Create app.py

app.py

@@ -0,0 +1,289 @@
+import os
+from langchain_huggingface import HuggingFaceEndpoint, HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains import RetrievalQA
+
+import gradio as gr
+import warnings
+import uuid
+
+
+MODEL_OPTIONS = [
+    "meta-llama/Llama-3.2-3B-Instruct",
+    "meta-llama/Llama-3.1-8B-Instruct",
+    "mistralai/Mistral-7B-Instruct-v0.3",
+    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "google/gemma-2-9b-it",
+    "google/gemma-2-27b-it",
+    "Qwen/Qwen2.5-7B-Instruct",
+    "Qwen/Qwen2.5-14B-Instruct",
+    "microsoft/Phi-3.5-mini-instruct",
+    "HuggingFaceH4/zephyr-7b-beta"
+]
+
+
+# Suppress warnings
+def warn(*args, **kwargs):
+    pass
+
+
+warnings.warn = warn
+warnings.filterwarnings("ignore")
+
+
+# ---------------------------
+# Get credentials from environment variables
+# ---------------------------
+def get_huggingface_token():
+    """
+    Get HuggingFace API token from environment.
+    Set this in your Space settings under Settings > Repository secrets:
+    - HF_TOKEN or HUGGINGFACE_TOKEN
+    """
+    token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_TOKEN")
+    
+    if not token:
+        raise ValueError(
+            "HF_TOKEN not found. Please set it in your HuggingFace Space secrets."
+        )
+    
+    return token
+
+
+# ---------------------------
+# LLM
+# ---------------------------
+def get_llm(model_id: str = MODEL_OPTIONS[0], max_tokens: int = 256, temperature: float = 0.8):
+    token = get_huggingface_token()
+    
+    llm = HuggingFaceEndpoint(
+        repo_id=model_id,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        huggingfacehub_api_token=token,
+    )
+    return llm
+
+
+# ---------------------------
+# Document loader
+# ---------------------------
+def document_loader(file):
+    # Handle file path string from Gradio
+    file_path = file if isinstance(file, str) else file.name
+    loader = PyPDFLoader(file_path)
+    loaded_document = loader.load()
+    return loaded_document
+
+
+# ---------------------------
+# Text splitter
+# ---------------------------
+def text_splitter(data, chunk_size: int = 500, chunk_overlap: int = 50):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        length_function=len,
+    )
+    chunks = splitter.split_documents(data)
+    return chunks
+
+
+# ---------------------------
+# Embedding model
+# ---------------------------
+def get_embedding_model(model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
+    """
+    Create HuggingFace embedding model.
+    Using sentence-transformers for efficient embeddings.
+    """
+    embedding = HuggingFaceEmbeddings(
+        model_name=model_name,
+        model_kwargs={'device': 'cpu'},
+        encode_kwargs={'normalize_embeddings': True}
+    )
+    return embedding
+
+
+# ---------------------------
+# Vector DB
+# ---------------------------
+def vector_database(chunks, embedding_model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
+    embedding_model = get_embedding_model(embedding_model_name)
+    
+    # Create unique collection name to avoid reusing cached data
+    collection_name = f"rag_collection_{uuid.uuid4().hex[:8]}"
+
+    vectordb = Chroma.from_documents(
+        chunks, 
+        embedding_model,
+        collection_name=collection_name
+    )
+    return vectordb
+
+
+# ---------------------------
+# Retriever
+# ---------------------------
+def retriever(file, chunk_size: int = 500, chunk_overlap: int = 50, embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2"):
+    splits = document_loader(file)
+    chunks = text_splitter(splits, chunk_size, chunk_overlap)
+    vectordb = vector_database(chunks, embedding_model)
+    retriever_obj = vectordb.as_retriever()
+    return retriever_obj
+
+
+# ---------------------------
+# QA Chain
+# ---------------------------
+def retriever_qa(file, query, model_choice, max_tokens, temperature, embedding_model, chunk_size, chunk_overlap):
+    if not file:
+        return "Please upload a PDF file first."
