Mark 1 app.py

app.py

@@ -1,64 +1,191 @@
 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import os
+from langchain.document_loaders import WebBaseLoader
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_ollama import ChatOllama
+import re
+import torch
+
+
+def preprocessing_text(document: list) -> list:
+    document[0].page_content = re.sub(r"\n{2,}", "\n\n", document[0].page_content)
+    return document
+
+def loading_the_webpage(url: str) -> list:
+    loader = WebBaseLoader(url)
+    document = preprocessing_text(loader.load())
+    return document
+
+
+def chunking(document: list) -> list:
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size= 1024, 
+                                               chunk_overlap= 128, 
+                                               separators= ["\n\n", "\n", " ", ""])
+    return text_splitter.split_documents(documents= document)
+
+
+
+def create_vector_db(chunked_documents):
+    embeddings = OllamaEmbeddings(model= 'nomic-embed-text', show_progress= True)
+    vector_db = FAISS.from_documents(chunked_documents, embeddings)
+    return vector_db
+
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    llm = ChatOllama(model= "mistral",
+                    temperature= temperature,
+                    top_k= top_k, 
+                    num_predict= max_tokens)
+    
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+
+    retriever=vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff", 
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    return qa_chain
+
+def process_url_and_query(url: str, query: str):
+    # Load and process the webpage
+    documents = loading_the_webpage(url)
+    documents = preprocessing_text(documents)
+    
+    # Chunk the documents
+    chunked_documents = chunking(documents)
+    
+    # Create a vector database from chunked documents
+    vector_db = create_vector_db(chunked_documents)
+    
+    # Initialize the LLM chain
+    qa_chain = initialize_llmchain(llm_model="mistral", temperature=0.7, max_tokens=150, top_k=5, vector_db=vector_db)
+    
+    # Get the answer for the user's query
+    answer = qa_chain({"question": query})
+    
+    return answer['answer']
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Webpage Querying App")
+    
+    url_input = gr.Textbox(label="Enter URL")
+    query_input = gr.Textbox(label="Enter your query")
+    
+    submit_button = gr.Button("Submit")
+    
+    output_textbox = gr.Textbox(label="Response", interactive=False)
+
+    submit_button.click(process_url_and_query, inputs=[url_input, query_input], outputs=output_textbox)
+
+# Launch the app
+demo.launch()
+
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+
+# 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)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()