Update app.py

app.py

@@ -1,59 +1,51 @@
+import os
+import torch
+from PIL import Image
 import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
 from random import randint
-from all_models import models
-from externalmod import gr_Interface_load
 import asyncio
 from threading import RLock
-lock = RLock()
-
-def load_fn(models):
-    global models_load
-    models_load = {}
-    
-    for model in models:
-        if model not in models_load.keys():
-            try:
-                m = gr_Interface_load(f'models/{model}')
-            except Exception as error:
-                print(error)
-                m = gr.Interface(lambda: None, ['text'], ['image'])
-            models_load.update({model: m})
-
-
-load_fn(models)
-
-
-num_models = 6
-default_models = models[:num_models]
-timeout = 300
-
-def extend_choices(choices):
-    return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
+from externalmod import gr_Interface_load
 
+# Define model IDs
+llm_model_id = "mistralai/Mistral-7B-Instruct-v0.3"
+blip_model_id = "Salesforce/blip-image-captioning-large"
+model_name = "John6666/wai-ani-hentai-pony-v3-sdxl"
 
-def update_imgbox(choices):
-    choices_plus = extend_choices(choices[:num_models])
-    return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
+# Initialize BLIP processor and model
+processor = BlipProcessor.from_pretrained(blip_model_id)
+model = BlipForConditionalGeneration.from_pretrained(blip_model_id)
 
+# Initialize the model loading function
+lock = RLock()
+model_load = None
 
-def update_imgbox_gallery(choices):
-    choices_plus = extend_choices(choices[:num_models])
-    return [gr.Gallery(None, label = m, visible = (m != 'NA')) for m in choices_plus]
+def load_fn(model):
+    global model_load
+    try:
+        model_load = gr_Interface_load(f'models/{model}')
+    except Exception as error:
+        print(error)
+        model_load = gr.Interface(lambda: None, ['text'], ['image'])
 
+load_fn(model_name)
 
-async def infer(model_str, prompt, timeout):
-    from PIL import Image
+async def infer(prompt, timeout):
     noise = ""
     rand = randint(1, 500)
     for i in range(rand):
         noise += " "
-    task = asyncio.create_task(asyncio.to_thread(models_load[model_str], f'{prompt} {noise}'))
+    task = asyncio.create_task(asyncio.to_thread(model_load, f'{prompt} {noise}'))
     await asyncio.sleep(0)
     try:
         result = await asyncio.wait_for(task, timeout=timeout)
     except (Exception, asyncio.TimeoutError) as e:
         print(e)
-        print(f"Task timed out: {model_str}")
+        print(f"Task timed out: {model_name}")
         if not task.done(): task.cancel()
         result = None
     if task.done() and result is not None:
@@ -62,97 +54,107 @@ async def infer(model_str, prompt, timeout):
         return image
     return None
 
-def gen_fn(model_str, prompt):
-    if model_str == 'NA':
-        return None
+def gen_fn(prompt):
     try:
         loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_str, prompt, timeout))
+        result = loop.run_until_complete(infer(prompt, timeout=300))
     except (Exception, asyncio.CancelledError) as e:
         print(e)
-        print(f"Task aborted: {model_str}")
+        print(f"Task aborted: {model_name}")
         result = None
     finally:
         loop.close()
     return result
 
-
-def add_gallery(image, model_str, gallery):
+def add_gallery(image, gallery):
     if gallery is None: gallery = []
     with lock:
-        if image is not None: gallery.insert(0, (image, model_str))
+        if image is not None: gallery.insert(0, (image, model_name))
     return gallery
 
-
-def gen_fn_gallery(model_str, prompt, gallery):
+def gen_fn_gallery(prompt, gallery):
     if gallery is None: gallery = []
-    if model_str == 'NA':
-        yield gallery
     try:
         loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_str, prompt, timeout))
+        result = loop.run_until_complete(infer(prompt, timeout=300))
         with lock:
             if result: gallery.insert(0, result)
     except (Exception, asyncio.CancelledError) as e:
         print(e)
-        print(f"Task aborted: {model_str}")
+        print(f"Task aborted: {model_name}")
     finally:
         loop.close()
     yield gallery
 
