Update app.py

app.py

@@ -1,81 +1,60 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import torch
-from TTS.api import TTS
 import os
 import subprocess
-
-# Force CPU usage
-device = "cpu"
-
-# Load TTS Model (Running on CPU)
-tts_model = TTS("tts_models/en/ljspeech/tacotron2-DDC", gpu=False)  # ✅ Ensures CPU-only execution
-
-# Hugging Face LLM Client (DeepSeek R1 7B)
-client = InferenceClient("deepseek-ai/deepseek-r1-7b")
-
-# RVC Model Paths
-RVC_MODEL_PATH = "zeldabotw.pth"
-RVC_INDEX_PATH = "zeldabotw.index"
-
-# Function to call RVC for voice conversion (CPU Mode)
-def convert_voice(input_wav, output_wav):
-    """Converts input TTS audio to ZeldaBotW voice using RVC (CPU Mode)."""
-    if not os.path.exists(RVC_MODEL_PATH) or not os.path.exists(RVC_INDEX_PATH):
-        raise FileNotFoundError("RVC model files not found! Ensure zeldabotw.pth and zeldabotw.index are in the same directory.")
-
-    command = f"python infer_rvc.py --input {input_wav} --output {output_wav} --model {RVC_MODEL_PATH} --index {RVC_INDEX_PATH} --pitch_shift 0 --device cpu"
-    
-    process = subprocess.run(command, shell=True, capture_output=True, text=True)
-    if process.returncode != 0:
-        print("RVC conversion failed:", process.stderr)
-        return None
-    return output_wav
-
-# Chatbot Response + TTS + RVC
-def respond(message, history, 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]})
-    
-    messages.append({"role": "user", "content": message})
-    
-    response = ""
-
-    # Get LLM Response
-    for message in client.chat_completion(messages, max_tokens=max_tokens, stream=False, temperature=temperature, top_p=top_p):
-        response += message.choices[0].message.content
-
-    # Generate Speech from Text (CPU Mode)
-    tts_audio_path = "tts_output.wav"
-    tts_model.tts_to_file(text=response, file_path=tts_audio_path)
-
-    # Convert TTS output to ZeldaBotW voice (CPU Mode)
-    rvc_audio_path = "rvc_output.wav"
-    rvc_converted_path = convert_voice(tts_audio_path, rvc_audio_path)
-
-    return response, tts_audio_path, rvc_converted_path  # ✅ Now correctly returns all outputs
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## DeepSeek R1 7B Chatbot with ZeldaBotW Voice (CPU Mode)")
-    
-    chatbot = gr.Chatbot(type="messages")  # ✅ Fix deprecated type warning
-    msg = gr.Textbox(label="User Input")
-    
-    system_msg = gr.Textbox(value="You are a friendly Chatbot.", label="System Message")
-    max_tokens = gr.Slider(1, 2048, value=512, step=1, label="Max Tokens")
-    temperature = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature")
-    top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)")
-    
-    tts_audio = gr.Audio(type="filepath", label="TTS Output")
-    rvc_audio = gr.Audio(type="filepath", label="RVC ZeldaBotW Voice")
-
-    def chat_fn(message, history):
-        return respond(message, history, system_msg.value, max_tokens.value, temperature.value, top_p.value)
-
-    msg.submit(chat_fn, inputs=[msg, chatbot], outputs=[chatbot, tts_audio, rvc_audio])
-
-demo.launch()
+import gradio as gr
+import ollama
+
+# Ensure Ollama is installed
+def install_ollama():
+    try:
+        subprocess.run(["ollama", "--version"], check=True)
+        print("✅ Ollama is already installed.")
+    except FileNotFoundError:
+        print("🚀 Installing Ollama...")
+        subprocess.run(["curl", "-fsSL", "https://ollama.com/install.sh", "|", "sh"], shell=True, check=True)
+        print("✅ Ollama installed successfully!")
+
+# Start Ollama if it's not running
+def start_ollama():
+    try:
+        subprocess.run(["pgrep", "-f", "ollama"], check=True)
+        print("✅ Ollama is already running.")
+    except subprocess.CalledProcessError:
+        print("🚀 Starting Ollama server...")
+        subprocess.Popen(["ollama", "serve"])
+        print("✅ Ollama started.")
+
+# Ensure model is downloaded to models/ folder
+MODEL_NAME = "deepseek-llm-7b"
+MODEL_PATH = f"models/{MODEL_NAME}"
+
+def download_model():
+    if not os.path.exists(MODEL_PATH):
+        print(f"🚀 Downloading model: {MODEL_NAME} to {MODEL_PATH} ...")
+        os.makedirs("models", exist_ok=True)
+        subprocess.run(["ollama", "pull", f"deepseek/{MODEL_NAME}"], check=True)
+        print(f"✅ Model downloaded to {MODEL_PATH}.")
+    else:
+        print(f"✅ Model {MODEL_NAME} already exists.")
+
+# Generate AI response using Ollama
+def chat_response(user_input):
+    response = ollama.chat(model=MODEL_NAME, messages=[{"role": "user", "content": user_input}])
+    return response['message']['content']
+
+# Run setup
+install_ollama()
+start_ollama()
+download_model()
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=chat_response,
+    inputs="text",
+    outputs="text",
+    title="DeepSeek ChatBot (Ollama)",
+    description="Chat with DeepSeek LLM 7B using Ollama."
+)
+
+# Launch Gradio App
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)