Created application file

app.py

@@ -0,0 +1,79 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from huggingface_hub import inference_api
+import speech_recognition as sr
+import pyttsx3
+import diffusers
+
+# Set up speech recognition and synthesis
+r = sr.Recognizer()
+engine = pyttsx3.init()
+
+# Set up the Hugging Face Hub model and tokenizer
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Set up the Serverless Inference API
+inference_api_token = "YOUR_INFERENCES_API_TOKEN"
+inference_api = inference_api.InferenceApi(token=inference_api_token)
+
+# Set up the Diffusers library
+diffusers_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+diffusers_model = diffusers.DDPMPipeline.from_pretrained("stabilityai/stable-diffusion-2")
+
+def recognize_speech():
+    with sr.Microphone() as source:
+        print("Say something!")
+        audio = r.listen(source)
+        try:
+            text = r.recognize_google(audio, language="en-US")
+            return text
+        except sr.UnknownValueError:
+            print("Sorry, I didn't catch that. Try again!")
+            return None
+
+def respond_to_text(text):
+    inputs = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        max_length=512,
+        return_attention_mask=True,
+        return_tensors='pt'
+    )
+    outputs = inference_api.predict(model_name, inputs)
+    logits = outputs.logits
+    _, predicted = torch.max(logits, dim=1)
+    response = tokenizer.decode(predicted[0], skip_special_tokens=True)
+    return response
+
+def generate_image(prompt):
+    image = diffusers_model(prompt, num_inference_steps=50, device=diffusers_device)
+    return image
+
+def speak_text(text):
+    engine.say(text)
+    engine.runAndWait()
+
+st.title("Chat with LLM and Generate Images")
+
+chat_input = st.text_input("Type or speak something:")
+if chat_input:
+    response = respond_to_text(chat_input)
+    st.write("LLM Response:", response)
+    speak_text(response)
+
+generate_image_button = st.button("Generate Image")
+if generate_image_button:
+    prompt = st.text_input("Enter a prompt for the image:")
+    image = generate_image(prompt)
+    st.image(image, use_column_width=True)
+
+mic_button = st.button("Speak")
+if mic_button:
+    text = recognize_speech()
+    if text:
+        response = respond_to_text(text)
+        st.write("LLM Response:", response)
+        speak_text(response)