Update space

app.py

@@ -1,63 +1,100 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
+import groq
+import warnings
+import asyncio
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.llms.groq import Groq
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 
-"""
-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)",
-        ),
-    ],
-)
+# A warning may appear which doesn't 
+# affect the operation of the code
+# Suppress it with this code
+warnings.filterwarnings("ignore", message=".*clean_up_tokenization_spaces.*")
 
+# Global variables
+index = None
+query_engine = None
 
+# Initialize Groq LLM and ensure it is used
+llm = Groq(model="mixtral-8x7b-32768")
+Settings.llm = llm  # Ensure Groq is the LLM being used
+
+# Initialize our chosen embedding model
+embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# These are our RAG fucntions, called in response to user
+# initiated events e.g clicking the Load Documents button
+# on the GUI
+#
+def load_documents(file_objs):
+    global index, query_engine
+    try:
+        if not file_objs:
+            return "Error: No files selected."
+
+        documents = []
+        document_names = []
+        for file_obj in file_objs:
+            document_names.append(file_obj.name)
+            loaded_docs = SimpleDirectoryReader(input_files=[file_obj.name]).load_data()
+            documents.extend(loaded_docs)
+
+        if not documents:
+            return "No documents found in the selected files."
+
+        # Create index from documents using Groq LLM and HuggingFace Embeddings
+        index = VectorStoreIndex.from_documents(
+            documents,
+            llm=llm,  # Ensure Groq is used here
+            embed_model=embed_model
+        )
+
+        # Create query engine
+        query_engine = index.as_query_engine()
+
+        return f"Successfully loaded {len(documents)} documents from the files: {', '.join(document_names)}"
+    except Exception as e:
+        return f"Error loading documents: {str(e)}"
+
+async def perform_rag(query, history):
+    global query_engine
+    if query_engine is None:
+        return history + [("Please load documents first.", None)]
+    try:
+        response = await asyncio.to_thread(query_engine.query, query)
+        return history + [(query, str(response))]
+    except Exception as e:
+        return history + [(query, f"Error processing query: {str(e)}")]
+
+def clear_all():
+    global index, query_engine
+    index = None
+    query_engine = None
+    return None, "", [], ""  # Reset file input, load output, chatbot, and message input to default states
+
+
+# Create the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# RAG Multi-file Chat Application")
+
+    with gr.Row():
+        file_input = gr.File(label="Select PDF files to load", file_count="multiple")
+        load_btn = gr.Button("Load Documents")
+
+    load_output = gr.Textbox(label="Load Status")
+
+    msg = gr.Textbox(label="Enter your question")
+    chatbot = gr.Chatbot()  
+    clear = gr.Button("Clear")
+
+    # Set up event handlers
+    load_btn.click(load_documents, inputs=[file_input], outputs=[load_output])
+    msg.submit(perform_rag, inputs=[msg, chatbot], outputs=[chatbot])
+    clear.click(clear_all, outputs=[file_input, load_output, chatbot, msg], queue=False)
+
+# Run the app
 if __name__ == "__main__":
+    demo.queue()
     demo.launch()

requirements.txt

@@ -1 +1,8 @@
-huggingface_hub==0.22.2
+huggingface_hub==0.22.2
+gradio
+groq
+llama-index-llms-groq 
+llama_index
+openpyxl
+llama-index-embeddings-huggingface
+doc2txt