Create app.py

app.py

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+import os
+import gradio as gr
+from langchain_core.prompts import PromptTemplate
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+from langchain.chains.question_answering import load_qa_chain
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from PIL import Image
+import io
+from threading import Thread
+from transformers import TextIteratorStreamer
+
+# Configure Gemini API
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Load OpenELM model
+checkpoint = "apple/OpenELM-270M"
+checkpoint_tok = "meta-llama/Llama-2-7b-hf"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_tok)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+low_cpu_mem_usage = True if torch.cuda.is_available() else False
+
+model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch_dtype, trust_remote_code=True, low_cpu_mem_usage=low_cpu_mem_usage)
+model.to(device)
+
+# Adjust tokenizer settings
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+# Define other settings
+max_new_tokens = 250
+repetition_penalty = 1.4
+rtl = False
+
+# Function to process PDF using Gemini API
+def process_pdf(file_path, question):
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    prompt_template = """Answer the question as precise as possible using the provided context. If the answer is not contained in the context, say "answer not available in context" \n\n Context: \n {context}?\n Question: \n {question} \n Answer: """
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    
+    pdf_loader = PyPDFLoader(file_path)
+    pages = pdf_loader.load_and_split()
+    context = "\n".join(str(page.page_content) for page in pages[:200])
+    stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
+    return stuff_answer['output_text']
+
+# Function to process images using Gemini API
+def process_image(image, question):
+    model = genai.GenerativeModel('gemini-pro-vision')
+    response = model.generate_content([image, question])
+    return response.text
+
+# Function to generate follow-up using OpenELM model
+def generate_openelm_followup(answer):
+    prompt = f"Based on this answer: {answer}\nGenerate a follow-up question:"
+    inputs = tokenizer([prompt], return_tensors='pt').input_ids.to(model.device)
+
+    # Streaming output using TextIteratorStreamer
+    decode_kwargs = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    streamer = TextIteratorStreamer(tokenizer, timeout=5., decode_kwargs=decode_kwargs)
+
+    generation_kwargs = dict(input_ids=inputs, streamer=streamer, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty)
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    followup = ""
+    for new_text in streamer:
+        if new_text:
+            followup += new_text.replace(tokenizer.pad_token, "").replace(tokenizer.bos_token, "")
+    
+    return followup
+
+# Function to process input and generate output
+def process_input(file, image, question):
+    try:
+        if file is not None:
+            gemini_answer = process_pdf(file.name, question)
+        elif image is not None:
+            gemini_answer = process_image(image, question)
+        else:
+            return "Please upload a PDF file or an image."
+
+        openelm_followup = generate_openelm_followup(gemini_answer)
+        combined_output = f"Gemini Answer: {gemini_answer}\n\nOpenELM Follow-up: {openelm_followup}"
+        return combined_output
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Define Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Multi-modal RAG Knowledge Retrieval using Gemini API and OpenELM Model")
+    
+    with gr.Row():
+        with gr.Column():
+            input_file = gr.File(label="Upload PDF File")
+            input_image = gr.Image(type="pil", label="Upload Image")
+        input_question = gr.Textbox(label="Ask about the document or image")
+    
+    output_text = gr.Textbox(label="Answer - Combined Gemini and OpenELM")
+    
+    submit_button = gr.Button("Submit")
+    submit_button.click(fn=process_input, inputs=[input_file, input_image, input_question], outputs=output_text)
+
+demo.launch()