Attempt to make response go to audio via pipeline

app.py

@@ -3,11 +3,13 @@ import toml
 from omegaconf import OmegaConf
 from query import VectaraQuery
 import os
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
 import streamlit as st
 from PIL import Image
 
+model = pipeline("sentiment-analysis") #needs finishing
+
 master_prompt = """
 As a Natural Farming Fertilizers Assistant, you will assist the user with any farming related question, always willing to answer any question and provide useful organic farming advice in the following format.
 ' ' '
@@ -93,27 +95,36 @@ def launch_bot():
                     #call in Mistral
                 prompt3 = master_prompt + prompt2 + "context:" + response
                 print("Called in Mistral")
-                device = "cuda" # the device to load the model onto
-
-
-                model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
-
-                tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
-
-#from mistral docs: prompt = "My favourite condiment is"
-
-
-                model_inputs = tokenizer([prompt3], return_tensors="pt").to(device)
-
-                model.to(device)
-
-
-                generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
-                st.write("Mistral:" + tokenizer.batch_decode(generated_ids)[0])
+                    # ADD IN LLM
+                # st.write("Mistral:" ) #Needs finishing
               #  else:
-               #     st.write(response) 
+                st.write(response) 
         message = {"role": "assistant", "content": response}
         st.session_state.messages.append(message)
+
+    # If assistant has most recently reaponded create audio of response 
+        if st.session_state.messages[-1]["role"] == "assistant":
+        #text-to-speech
+            pipe = pipeline(model="suno/bark-small")
+            output = pipe()
+
+            audio = output[response]
+            sampling_rate = output["sampling_rate"]
+        # ST interface for audio
+            audio_file = audio
+            audio_bytes = audio_file.read()
+
+            st.audio(audio_bytes, format='audio/ogg')
+
+            sample_rate = 44100  # 44100 samples per second
+            seconds = 2  # Note duration of 2 seconds
+            frequency_la = 440  # Our played note will be 440 Hz
+    # Generate array with seconds*sample_rate steps, ranging between 0 and seconds
+            t = np.linspace(0, seconds, seconds * sample_rate, False)
+    # Generate a 440 Hz sine wave
+            note_la = np.sin(frequency_la * t * 2 * np.pi)
+
+            st.audio(note_la, sample_rate=sample_rate)
     
 if __name__ == "__main__":
     launch_bot()