added app

app.py

@@ -0,0 +1,70 @@
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import HuggingFaceHub
+from langchain.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import gradio as gr
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def upload_file(files, input_text):
+    file_paths = []
+    target_directory = "/content/uploaded_files"
+    os.makedirs(target_directory, exist_ok=True)
+    try:
+      for file in files:
+          filename = os.path.basename(file.name)
+          file_path = os.path.join(target_directory, filename)
+
+          with open(file_path, "wb") as f:
+              with open(file.name, "rb") as uploaded_file:
+                  f.write(uploaded_file.read())
+
+          file_paths.append(file_path)
+
+      loader = DirectoryLoader(target_directory,
+                              glob='*.pdf',
+                              loader_cls=PyPDFLoader)
+
+      documents = loader.load()
+      text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,
+                                                    chunk_overlap=30)
+      texts = text_splitter.split_documents(documents)
+
+      embeddings = HuggingFaceEmbeddings()
+      db = Chroma.from_documents(texts, embeddings)
+      retriever = db.as_retriever(search_kwargs={'k': 2})
+      repo_id = "mistralai/Mistral-7B-v0.1"
+      llm = HuggingFaceHub(huggingfacehub_api_token=os.getenv("MY_HUGGING_FACE_TOKEN"),
+                      repo_id=repo_id, model_kwargs={"temperature":0.5, "max_new_tokens":50})
+      qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever,return_source_documents=False)
+      chat_history = []
+      answers = []
+      query = input_text
+      result = qa_chain({'question': query, 'chat_history': chat_history})
+      answers.append(result['answer'])
+      chat_history.append((query, result['answer']))
+
+      return "\n".join(answers)
+    finally:
+        # Delete the files from the target directory
+        for file_path in file_paths:
+            os.remove(file_path)
+
+# Define the Gradio interface
+interface = gr.Interface(
+  fn=upload_file,
+  inputs=["files", gr.Textbox(label="Enter Text")],
+  outputs="text",
+  title="File and Text Processing",
+  description="Upload a file and enter some text. Click 'Submit' to process them together."
+)
+
+# Launch the interface
+interface.launch()
+