Create app.py

app.py

@@ -0,0 +1,113 @@
+import gradio as gr
+import os
+import pdfplumber
+import tempfile
+from huggingface_hub import InferenceClient
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+
+# Initialize Hugging Face InferenceClient
+client = InferenceClient(
+    provider="novita",
+    api_key=hf_token  #"hf_xxxxxxxxxxxxxxxxxxxxxxxxx"  # Replace with your HF token
+)
+
+# Global vectorstore
+vectorstore = None
+
+# Load and process the uploaded PDF
+def load_pdf(file):
+    global vectorstore
+
+    try:
+        # Save uploaded file to temp path (file is already bytes in Kaggle!)
+        temp_pdf_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name
+        with open(temp_pdf_path, "wb") as f:
+            f.write(file)  # <--- FIXED LINE
+
+        # Extract text using pdfplumber
+        import pdfplumber
+        raw_text = ""
+        with pdfplumber.open(temp_pdf_path) as pdf:
+            for page in pdf.pages:
+                text = page.extract_text()
+                if text:
+                    raw_text += text + "\n"
+
+        if not raw_text.strip():
+            return "❌ No extractable text found in the PDF."
+
+        # Chunk the text
+        splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
+        texts = splitter.split_text(raw_text)
+
+        # Create FAISS vectorstore
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        vectorstore = FAISS.from_texts(texts, embeddings)
+
+        return "✅ PDF successfully processed. You can now ask questions!"
+    
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+
+
+
+
+
+def ask_question(query):
+    global vectorstore
+
+    if vectorstore is None:
+        return "❌ Please upload a PDF first."
+
+    try:
+        docs = vectorstore.similarity_search(query, k=3)
+        context = "\n\n".join([doc.page_content for doc in docs])
+
+        # Prepare chat message format
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that answers questions based on a document."
+            },
+            {
+                "role": "user",
+                "content": f"Answer this question using the context below:\n\nContext:\n{context}\n\nQuestion:\n{query}"
+            }
+        ]
+
+        # Use chat.completions.create
+        completion = client.chat.completions.create(
+            model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
+            messages=messages,
+            max_tokens=500
+        )
+
+        return completion.choices[0].message.content.strip()
+
+    except Exception as e:
+        return f"❌ Failed to generate answer: {str(e)}"
+
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 📄 RAG PDF Chatbot using Hugging Face Inference API")
+
+    with gr.Row():
+        file_input = gr.File(label="Upload PDF", type="binary")
+        upload_btn = gr.Button("Process")
+    
+    status_box = gr.Textbox(label="Status", interactive=False)
+
+    with gr.Row():
+        question = gr.Textbox(label="Ask a Question")
+        ask_btn = gr.Button("Ask")
+    
+    answer = gr.Textbox(label="Answer", lines=6)
+
+    upload_btn.click(load_pdf, inputs=file_input, outputs=status_box)
+    ask_btn.click(ask_question, inputs=question, outputs=answer)
+
+demo.launch()