Update app.py

app.py

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+import gradio as gr
+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import LlamaCpp
+from langchain.prompts import ChatPromptTemplate
+from huggingface_hub import hf_hub_download
+
+# --- 1. Model Setup (The "Brain") ---
+# We download a "Quantized" (compressed) version of Llama 3 to run on CPU
+MODEL_REPO = "QuantFactory/Meta-Llama-3-8B-Instruct-GGUF"
+MODEL_FILE = "Meta-Llama-3-8B-Instruct.Q4_K_M.gguf"
+
+print("Downloading model... this may take a minute on first run.")
+model_path = hf_hub_download(
+    repo_id=MODEL_REPO,
+    filename=MODEL_FILE,
+    repo_type="model"
+)
+
+# Initialize the Model
+llm = LlamaCpp(
+    model_path=model_path,
+    n_ctx=4096,       # Context window size
+    temperature=0.7,  # Creativity
+    max_tokens=2000,  # Max length of output
+    n_batch=512,
+    verbose=True
+)
+
+# --- 2. The Core Logic ---
+def generate_question_paper(pdf_file, difficulty, num_questions):
+    if not pdf_file:
+        return "Please upload a PDF file first."
+    
+    try:
+        # A. Load PDF
+        loader = PyPDFLoader(pdf_file.name)
+        pages = loader.load()
+        
+        # B. Split Text
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=100
+        )
+        chunks = text_splitter.split_documents(pages)
+        
+        # C. Vector Store (FAISS)
+        # We use FAISS (Ram-based) instead of Chroma for better Cloud compatibility
+        embeddings = FastEmbedEmbeddings()
+        vector_store = FAISS.from_documents(chunks, embeddings)
+        
+        # D. Retrieve Context
+        # Get the top 7 most relevant chunks
+        retriever = vector_store.as_retriever(search_kwargs={"k": 7})
+        context_docs = retriever.invoke("Key concepts and definitions")
+        context_text = "\n\n".join([doc.page_content for doc in context_docs])
+        
+        # E. Prompt
+        template = """
+        You are an expert academic examiner. Create a formal Question Paper based ONLY on the context provided below.
+        
+        CONTEXT:
+        {context}
+        
+        INSTRUCTIONS:
+        - Difficulty: {difficulty}
+        - Total Questions: {num_questions}
+        - Format:
+          Section A: Multiple Choice Questions (MCQs)
+          Section B: Short Answer Questions
+          Section C: Long Answer/Essay Questions
+        - Provide the Answer Key for MCQs at the very end.
+        
+        Do not output conversational text. Output ONLY the exam paper.
+        """
+        
+        prompt = ChatPromptTemplate.from_template(template)
+        
+        # F. Generate
+        chain = prompt | llm
+        response = chain.invoke({
+            "context": context_text, 
+            "difficulty": difficulty,
+            "num_questions": num_questions
+        })
+        
+        return response
+
+    except Exception as e:
+        return f"Error processing PDF: {str(e)}"
+
+# --- 3. The UI ---
+theme = gr.themes.Soft(primary_hue="blue")
+
+with gr.Blocks(theme=theme, title="AI Question Paper Generator") as demo:
+    gr.Markdown("# 📄 AI Question Paper Generator")
+    gr.Markdown("Hosted on Hugging Face • Powered by Llama 3 (GGUF)")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            pdf_input = gr.File(label="Upload Study Material (PDF)")
+            
+            with gr.Group():
+                difficulty = gr.Radio(
+                    ["Easy", "Medium", "Hard"], 
+                    label="Difficulty", 
+                    value="Medium"
+                )
+                num_questions = gr.Slider(
+                    5, 20, value=10, step=1, 
+                    label="Total Questions"
+                )
+            
+            btn = gr.Button("Generate Question Paper", variant="primary")
+        
+        with gr.Column(scale=2):
+            output = gr.Markdown(label="Generated Paper")
+
+    btn.click(
+        fn=generate_question_paper,
+        inputs=[pdf_input, difficulty, num_questions],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()