Update app.py

app.py

@@ -1,102 +1,170 @@
+import os
+import json
+import re
 import gradio as gr
-from huggingface_hub import InferenceApi
-from duckduckgo_search import DDGS
 import requests
-import json
+import random
+import urllib.parse
+from tempfile import NamedTemporaryFile
+from bs4 import BeautifulSoup
 from typing import List
 from pydantic import BaseModel, Field
+from huggingface_hub import InferenceApi
+from duckduckgo_search import DDGS
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFaceHub
+from langchain_core.documents import Document
+from sentence_transformers import SentenceTransformer
+from llama_parse import LlamaParse
 
-# Global variables
+# Environment variables and configurations
 huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")
+llama_cloud_api_key = os.environ.get("LLAMA_CLOUD_API_KEY")
+
+# Initialize SentenceTransformer and LlamaParse
+sentence_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+llama_parser = LlamaParse(
+    api_key=llama_cloud_api_key,
+    result_type="markdown",
+    num_workers=4,
+    verbose=True,
+    language="en",
+)
+
+def load_document(file: NamedTemporaryFile, parser: str = "pypdf") -> List[Document]:
+    if parser == "pypdf":
+        loader = PyPDFLoader(file.name)
+        return loader.load_and_split()
+    elif parser == "llamaparse":
+        try:
+            documents = llama_parser.load_data(file.name)
+            return [Document(page_content=doc.text, metadata={"source": file.name}) for doc in documents]
+        except Exception as e:
+            print(f"Error using Llama Parse: {str(e)}")
+            print("Falling back to PyPDF parser")
+            loader = PyPDFLoader(file.name)
+            return loader.load_and_split()
+    else:
+        raise ValueError("Invalid parser specified. Use 'pypdf' or 'llamaparse'.")
+
+def update_vectors(files, parser):
+    if not files:
+        return "Please upload at least one PDF file."
+    
+    embed = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    total_chunks = 0
+    
+    all_data = []
+    for file in files:
+        data = load_document(file, parser)
+        all_data.extend(data)
+        total_chunks += len(data)
+    
+    if os.path.exists("faiss_database"):
+        database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
+        database.add_documents(all_data)
+    else:
+        database = FAISS.from_documents(all_data, embed)
+    
+    database.save_local("faiss_database")
+    
+    return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files using {parser}."
+
+def clear_cache():
+    if os.path.exists("faiss_database"):
+        os.remove("faiss_database")
+        return "Cache cleared successfully."
+    else:
+        return "No cache to clear."
+
+def get_model(temperature, top_p, repetition_penalty):
+    return HuggingFaceHub(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+        model_kwargs={
+            "temperature": temperature,
+            "top_p": top_p,
+            "repetition_penalty": repetition_penalty,
+            "max_length": 1000
+        },
+        huggingfacehub_api_token=huggingface_token
+    )
 
-# Function to perform a DuckDuckGo search
 def duckduckgo_search(query):
     with DDGS() as ddgs:
         results = ddgs.text(query, max_results=5)
     return results
 
-class CitingSources(BaseModel):
-    sources: List[str] = Field(
-        ...,
-        description="List of sources to cite. Should be an URL of the source."
-    )
+def get_response_with_search(query, temperature, top_p, repetition_penalty, use_pdf=False):
+    model = get_model(temperature, top_p, repetition_penalty)
+    embed = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+
+    if use_pdf and os.path.exists("faiss_database"):
+        database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
+        retriever = database.as_retriever()
+        relevant_docs = retriever.get_relevant_documents(query)
+        context = "\n".join([f"Content: {doc.page_content}\nSource: {doc.metadata['source']}\n" for doc in relevant_docs])
+    else:
+        search_results = duckduckgo_search(query)
+        context = "\n".join(f"{result['title']}\n{result['body']}\nSource: {result['href']}\n" 
+                            for result in search_results if 'body' in result)
 
-def get_response_with_search(query):
-    # Perform the web search
-    search_results = duckduckgo_search(query)
-    
-    # Use the search results as context for the model
-    context = "\n".join(f"{result['title']}\n{result['body']}\nSource: {result['href']}\n" 
-                        for result in search_results if 'body' in result)
-    
-    # Prompt formatted for Mistral-7B-Instruct
     prompt = f"""<s>[INST] Using the following context:
 {context}
 Write a detailed and complete research document that fulfills the following user request: '{query}'
 After writing the document, please provide a list of sources used in your response. [/INST]"""
+
+    response = model(prompt)
     
