Update idea.txt

idea.txt

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+print("start1")
+import os
+import sys
+import subprocess
+import gradio as gr
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.schema import Document
+print("start2")
+
+
+# Check if already installed to avoid reinstalling
+try:
+    import llama_cpp
+    print("llama_cpp already installed.")
+except ImportError:
+    print("Installing llama_cpp from wheel...")
+    subprocess.check_call([
+    sys.executable, "-m", "pip", "install",
+    "llama-cpp-python", "--no-binary", ":all:", "--force-reinstall"
+])
+
+
+from llama_cpp import Llama
+print("start3")
+import warnings
+warnings.filterwarnings("ignore")
+
+print("Start")
+import subprocess
+
+subprocess.run([
+    "huggingface-cli", "download",
+    "TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
+    "mistral-7b-instruct-v0.1.Q2_K.gguf",
+    "--local-dir", "./models",
+    "--local-dir-use-symlinks", "False"
+], check=True)
+
+# ------------------------------
+# Device and Embedding Setup (CPU optimized)
+# ------------------------------
+modelPath = "sentence-transformers/all-mpnet-base-v2"
+model_kwargs = {"device": "cpu"}  # Force CPU usage
+encode_kwargs = {"normalize_embedding": False}
+
+embeddings = HuggingFaceEmbeddings(
+    model_name=modelPath,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+# ------------------------------
+# Load Mistral GGUF via llama.cpp (CPU optimized)
+# ------------------------------
+llm_cpp = Llama(
+    model_path="./models/mistral-7b-instruct-v0.1.Q2_K.gguf",
+    n_ctx=2048,
+    n_threads=4,  # Adjust based on your CPU cores
+    n_gpu_layers=0,  # Force CPU-only
+    temperature=0.7,
+    top_p=0.9,
+    repeat_penalty=1.1
+)
+
+# ------------------------------
+# LangChain-compatible wrapper
+# ------------------------------
+def mistral_llm(prompt):
+    output = llm_cpp(
+        prompt,
+        max_tokens=512,  # Reduced for CPU performance
+        stop=["</s>", "[INST]", "[/INST]"]
+    )
+    return output["choices"][0]["text"].strip()
+
+# ------------------------------
+# Prompt Template (unchanged)
+# ------------------------------
+def get_qa_prompt():
+    template = """<s>[INST] \
+You are a helpful, knowledgeable AI assistant. Answer the user's question based on the provided context.
+
+Guidelines:
+- Respond in a natural, conversational tone
+- Be detailed but concise
+- Use paragraphs and bullet points when appropriate
+- If you don't know, say so
+- Maintain a friendly and professional demeanor
+
+Conversation History:
+{chat_history}
+
+Relevant Context:
+{context}
+
+Current Question: {question} 
+
+Provide a helpful response: [/INST]"""
+    return PromptTemplate(
+        template=template,
+        input_variables=["context", "question", "chat_history"]
+    )
+
+# ------------------------------
+# PDF and Chat Logic (optimized for CPU)
+# ------------------------------
+def pdf_text(pdf_docs):
+    text = ""
+    for doc in pdf_docs:
+        reader = PdfReader(doc)
+        for page in reader.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n"
+    return text
+
+def get_chunks(text):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=800,  # Smaller chunks for CPU
+        chunk_overlap=100,
+        length_function=len
+    )
+    chunks = splitter.split_text(text)
+    return [Document(page_content=chunk) for chunk in chunks]
+
+def get_vectorstore(documents):
+    db = FAISS.from_documents(documents, embedding=embeddings)
+    db.save_local("faiss_index")
+
+def format_chat_history(history):
+    return "\n".join([f"User: {q}\nAssistant: {a}" for q, a in history[-2:]])  # Shorter history
+
+def handle_pdf_upload(pdf_files):
+    if not pdf_files:
+        return "⚠️ Upload at least one PDF"
+    try:
+        text = pdf_text(pdf_files)
+        if not text.strip():
+            return "⚠️ Could not extract text"
+        chunks = get_chunks(text)
+        get_vectorstore(chunks)
+        return f"✅ Processed {len(pdf_files)} PDF(s) with {len(chunks)} chunks"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+def user_query(msg, chat_history):
+    if not os.path.exists("faiss_index"):
+        chat_history.append((msg, "Please upload PDF documents first."))
+        return "", chat_history
+
+    try:
+        db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+        retriever = db.as_retriever(search_kwargs={"k": 2})  # Fewer documents for CPU
+        docs = retriever.get_relevant_documents(msg)
+        context = "\n\n".join([d.page_content for d in docs][:2])  # Limit context
+
+        prompt = get_qa_prompt()
+        final_prompt = prompt.format(
+            context=context[:1500],  # Further limit context size
+            question=msg,
+            chat_history=format_chat_history(chat_history)
+        )
+
+        response = mistral_llm(final_prompt)
+        chat_history.append((msg, response))
+        return "", chat_history
+    except Exception as e:
+        error_msg = f"Sorry, I encountered an error: {str(e)}"
+        chat_history.append((msg, error_msg))
+        return "", chat_history
+
+# ------------------------------
+# Gradio Interface (your exact requested format)
+# ------------------------------
+with gr.Blocks(theme=gr.themes.Soft(), title="PDF Chat Assistant") as demo:
+    with gr.Row():
+        gr.Markdown("""
+        # 📚 PDF Chat Assistant
+        ### Have natural conversations with your documents ((Note: This Space runs on CPU, so responses may take a few mins.))
+        """)
+    with gr.Row():
+        with gr.Column(scale=1, min_width=300):
+            gr.Markdown("### Document Upload")
+            pdf_input = gr.File(
+                file_types=[".pdf"],
+                file_count="multiple",
+                label="Upload PDFs",
+                height=100
+            )
+            upload_btn = gr.Button("Process Documents", variant="primary")
+            status_box = gr.Textbox(label="Status", interactive=False)
+            gr.Markdown("""
+            **Instructions:**
+            1. Upload PDF documents
+            2. Click Process Documents
+            3. Start chatting in the right panel
+            """)
+
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(
+                height=600,
+                bubble_full_width=False,
+                avatar_images=(
+                    "user.png", 
+                    "bot.png"
+                )
+            )
+            
+            with gr.Row():
+                message = gr.Textbox(
+                    placeholder="Type your question about the documents...",
+                    show_label=False,
+                    container=False,
+                    scale=7,
+                    autofocus=True
+                )
+                submit_btn = gr.Button("Send", variant="primary", scale=1)
+            
+            with gr.Row():
+                clear_chat = gr.Button("🧹 Clear Conversation")
+                examples = gr.Examples(
+                    examples=[
+                        "Summarize the key points from the documents",
+                        "What are the main findings?",
+                        "Explain this in simpler terms"
+                    ],
+                    inputs=message,
+                    label="Example Questions"
+                )
+
+    upload_btn.click(handle_pdf_upload, inputs=pdf_input, outputs=status_box)
+    submit_btn.click(user_query, inputs=[message, chatbot], outputs=[message, chatbot])
+    message.submit(user_query, inputs=[message, chatbot], outputs=[message, chatbot])
+    clear_chat.click(lambda: [], None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch()  # Disable sharing for local CPU use