Update to app.py to rag system

app.py

@@ -1,64 +1,81 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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": message})
-
-    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
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
+import numpy as np
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+from datasets import load_dataset
+import torch
+
+# Initialize models
+retriever = SentenceTransformer("all-MiniLM-L6-v2")
+generator = AutoModelForCausalLM.from_pretrained("distilgpt2")
+tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+
+
+# Simple vector store
+class VectorStore:
+    def __init__(self):
+        self.documents = []
+        self.embeddings = []
+
+    def add_document(self, document):
+        self.documents.append(document)
+        embedding = retriever.encode(document)
+        self.embeddings.append(embedding)
+
+    def search(self, query, k=3):
+        query_embedding = retriever.encode(query)
+        similarities = np.dot(self.embeddings, query_embedding) / (
+            np.linalg.norm(self.embeddings, axis=1) * np.linalg.norm(query_embedding)
+        )
+        top_k_indices = np.argsort(similarities)[-k:][::-1]
+        return [self.documents[i] for i in top_k_indices]
+
+
+# Initialize vector store
+vector_store = VectorStore()
+
+# Load sample dataset (e.g., Wikipedia snippets)
+dataset = load_dataset(
+    "wikipedia", "20220301.simple", split="train[:1000]", trust_remote_code=True
 )
+for doc in dataset["text"]:
+    vector_store.add_document(doc)
+
 
+# RAG function
+def rag_query(query, max_length=100):
+    # Retrieve relevant documents
+    retrieved_docs = vector_store.search(query)
+    context = " ".join(retrieved_docs)
+
+    # Generate response
+    input_text = f"Context: {context}\n\nQuestion: {query}\nAnswer:"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
+
+    with torch.no_grad():
+        outputs = generator.generate(
+            inputs.input_ids,
+            max_length=max_length + len(inputs.input_ids[0]),
+            num_return_sequences=1,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("Answer:")[-1].strip()
+
+
+# Gradio interface
+def gradio_interface(query):
+    return rag_query(query)
+
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(label="Enter your question"),
+    outputs=gr.Textbox(label="Answer"),
+    title="RAG System with Hugging Face and Gradio",
+    description="Ask questions based on a Wikipedia-based knowledge base.",
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()