-    # API endpoint for Mistral-7B-Instruct-v0.3
-    API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
+    main_content, sources = split_response(response)
     
-    # Headers
-    headers = {"Authorization": f"Bearer {huggingface_token}"}
+    return main_content, sources
+
+def split_response(response):
+    parts = response.split("Sources:", 1)
+    main_content = parts[0].strip()
+    sources = parts[1].strip() if len(parts) > 1 else ""
+    return main_content, sources
+
+def chatbot_interface(message, history, temperature, top_p, repetition_penalty, use_pdf):
+    main_content, sources = get_response_with_search(message, temperature, top_p, repetition_penalty, use_pdf)
+    formatted_response = f"{main_content}\n\nSources:\n{sources}"
+    return formatted_response
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# AI-powered Web Search and PDF Chat Assistant")
     
-    # Payload
-    payload = {
-        "inputs": prompt,
-        "parameters": {
-            "max_new_tokens": 1000,
-            "temperature": 0.7,
-            "top_p": 0.95,
-            "top_k": 40,
-            "repetition_penalty": 1.1
-        }
-    }
+    with gr.Row():
+        file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"])
+        parser_dropdown = gr.Dropdown(choices=["pypdf", "llamaparse"], label="Select PDF Parser", value="pypdf")
+        update_button = gr.Button("Upload PDF")
     
-    # Make the API call
-    response = requests.post(API_URL, headers=headers, json=payload)
+    update_output = gr.Textbox(label="Update Status")
+    update_button.click(update_vectors, inputs=[file_input, parser_dropdown], outputs=update_output)
     
-    if response.status_code == 200:
-        result = response.json()
-        if isinstance(result, list) and len(result) > 0:
-            generated_text = result[0].get('generated_text', 'No text generated')
-            
-            # Remove the instruction part
-            content_start = generated_text.find("[/INST]")
-            if content_start != -1:
-                generated_text = generated_text[content_start + 7:].strip()
-            
-            # Split the response into main content and sources
-            parts = generated_text.split("Sources:", 1)
-            main_content = parts[0].strip()
-            sources = parts[1].strip() if len(parts) > 1 else ""
-            
-            return main_content, sources
-        else:
-            return f"Unexpected response format: {result}", ""
-    else:
-        return f"Error: API returned status code {response.status_code}", ""
+    with gr.Row():
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(label="Conversation")
+            msg = gr.Textbox(label="Ask a question")
+            submit_button = gr.Button("Submit")
+        with gr.Column(scale=1):
+            temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.7, step=0.1)
+            top_p = gr.Slider(label="Top P", minimum=0.0, maximum=1.0, value=0.95, step=0.05)
+            repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.1, step=0.1)
+            use_pdf = gr.Checkbox(label="Use PDF Documents", value=False)
 
-def chatbot_interface(message, history):
-    main_content, sources = get_response_with_search(message)
-    formatted_response = f"{main_content}\n\nSources:\n{sources}"
-    return formatted_response
+    def respond(message, chat_history, temperature, top_p, repetition_penalty, use_pdf):
+        bot_message = chatbot_interface(message, chat_history, temperature, top_p, repetition_penalty, use_pdf)
+        chat_history.append((message, bot_message))
+        return "", chat_history
 
-# Gradio chatbot interface
-iface = gr.ChatInterface(
-    fn=chatbot_interface,
-    title="AI-powered Web Search Assistant",
-    description="Ask questions, and I'll search the web and provide answers using the Mistral-7B-Instruct model.",
-    examples=[
-        ["What are the latest developments in AI?"],
-        ["Tell me about recent updates on GitHub"],
-        ["What are the best hotels in Galapagos, Ecuador?"],
-        ["Summarize recent advancements in Python programming"],
-    ],
-    retry_btn="Retry",
-    undo_btn="Undo",
-    clear_btn="Clear",
-)
+    submit_button.click(respond, inputs=[msg, chatbot, temperature, top_p, repetition_penalty, use_pdf], outputs=[msg, chatbot])
+    
+    clear_button = gr.Button("Clear Cache")
+    clear_output = gr.Textbox(label="Cache Status")
+    clear_button.click(clear_cache, inputs=[], outputs=clear_output)
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()