetime.fromtimestamp(time.time()))
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+print("done setup")
+print(datetime.fromtimestamp(time.time()))
+
+def generate_letter(date, letter_time, purpose, place, sender, receiver):
+    prompt = (f"Tuliskan surat resmi dengan detail sebagai berikut:\n"
+              f"Tanggal: {date}\nWaktu: {letter_time}\nTujuan: {purpose}\nTempat: {place}\n"
+              f"Pengirim: {sender}\nPenerima: {receiver}\n\nSurat:")
+    """prompt = (f"Write a formal letter with the following details:\n"
+              f"Date: {date}\nTime: {letter_time}\nPurpose: {purpose}\nPlace: {place}\n"
+              f"Sender: {sender}\nReceiver: {receiver}\n\nLetter:")"""
+    """
+    messages = [ 
+        {"role": "system", "content": "You are a helpful AI assistant."}, 
+        {"role": "user", "content": f"{prompt}"}, 
+    ] 
+    
+    pipe = pipeline( 
+    "text-generation", 
+    model=model, 
+    tokenizer=tokenizer, 
+    ) 
+    
+    generation_args = { 
+        "max_new_tokens": 500, 
+        "return_full_text": False, 
+        "temperature": 0.0, 
+        "do_sample": False, 
+    } 
+    
+    output = pipe(messages, **generation_args) 
+    return output[0]['generated_text']
+    """
+
+    print("tokenizer")
+    print(datetime.fromtimestamp(time.time()))
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    print("model.generate")
+    print(datetime.fromtimestamp(time.time()))
+    output = model.generate(**inputs, max_length=100, temperature=0.7, top_p=0.9)
+    
+    print("return")
+    print(datetime.fromtimestamp(time.time()))
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+    """response = model(prompt)
+    return response"""
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_letter,
+    inputs=[
+        gr.Textbox(label="Tanggal"),
+        gr.Textbox(label="Waktu"),
+        gr.Textbox(label="Tujuan"),
+        gr.Textbox(label="Tempat"),
+        gr.Textbox(label="Pengirim"),
+        gr.Textbox(label="Penerima"),
+    ],
+    #outputs=gr.Textbox(label="Generated Letter"),
+    outputs=gr.Textbox(label="Surat yang dibuat secara otomatis"),
+    #title="Letter Generator",
+    title="Generator Surat",
+    #description="Enter the details and generate a formal letter automatically."
+    description="Tuliskan detail informasi surat yang ingin dibuat."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+