+    
+    if not query.strip():
+        return "Please enter a query."
+    
+    try:
+        selected_model = model_choice or MODEL_OPTIONS[0]
+        llm = get_llm(selected_model, int(max_tokens), float(temperature))
+        retriever_obj = retriever(file, int(chunk_size), int(chunk_overlap), embedding_model)
+        qa = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=retriever_obj,
+            return_source_documents=True,
+        )
+        response = qa.invoke({"query": query})
+        return response['result']
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# ---------------------------
+# Gradio Interface
+# ---------------------------
+with gr.Blocks(title="QA Bot - PDF Question Answering") as demo:
+    gr.Markdown("# �� QA Bot - PDF Question Answering")
+    gr.Markdown(
+        "Upload a PDF document and ask questions about its content. "
+        "Powered by HuggingFace models and LangChain."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            file_input = gr.File(
+                label="Upload PDF File",
+                file_count="single",
+                file_types=[".pdf"],
+                type="filepath"
+            )
+            
+            query_input = gr.Textbox(
+                label="Your Question",
+                lines=3,
+                placeholder="Ask a question about the uploaded document..."
+            )
+            
+            model_dropdown = gr.Dropdown(
+                label="LLM Model",
+                choices=MODEL_OPTIONS,
+                value=MODEL_OPTIONS[0],
+            )
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False):
+                max_tokens_slider = gr.Slider(
+                    label="Max New Tokens",
+                    minimum=50,
+                    maximum=2048,
+                    value=256,
+                    step=1,
+                    info="Maximum number of tokens in the generated output"
+                )
+                
+                temperature_slider = gr.Slider(
+                    label="Temperature",
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=0.8,
+                    step=0.1,
+                    info="Controls randomness/creativity of responses"
+                )
+                
+                truncate_slider = gr.Dropdown(
+                    label="Embedding Model",
+                    choices=[
+                        "sentence-transformers/all-MiniLM-L6-v2",
+                        "sentence-transformers/all-mpnet-base-v2",
+                        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+                        "BAAI/bge-small-en-v1.5",
+                        "BAAI/bge-base-en-v1.5"
+                    ],
+                    value="sentence-transformers/all-MiniLM-L6-v2",
+                    info="Model used for generating embeddings"
+                )
+                
+                chunk_size_slider = gr.Slider(
+                    label="Chunk Size",
+                    minimum=100,
+                    maximum=2000,
+                    value=500,
+                    step=50,
+                    info="Size of text chunks for processing"
+                )
+                
+                chunk_overlap_slider = gr.Slider(
+                    label="Chunk Overlap",
+                    minimum=0,
+                    maximum=500,
+                    value=50,
+                    step=10,
+                    info="Overlap between consecutive chunks"
+                )
+            
+            submit_btn = gr.Button("Ask Question", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_text = gr.Textbox(
+                label="Answer",
+                lines=15,
+                show_copy_button=True
+            )
+    
+    submit_btn.click(
+        fn=retriever_qa,
+        inputs=[
+            file_input,
+            query_input,
+            model_dropdown,
+            max_tokens_slider,
+            temperature_slider,
+            truncate_slider,
+            chunk_size_slider,
+            chunk_overlap_slider
+        ],
+        outputs=output_text
+    )
+    
+    gr.Markdown(
+        """
+        ### 📝 Instructions
+        1. Upload a PDF document
+        2. Enter your question in the text box
+        3. (Optional) Select a different LLM model
+        4. (Optional) Adjust advanced settings for fine-tuning
+        5. Click "Ask Question" to get an answer
+        
+        ### 🔐 Setup
+        This Space requires a HuggingFace API token. Set the following in your Space secrets:
+        - `HF_TOKEN`: Your HuggingFace API token (get it from https://huggingface.co/settings/tokens)
+        """
+    )
+
+
+# ---------------------------
+# Launch the app
+# ---------------------------
+if __name__ == "__main__":
+    demo.launch()