Update app.py

app.py

@@ -1,39 +1,22 @@
-import subprocess
 import os
-from llama_cpp import Llama
-import gradio as gr
-
-# 🔹 Get Hugging Face Token from environment variable
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# 🔹 Correct Model URL & Filename
-MODEL_URL = "https://huggingface.co/TheBloke/Mistral-7B-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf"
-MODEL_PATH = "./models/mistral-7b-instruct-v0.1.Q4_K_M.gguf"
-
-# 🔹 Ensure the models directory exists
-os.makedirs("./models", exist_ok=True)
-
-# 🔹 Check if the model exists, else download it
-if not os.path.exists(MODEL_PATH):
-    print("🚀 Downloading Mistral-7B Q4 GGUF model...")
-    subprocess.run([
-        "wget", "--header", f"Authorization: Bearer {HF_TOKEN}",
-        MODEL_URL, "-O", MODEL_PATH
-    ], check=True)
-    print("✅ Download complete!")
-
-# 🔹 Load the model
-print("📥 Loading the model...")
-model = Llama(model_path=MODEL_PATH, n_ctx=4096, n_threads=8)
-print("✅ Model loaded successfully!")
-
-# 🔹 Define a function to interact with the model
-def chat_with_mistral(prompt):
-    response = model(prompt, max_tokens=512)
-    return response["choices"][0]["text"]
-
-# 🔹 Create a Gradio UI
-iface = gr.Interface(fn=chat_with_mistral, inputs="text", outputs="text", title="Mistral-7B Chatbot")
-
-# 🔹 Launch the app
-iface.launch()
+from ctransformers import AutoModelForCausalLM
+
+# Define the model repository and file
+model_repo = "TheBloke/OpenHermes-2-Mistral-7B-GGUF"
+model_file = "openhermes-2-mistral-7b.Q4_K_M.gguf"
+
+# Download the model using ctransformers
+print(f"Downloading {model_file} from {model_repo}...")
+model = AutoModelForCausalLM.from_pretrained(
+    model_repo,
+    model_file=model_file,
+    model_type="mistral",
+    # Uncomment the following line if you have a CUDA-capable GPU
+    # gpu_layers=50
+)
+print("Model downloaded and loaded successfully.")
+
+# Test the model with a simple prompt
+prompt = "AI is going to"
+response = model(prompt)
+print(f"Prompt: {prompt}\nResponse: {response}")