Create app.py

app.py

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+import gradio as gr
+from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, ServiceContext
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from PyPDF2 import PdfReader
+
+# Load tokenizer and model from HuggingFace (StableLM)
+tokenizer = AutoTokenizer.from_pretrained("StabilityAI/stablelm-tuned-alpha-3b")
+model = AutoModelForCausalLM.from_pretrained("StabilityAI/stablelm-tuned-alpha-3b")
+
+# Create service context for the LLM
+service_context = ServiceContext.from_defaults(
+    llm_predictor=(model, tokenizer),  # Attach the model and tokenizer
+    chunk_size=1024
+)
+
+# Function to load PDF
+def load_pdf(file):
+    reader = PdfReader(file.name)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to create an index and query it
+def chat_with_pdf(pdf, query):
+    # Read the PDF content
+    pdf_text = load_pdf(pdf)
+    
+    # Use llama-index to create a document index
+    documents = [pdf_text]
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
+    
+    # Query the index
+    response = index.query(query)
+    return response.response
+
+# Gradio interface
+def chatbot(pdf, query):
+    if not pdf or not query:
+        return "Please upload a PDF and enter a query."
+    
+    response = chat_with_pdf(pdf, query)
+    return response
+
+# Define Gradio inputs and interface
+pdf_input = gr.inputs.File(label="Upload your PDF")
+query_input = gr.inputs.Textbox(label="Ask a question about the PDF")
+output = gr.outputs.Textbox(label="Chatbot Response")
+
+gr.Interface(fn=chatbot, inputs=[pdf_input, query_input], outputs=output, title="PDF Chatbot").launch()