Update app.py

app.py

@@ -1,218 +1,24 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import TextLoader
-
-# Initialize the Hugging Face Inference client with an open-source LLM
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")  # You can use any supported model
-
-# Sample knowledge base for Crustdata APIs
-docs = """
-# Crustdata Dataset API
-
-## Description
-The Crustdata Dataset API provides access to a wide variety of datasets across different domains. It allows users to search, filter, and retrieve datasets based on categories, tags, and other metadata.
-
-## Key Endpoints
-
-### 1. **GET /datasets**
-- **Description**: Retrieves a list of available datasets.
-- **Parameters**:
-  - `category` (optional): Filter datasets by a specific category.
-  - `tags` (optional): Filter datasets by tags (comma-separated).
-  - `limit` (optional): Maximum number of datasets to return (default: 10).
-
-- **Example Request**:
-  ```bash
-  curl -X GET "https://api.crustdata.com/datasets?category=finance&tags=economy,stocks&limit=5"
-  ```
-
-- **Example Response**:
-  ```json
-  {
-    "datasets": [
-      {
-        "id": "12345",
-        "name": "Global Finance Dataset",
-        "category": "finance",
-        "tags": ["economy", "stocks"]
-      },
-      ...
-    ]
-  }
-  ```
-
-### 2. **GET /datasets/{id}**
-- **Description**: Retrieves detailed information about a specific dataset.
-- **Parameters**:
-  - `id` (required): The unique identifier of the dataset.
-
-- **Example Request**:
-  ```bash
-  curl -X GET "https://api.crustdata.com/datasets/12345"
-  ```
-
-- **Example Response**:
-  ```json
-  {
-    "id": "12345",
-    "name": "Global Finance Dataset",
-    "description": "A comprehensive dataset on global financial markets.",
-    "category": "finance",
-    "tags": ["economy", "stocks"],
-    "source": "World Bank"
-  }
-  ```
-
----
-
-# Crustdata Discovery and Enrichment API
-
-## Description
-The Crustdata Discovery and Enrichment API allows users to enrich their datasets by adding metadata, geolocation information, and other relevant attributes.
-
-## Key Endpoints
-
-### 1. **POST /enrich**
-- **Description**: Enriches input data with additional metadata based on the specified enrichment type.
-- **Parameters**:
-  - `input_data` (required): A list of data entries to be enriched.
-  - `enrichment_type` (required): The type of enrichment to apply. Supported types:
-    - `geolocation`
-    - `demographics`
-
-- **Example Request**:
-  ```bash
-  curl -X POST "https://api.crustdata.com/enrich" \
-    -H "Content-Type: application/json" \
-    -d '{
-          "input_data": [{"address": "123 Main St, Springfield"}],
-          "enrichment_type": "geolocation"
-        }'
-  ```
-
-- **Example Response**:
-  ```json
-  {
-    "enriched_data": [
-      {
-        "address": "123 Main St, Springfield",
-        "latitude": 37.12345,
-        "longitude": -93.12345
-      }
-    ]
-  }
-  ```
-
-### 2. **POST /search**
-- **Description**: Searches for relevant metadata or datasets based on user-provided criteria.
-- **Parameters**:
-  - `query` (required): The search term or query string.
-  - `filters` (optional): Additional filters to narrow down the search results.
-  
-- **Example Request**:
-  ```bash
-  curl -X POST "https://api.crustdata.com/search" \
-    -H "Content-Type: application/json" \
-    -d '{
-          "query": "energy consumption",
-          "filters": {"category": "energy"}
-        }'
-  ```
-
-- **Example Response**:
-  ```json
-  {
-    "results": [
-      {
-        "id": "67890",
-        "name": "Energy Consumption Dataset",
-        "category": "energy",
-        "tags": ["consumption", "renewables"]
-      }
-    ]
-  }
-  ```
-
----
-
-# General Notes
-- All endpoints require authentication using an API key.
-- API requests must include the `Authorization` header:
-  ```plaintext
-  Authorization: Bearer YOUR_API_KEY
-  ```
-- Response format: JSON
-- Base URL: `https://api.crustdata.com`
-"""
-
-# Split the documentation into smaller chunks
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-doc_chunks = text_splitter.create_documents([docs])
-
-# Create embeddings and initialize FAISS vector store
-embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
-embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
-docsearch = FAISS.from_documents(doc_chunks, embeddings)
-
-
-def retrieve_context(query):
-    """Retrieve the most relevant context from the knowledge base."""
-    results = docsearch.similarity_search(query, k=2)  # Retrieve top 2 most similar chunks
-    context = "\n".join([res.page_content for res in results])
-    return context
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    """Generate a response using the Hugging Face Inference API."""
-    # Retrieve relevant context from the knowledge base
-    context = retrieve_context(message)
-    prompt = f"{system_message}\n\nContext:\n{context}\n\nUser: {message}\nAssistant:"
-
-    messages = [{"role": "system", "content": system_message}]
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": prompt})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
-
-
-# Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a technical assistant for Crustdata APIs.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
-    ],
-    title="Crustdata API Chatbot",
-    description="Ask any technical questions about Crustdata’s Dataset and Discovery APIs.",
+from diffusers import StableDiffusionPipeline
+import torch
+
+# Load the Ghibli LoRA model
+model_id = "openfree/flux-chatgpt-ghibli-lora"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe.to("cpu")  # Ensure you have GPU enabled
+
+def convert_to_ghibli(image):
+    prompt = "A Ghibli-style anime scene, highly detailed, vibrant colors"
+    result = pipe(prompt, image=image).images[0]
+    return result
+
+# Create Gradio UI
+iface = gr.Interface(
+    fn=convert_to_ghibli,
+    inputs=gr.Image(type="pil"),
+    outputs="image",
+    title="Ghibli Image Converter",
+    description="Upload an image and convert it to a Studio Ghibli-style artwork using AI."
 )
 
-if __name__ == "__main__":
-    demo.launch(share=True)
+iface.launch()