Create Llama.py

Llama.py

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+import pyttsx3
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Load pre-trained model and tokenizer from Hugging Face
+model_name = "decapoda-research/llama-7b-hf"  # Placeholder, replace with actual model if available
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Initialize text-to-speech engine
+tts_engine = pyttsx3.init()
+
+def generate_text(prompt):
+    # Use the model to generate text based on the prompt
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(inputs["input_ids"], max_length=50)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+def text_to_speech(text):
+    # Use the TTS engine to convert text to speech
+    tts_engine.say(text)
+    tts_engine.runAndWait()
+
+def main():
+    prompt = "Once upon a time"  # Replace with your desired prompt
+    generated_text = generate_text(prompt)
+    print(f"Generated Text: {generated_text}")
+    text_to_speech(generated_text)
+
+if __name__ == "__main__":
+    main()