Create app.py

app.py

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+import os
+import tempfile
+import gradio as gr
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.retrieval_qa.base import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain_community.vectorstores import Chroma
+from langchain.llms.base import LLM
+from groq import Groq
+from typing import Any, List, Optional
+
+# Set up Groq client
+GROQ_API_KEY = "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"
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+groq_client = Groq(api_key=GROQ_API_KEY)
+
+# Custom LLM class for Groq
+class GroqLLM(LLM):
+    client: Any
+    model: str = "llama3-8b-8192"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        chat_completion = self.client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model=self.model,
+        )
+        return chat_completion.choices[0].message.content
+
+    @property
+    def _llm_type(self) -> str:
+        return "groq"
+
+# Initialize GroqLLM
+llm = GroqLLM(client=groq_client)
+
+# Custom prompt template
+template = """You are a direct and concise assistant. Answer the question using only the information provided in the context. Give only the specific answer requested, with no additional explanation or information.
+
+Context: {context}
+
+Question: {question}
+
+Answer:"""
+
+PROMPT = PromptTemplate(
+    template=template, input_variables=["context", "question"]
+)
+
+class PDFQuestionAnswering:
+    def __init__(self):
+        self.qa_system = None
+
+    def setup_qa_system(self, pdf_file):
+        # Create a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+            temp_file.write(pdf_file)
+            temp_file_path = temp_file.name
+
+        # Load PDF
+        loader = PyPDFLoader(temp_file_path)
+        documents = loader.load()
+
+        # Remove the temporary file
+        os.unlink(temp_file_path)
+
+        # Text splitting
+        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+        texts = text_splitter.split_documents(documents)
+
+        # Embeddings
+        embeddings = HuggingFaceEmbeddings()
+
+        # Vector store
+        docsearch = Chroma.from_documents(texts, embeddings)
+
+        # Set up RetrievalQA
+        self.qa_system = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type="stuff",
+            retriever=docsearch.as_retriever(),
+            chain_type_kwargs={"prompt": PROMPT}
+        )
+
+        return "PDF processed successfully. You can now ask questions."
+
+    def get_answer(self, question):
+        if self.qa_system is None:
+            return "Please upload a PDF file first."
+
+        raw_answer = self.qa_system.run(question)
+        return raw_answer.strip()
+
+pdf_qa = PDFQuestionAnswering()
+
+def process_pdf(pdf_file):
+    if pdf_file is None:
+        return "Please upload a PDF file."
+    return pdf_qa.setup_qa_system(pdf_file)
+
+def answer_question(question):
+    return pdf_qa.get_answer(question)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# GROQ and LLAMA-3 Custom RAG Bot ")
+    with gr.Row():
+        pdf_input = gr.File(label="Upload PDF", type="binary", file_types=[".pdf"])
+        pdf_output = gr.Textbox(label="PDF Processing Status")
+    pdf_button = gr.Button("Process PDF")
+    
+    with gr.Row():
+        question_input = gr.Textbox(label="Enter your question")
+        answer_output = gr.Textbox(label="Answer")
+    question_button = gr.Button("Get Answer")
+
+    pdf_button.click(process_pdf, inputs=[pdf_input], outputs=[pdf_output])
+    question_button.click(answer_question, inputs=[question_input], outputs=[answer_output])
+
+if __name__ == "__main__":
+    demo.launch()