Initial commit

Qbot.py

@@ -0,0 +1,184 @@
+from langchain_community.llms import HuggingFaceHub
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains import RetrievalQA
+import gradio as gr
+
+#ignoring Unnecessary warnings
+def warn(*arg, **kwargs):
+    pass
+import warnings
+warnings.warn = warn
+warnings.filterwarnings("ignore")
+
+#Initialize LLM
+
+def get_llm(model_id, temperature, max_new_tokens):
+
+    huggingface_api_key = "" 
+
+    #initialize the pipeline
+    Qbot_llm = HuggingFaceHub(
+        repo_id=model_id,
+        model_kwargs={"max_new_tokens": max_new_tokens, "temperature": temperature},
+        huggingfacehub_api_token=huggingface_api_key
+    )
+    return Qbot_llm
+
+#Document Loader
+def document_loader(file):
+    loader = PyPDFLoader(file.name)
+    loaded_document = loader.load()
+    return loaded_document
+
+#Define Text Splitter
+def text_splitter(data):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 1000,
+        chunk_overlap = 50,
+        length_function = len,
+    )
+    chunks = text_splitter.split_documents(data)
+    return chunks
+
+#Define Vector store
+def vector_database(chunks, embedding_model_name):
+    embedding_model = HuggingFaceEmbeddings(model_name = embedding_model_name)
+    vectordb = Chroma.from_documents(chunks, embedding_model)
+    return vectordb
+
+#Define Embedding Model
+def huggingface_embeddings(model_name):
+    huggingface_embedding = HuggingFaceEmbeddings(model_name=model_name)
+    
+    return huggingface_embedding
+
+#Define Retriever
+def retriever(file, embedding_model_name):
+    splits = document_loader(file)
+    chunks = text_splitter(splits)
+    vectordb = vector_database(chunks, embedding_model_name)
+    retriever = vectordb.as_retriever()
+    return retriever
+
+#Define a Question Answering Chain
+#QA chain
+def retriever_qa(file, query, llm_model, temperature, max_new_tokens, embedding_model):
+    llm = get_llm(llm_model, temperature, max_new_tokens)
+    retriever_obj = retriever(file, embedding_model)
+    qa = RetrievalQA.from_chain_type(llm = llm,
+                                     chain_type = "stuff",
+                                     retriever = retriever_obj,
+                                     return_source_documents = False
+                                     )
+    response = qa.invoke(query)
+    return response["result"]
+
+llm_models = [
+    "EleutherAI/gpt-neo-2.7B",
+    "google/flan-t5-large",
+    "google/flan-t5-xl",
+]
+
+embedding_models = [
+    "sentence-transformers/all-distilroberta-v1",
+    "sentence-transformers/all-mpnet-base-v2",
+    "sentence-transformers/all-MiniLM-L6-v2",
+]
+
+# CSS for custom styling
+custom_css = """
+#component-0 {
+    max-width: 800px;
+    margin: auto;
+    padding: 20px;
+}
+.gradio-container {
+    font-family: 'Arial', sans-serif;
+}
+.gr-button {
+    background: linear-gradient(90deg, #4CAF50 0%, #45a049 100%);
+    border: none;
+    color: white;
+}
+.gr-button:hover {
+    background: linear-gradient(90deg, #45a049 0%, #4CAF50 100%);
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+}
+.gr-form {
+    background-color: #ffffff;
+    border-radius: 12px;
+    padding: 20px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+.gr-box {
+    border-radius: 8px;
+    border: 1px solid #e0e0e0;
+}
+"""
+
+#Gradio Interface
+rag_application = gr.Interface(
+    fn = retriever_qa,
+    allow_flagging = "never",
+    inputs=[
+        gr.File(label="Upload PDF File", file_count="single", file_types=[".pdf"],type="filepath", elem_classes="gr-box"), # Drag and drop file upload
+        gr.Textbox(label = "Input Query", lines=2, placeholder="Type your question here...", elem_classes="gr-box"),
+        gr.Dropdown(choices=llm_models, value="EleutherAI/gpt-neo-2.7B", label="LLM Model", elem_classes="gr-box"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.1, label="Temperature", elem_classes="gr-box"),
+        gr.Slider(minimum=64, maximum=512, value=128, step=32, label="Max Tokens", elem_classes="gr-box"),
+        gr.Dropdown(choices=embedding_models, value="sentence-transformers/all-distilroberta-v1", label="Embedding Model", elem_classes="gr-box")
+    ],
+    outputs=gr.Textbox(label="Output"),
+    title = "📚 QBot - Your PDF Assistant",
+    description="""
+    ### Welcome to QBot - Your Intelligent PDF Analysis Companion! 
+    
+    Transform any PDF document into an interactive knowledge base. Ask questions naturally and get precise answers powered by advanced language models.
+    
+    #### Features:
+    🔍 Intelligent PDF Processing
+    💡 Multiple Language Models
+    🎯 Customizable Response Settings
+    🔄 Various Embedding Options
+    
+    #### How to Use:
+    1. **Upload PDF**: Drop your document in the file uploader
+    2. **Ask Questions**: Type any question about your document
+    3. **Customize Settings**:
+       - Choose your preferred Language Model
+       - Adjust Temperature (0-1) for response creativity
+       - Set Max Tokens for response length
+       - Select Embedding Model for document processing
+    4. **Get Answers**: Receive AI-powered responses from your document
+    """,
+    article="""
+    #### Advanced Tips:
+    📊 **Model Selection**:
+    - GPT-Neo 2.7B: Best for general-purpose queries
+    - FLAN-T5 Large: Efficient for straightforward questions
+    - FLAN-T5 XL: Ideal for complex analysis
+    
+    🎛️ **Parameter Guide**:
+    - Temperature: Lower (0.1-0.4) for factual, Higher (0.6-0.9) for creative
+    - Max Tokens: 128 for brief answers, 256+ for detailed explanations
+    - Embedding Models: Choose based on document complexity and language
+    
+    💫 Powered by LangChain and Hugging Face
+    Made with 🤖 for seamless document interaction
+    """,
+    theme=gr.themes.Soft(
+        primary_hue="green",
+        secondary_hue="gray",
+        neutral_hue="gray",
+        radius_size=gr.themes.sizes.radius_sm,
+        font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"]
+    ),
+    css=custom_css
+)
+
+#Launch app
+rag_application.launch(share=True)

README.md

@@ -1,14 +1,6 @@
 ---
-title: Gradio Space
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
+title: Qbot
+app_file: Qbot.py
 sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Geadio app
+sdk_version: 4.44.0
 ---
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -1,64 +0,0 @@
-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()