Create app.py

app.py

@@ -0,0 +1,167 @@
+from llama_index.llms import HuggingFaceInferenceAPI
+from llama_index.llms import ChatMessage, MessageRole
+from llama_index.prompts import ChatPromptTemplate
+from llama_index import VectorStoreIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext, StorageContext, load_index_from_storage
+import gradio as gr
+import sys
+import logging
+import torch
+from huggingface_hub import InferenceClient
+import tqdm as notebook_tqdm
+import requests
+
+def download_file(url, filename):
+    """
+    Download a file from the specified URL and save it locally under the given filename.
+    """
+    response = requests.get(url, stream=True)
+
+    # Check if the request was successful
+    if response.status_code == 200:
+        with open(filename, 'wb') as file:
+            for chunk in response.iter_content(chunk_size=1024):
+                if chunk:  # filter out keep-alive new chunks
+                    file.write(chunk)
+        print(f"Download complete: {filename}")
+    else:
+        print(f"Error: Unable to download file. HTTP status code: {response.status_code}")
+
+def generate(prompt, history, file_link, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,):
+    mixtral = HuggingFaceInferenceAPI(
+        model_name="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+
+    service_context = ServiceContext.from_defaults(
+        llm=mixtral, embed_model="local:BAAI/bge-small-en-v1.5"
+    )
+
+
+    download = download_file(file_link,file_link.split("/")[-1])
+
+    documents = SimpleDirectoryReader("/content").load_data()
+    index = VectorStoreIndex.from_documents(documents,service_context=service_context)
+
+    # Text QA Prompt
+    chat_text_qa_msgs = [
+        ChatMessage(
+            role=MessageRole.SYSTEM,
+            content=(
+                "Always answer the question, even if the context isn't helpful."
+            ),
+        ),
+        ChatMessage(
+            role=MessageRole.USER,
+            content=(
+                "Context information is below.\n"
+                "---------------------\n"
+                "{context_str}\n"
+                "---------------------\n"
+                "Given the context information and not prior knowledge, "
+                "answer the question: {query_str}\n"
+            ),
+        ),
+    ]
+    text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)
+
+    # Refine Prompt
+    chat_refine_msgs = [
+        ChatMessage(
+            role=MessageRole.SYSTEM,
+            content=(
+                "Always answer the question, even if the context isn't helpful."
+            ),
+        ),
+        ChatMessage(
+            role=MessageRole.USER,
+            content=(
+                "We have the opportunity to refine the original answer "
+                "(only if needed) with some more context below.\n"
+                "------------\n"
+                "{context_msg}\n"
+                "------------\n"
+                "Given the new context, refine the original answer to better "
+                "answer the question: {query_str}. "
+                "If the context isn't useful, output the original answer again.\n"
+                "Original Answer: {existing_answer}"
+            ),
+        ),
+    ]
+    refine_template = ChatPromptTemplate(chat_refine_msgs)
+
+    stream= index.as_query_engine(
+          text_qa_template=text_qa_template, refine_template=refine_template, similarity_top_k=6
+      ).query(prompt)
+    print(str(stream))
+
+    output=""
+
+    for response in str(stream):
+          output += response
+          yield output
+    return output
+
+def upload_file(files):
+    file_paths = [file.name for file in files]
+    return file_paths
+
+additional_inputs=[
+    gr.Textbox(
+        label="File Link",
+        max_lines=1,
+        interactive=True,
+        value="https://arxiv.org/pdf/2401.10020.pdf"
+    ),
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=1024,
+        minimum=0,
+        maximum=2048,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+examples=[["Explain the paper and describe its novelty", None, None, None, None, None, ],
+          ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
+          ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
+          ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
+          ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
+          ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
+         ]
+
+gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    additional_inputs=additional_inputs,
+    title="RAG Demo",
+    examples=examples,
+    concurrency_limit=20,
+).launch(show_api=False,debug=True)