-
-CSS="""
-#container { max-width: 1200px; margin: 0 auto; !important; }
-.output { width=112px; height=112px; !important; }
-.gallery { width=100%; min_height=768px; !important; }
-.guide { text-align: center; !important; }
-"""
-
-with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
-    gr.HTML(
-    """
-        <div>
-        <p> <center>For simultaneous generations without hidden queue check out <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>! For more options like single model x6 check out <a href="https://huggingface.co/spaces/John6666/Diffusion80XX4sg">Diffusion80XX4sg</a> by John6666!</center>
-        </p></div>
-    """
-)  
-    with gr.Tab('Huggingface Diffusion'):
-        with gr.Column(scale=2):
-            txt_input = gr.Textbox(label='Your prompt:', lines=4)
-            with gr.Row():
-                gen_button = gr.Button(f'Generate up to {int(num_models)} images from 1 to {int(num_models)*3} minutes total', scale=2)
-                stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
-                gen_button.click(lambda: gr.update(interactive = True), None, stop_button)
-            gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
-
-        with gr.Column(scale=1):
-            with gr.Group():
-                with gr.Row():
-                    output = [gr.Image(label=m, show_download_button=True, elem_classes="output", interactive=False, min_width=80, show_share_button=False, visible=True) for m in default_models]
-                    #output = [gr.Image(label=m, show_download_button=True, elem_classes="output", interactive=False, show_share_button=True) for m in default_models]
-                    #output = [gr.Gallery(label=m, show_download_button=True, elem_classes="output", interactive=False, show_share_button=True, container=True, format="png", object_fit="cover") for m in default_models]
-                    current_models = [gr.Textbox(m, visible=False) for m in default_models]
-
-        with gr.Column(scale=2):
-            gallery = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery",
-                                interactive=False, show_share_button=True, container=True, format="png",
-                                preview=True, object_fit="cover", columns=2, rows=2) 
-
-        for m, o in zip(current_models, output):
-            #gen_event = gen_button.click(gen_fn, [m, txt_input], o)
-            #gen_event = gen_button.click(gen_fn_gallery, [m, txt_input, o], o)
-            gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn, inputs=[m, txt_input], outputs=[o])
-            o.change(add_gallery, [o, m, gallery], [gallery])
-            stop_button.click(lambda: gr.update(interactive = False), None, stop_button, cancels = [gen_event])
-
-        with gr.Column(scale=4):
-            with gr.Accordion('Model selection'):
-                model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
-                model_choice.change(update_imgbox, model_choice, output)
-                #model_choice.change(update_imgbox_gallery, model_choice, output)
-                model_choice.change(extend_choices, model_choice, current_models)
-
-    gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
-
-demo.queue()
-demo.launch()
+def generate_caption(image, min_len=30, max_len=100):
+    try:
+        inputs = processor(image, return_tensors="pt")
+        out = model.generate(**inputs, min_length=min_len, max_length=max_len)
+        caption = processor.decode(out[0], skip_special_tokens=True)
+        return caption
+    except Exception as e:
+        return 'Unable to generate caption.'
+
+def get_llm_hf_inference(model_id=llm_model_id, max_new_tokens=128, temperature=0.1):
+    try:
+        llm = HuggingFaceEndpoint(
+            repo_id=model_id,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            token=os.getenv("HF_TOKEN")
+        )
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        llm = None
+    return llm
+
+def get_response(system_message, chat_history, user_text, max_new_tokens=256):
+    hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1)
+    if hf is None:
+        return "Error with model inference.", chat_history
+
+    prompt = PromptTemplate.from_template(
+        "[INST] {system_message}\nCurrent Conversation:\n{chat_history}\n\nUser: {user_text}.\n [/INST]\nAI:"
+    )
+    chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content')
+    response = chat.invoke(input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history))
+    response = response.split("AI:")[-1]
+
+    chat_history.append({'role': 'user', 'content': user_text})
+    chat_history.append({'role': 'assistant', 'content': response})
+    return response, chat_history
+
+def chat_function(user_text, uploaded_image, system_message, chat_history):
+    # If an image is uploaded, generate a caption for it
+    if uploaded_image:
+        caption = generate_caption(uploaded_image)
+        chat_history.append({'role': 'user', 'content': f'![uploaded image](data:image/png;base64,{uploaded_image})'})
+        chat_history.append({'role': 'assistant', 'content': caption})
+        # Return the updated chat history
+        return chat_history, chat_history
+
+    # If no image is uploaded, generate a response from the chat model
+    response, updated_history = get_response(system_message, chat_history, user_text)
+    return response, updated_history
+
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Personal HuggingFace ChatBot")
+        
+        with gr.Row():
+            with gr.Column():
+                txt_input = gr.Textbox(label='Enter your text here', lines=4)
+                img_input = gr.Image(label='Upload an image', type='pil')
+                system_message = gr.Textbox(label='System Message', value="You are a friendly AI conversing with a human user.")
+                chat_history = gr.State(value=[{'role': 'assistant', 'content': 'Hello, there! How can I help you today?'}])
+                
+                submit_btn = gr.Button('Submit')
+                response_output = gr.Markdown()
+                gallery_output = gr.Gallery(label="Generated Images", show_download_button=True, elem_classes="gallery", interactive=False, show_share_button=True, container=True)
+                
+                submit_btn.click(chat_function, inputs=[txt_input, img_input, system_message, chat_history], outputs=[response_output, chat_history])
+                img_input.change(lambda img: add_gallery(gen_fn("Generate image of a fantasy scene"), gallery_output), inputs=[img_input], outputs=[gallery_output])
+                
+        demo.launch()
+
+gradio_interface()