Update app.py

app.py

@@ -1,39 +1,28 @@
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
-import streamlit as st
-from diffusers import UNet2DConditionModel, TextEncoder, VQModel
+import transformers
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
 
-# Use the default model names here
-unet_model_name = "unet/diffusion_pytorch_model.bin"
-text_encoder_name = "text_encoder/pytorch_model.bin"
-vae_model_name = "vae/diffusion_pytorch_model.bin"
+# Load tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
 
-# Create the pipeline or model objects using the default names
-pipeline = UNet2DConditionModel.from_pretrained(unet_model_name)
-# Title and User Input
-st.title("Text-to-Video with Streamlit")
-prompt = st.text_input("Enter your text prompt:", "Spiderman is surfing")
+# Define a function to preprocess user input
+def preprocess_input(text):
+  encoded_input = tokenizer(text, return_tensors='pt')
+  return encoded_input
 
-# Button to trigger generation
-if st.button("Generate Video"):
-    st.video(video_path)
+# Define a function to generate response based on user input
+def generate_response(user_input):
+  encoded_input = preprocess_input(user_input)
+  outputs = model(**encoded_input)
+  # Extract relevant information from model outputs (e.g., predicted class)
+  # Based on the extracted information, formulate a response using predefined responses or logic
+  response = "I'm still under development, but I understand you said: {}".format(user_input)
+  return response
 
-    # Ensure you have 'accelerate' version 0.17.0 or higher (see previous explanation)
-    import accelerate
-    if accelerate.__version__ < "0.17.0":
-        st.warning("Please upgrade 'accelerate' to version 0.17.0 or higher for CPU offloading.")
-    else:
-        with st.spinner("Generating video..."):
-            pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", 
-                                         torch_dtype=torch.float16, 
-                                         variant="fp16",
-                                         device="cpu") # Force CPU usage
-            pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-            pipe.enable_model_cpu_offload()  # Assuming 'accelerate' is updated  
-
-            video_frames = pipe(prompt, num_inference_steps=25).frames
-            video_path = export_to_video(video_frames)
-
-            # Display the video in the Streamlit app
-            st.video(video_path)
+# Start the chat loop
+while True:
+  user_input = input("You: ")
+  if user_input == "quit":
+    break
+  bot_response = generate_response(user_input)
+  print("Bot:", bot_response)