Update app.py

app.py

@@ -1,39 +1,44 @@
+# app.py
+# -*- coding: utf-8 -*-
+
+import os
 import torch
 import gradio as gr
-from diffusers import StableDiffusionImg2ImgPipeline
-from PIL import Image
-
-# Set up the device (use 'cpu' since we are running on CPU)
-device = "cpu"  # Force CPU usage
+from unsloth import FastLanguageModel
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from datasets import load_dataset
 
-# Load the pre-trained Stable Diffusion model from Hugging Face
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-2-1", torch_dtype=torch.float32)
-pipe = pipe.to(device)
+# Load the model and tokenizer
+max_seq_length = 2048
+dataset_path = "/content/dataset.jsonl"  # Update this path as needed
+dataset = load_dataset("json", data_files=dataset_path)
 
-# Function for generating an image based on input image and prompt
-def generate_image(photo, prompt):
-    # Resize input image to 512x512 (required by Stable Diffusion)
-    photo = photo.resize((512, 512))
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/Meta-Llama-3.1-8B",
+    max_seq_length=max_seq_length,
+    dtype=None,
+    load_in_4bit=True,
+)
 
-    # Convert the PIL image to a tensor
-    photo_tensor = torch.tensor(np.array(photo)).permute(2, 0, 1).unsqueeze(0).to(device).float() / 255.0
+# Prepare the model for inference
+model = FastLanguageModel.for_inference(model)
 
-    # Generate the image using Stable Diffusion model
-    generated_image = pipe(prompt=prompt, image=photo_tensor, strength=0.75, guidance_scale=7.5).images[0]
-    
-    # Return the generated image
-    return generated_image
+# Function to generate text
+def generate_response(user_input):
+    input = tokenizer(user_input, return_tensors="pt").to("cuda:0")
+    output = model.generate(**input)
+    return tokenizer.batch_decode(output)[0]
 
 # Create Gradio interface
-interface = gr.Interface(
-    fn=generate_image,  # The function to generate images
-    inputs=[
-        gr.Image(type="pil", label="Upload your photo"),  # Input image (your photo)
-        gr.Textbox(lines=2, placeholder="Enter prompt here", label="Enter text prompt")  # Input text prompt
-    ],
-    outputs=gr.Image(type="pil", label="Generated Image"),  # Output generated image
-    live=True  # Automatically update the output as the user changes the prompt or uploads a photo
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.inputs.Textbox(label="User  Input"),
+    outputs=gr.outputs.Textbox(label="Bot Response"),
+    title="Chatbot with Llama 3.1",
+    description="A chatbot powered by the Llama 3.1 model. Type your message below."
 )
 
-# Launch the Gradio interface
-interface.launch()
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()