Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+import os
+from langchain.embeddings import CohereEmbeddings
+from langchain.vectorstores.faiss import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.llms.fireworks import Fireworks 
+from langchain import VectorDBQA
+
+FIREWORKS_API_KEY = os.getenv("FIREWORKS_API_KEY")
+
+def pdf_to_text(pdf_file, query):
+  # Open the PDF file in binary mode
+  with open(pdf_file.name, 'rb') as pdf_file:
+      # Create a PDF reader object
+      pdf_reader = PyPDF2.PdfReader(pdf_file)
+
+      # Create an empty string to store the text
+      text = ""
+
+      # Loop through each page of the PDF
+      for page_num in range(len(pdf_reader.pages)):
+          # Get the page object
+          page = pdf_reader.pages[page_num]
+          # Extract the texst from the page and add it to the text variable
+          text += page.extract_text()
+  #embedding step 
+  llm = Fireworks(model="accounts/fireworks/models/llama-v2-13b-chat", model_kwargs={"temperature": 0, "max_tokens": 500, "top_p": 1.0})  
+  text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+  texts = text_splitter.split_text(text)
+
+  embeddings = CohereEmbeddings(cohere_api_key="Ev0v9wwQPa90xDucdHTyFsllXGVHXouakUMObkNb")
+  #vector store
+  vectorstore = FAISS.from_texts(texts, embeddings)
+
+  #inference
+  qa = VectorDBQA.from_chain_type(llm=llm, chain_type="stuff", vectorstore=vectorstore)
+  return qa.run(query)
+
+# Define the Gradio interface
+pdf_input = gr.inputs.File(label="PDF File")
+query_input = gr.inputs.Textbox(label="Query")
+outputs = gr.outputs.Textbox(label="Chatbot Response")
+interface = gr.Interface(fn=pdf_to_text, inputs=[pdf_input, query_input], outputs=outputs)
+
+# Run the interface
+interface.launch()