Update app.py

app.py

@@ -1,147 +1,121 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
-import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
-
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
-
-
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
-
-
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
-
-
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
-    )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
-    )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
-
-async def process_inputs(class_names: List[str], image_url: str):
+import os, dotenv, anthropic, panel, platform
+from langchain_community.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader
+
+panel.extension()
+
+# Set API key
+dotenv.load_dotenv()
+ANTHROPIC_API_KEY = os.getenv('ANTHROPIC_API_KEY')
+
+@panel.cache
+def load_vectorstore():
+    if "macOS" in platform.platform():
+        device="mps"
+    else:
+        device="cpu"
+
+    # Create the HF embeddings
+    model_name = "sentence-transformers/all-mpnet-base-v2"
+    model_kwargs = {'device': device}
+    encode_kwargs = {'normalize_embeddings': False}
+
+    hf_embeddings = HuggingFaceEmbeddings(
+                        model_name=model_name,
+                        model_kwargs=model_kwargs,
+                        encode_kwargs=encode_kwargs
+                    )
+
+    # If the vector embeddings of the documents have not been created
+    if not os.path.isfile('chroma_db/chroma.sqlite3'):
+
+        # Load the documents
+        loader = DirectoryLoader('Docs/', glob="./*.pdf", loader_cls=PyPDFLoader)
+        data = loader.load()
+
+        # Split the docs into chunks
+        splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=50
+        )
+        docs = splitter.split_documents(data)
+
+        # Embed the documents and store them in a Chroma DB
+        vectorstore = Chroma.from_documents(documents=docs,embedding=hf_embeddings, persist_directory="./chroma_db")
+    else:
+        # load ChromaDB from disk
+        vectorstore = Chroma(persist_directory="./chroma_db", embedding_function=hf_embeddings)
+
+    return vectorstore
+
+# Initialize the chat history
+chat_history = []
+
+async def get_response(contents, user, instance):
+
+    # Load the vectorstore
+    vectorstore = load_vectorstore()
+
+    question = contents
+
+    # Get the relevant information to form the context on which to query with the LLM
+    docs = vectorstore.similarity_search(question)
+
+    context = "\n"
+    for doc in docs:
+        context += "\n" + doc.page_content + "\n"
+
+    # Update the global chat_history with the user's question
+    global chat_history
+    chat_history.append({"role": "user", "content": question})
+
+    # Define prompt template
+    prompt = f"""
+    Here are the Task Context and History
+
+    - Context: {context}
+    - Chat History: {chat_history}
+    - User Question: {question}
     """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
-        try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
-        except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
-    
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
-    
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
-    
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
-
-
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
-
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
+    # Create the Anthropic client
+    client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)
+
+    response = ''
+    # Generate the completion with the updated chat_history
+    with client.messages.stream(
+        max_tokens=1024,
+        messages=[
+            {"role": "user", "content": prompt}
+        ],
+        model="claude-3-haiku-20240307",
+    ) as stream:
+      for text in stream.text_stream:
+          response += text
+          yield response
+
+    # Append the assistant's response to the chat_history
+    chat_history.append({"role": "assistant", "content": response})
+
+chat_interface = panel.chat.ChatInterface(
+    callback=get_response, 
+    callback_user="Sarathi",
+    sizing_mode="stretch_width", 
+    callback_exception='verbose',
+    message_params=dict(
+                default_avatars={"Sarathi": "S", "User": "U"},
+                reaction_icons={"like": "thumb-up"},
+            ),
 )
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+chat_interface.send(
+    {"user": "Sarathi", "value": '''Welcome to Sarathi, your personal assistant for Assam Tourism.'''},
+    respond=False,
 )
 
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
+template = panel.template.BootstrapTemplate(title="Sarathi", favicon="favicon.png", header_background = "#000000", main=[panel.Tabs( ('Chat', chat_interface), dynamic=True )])
+
+template.servable()
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)