Upload app.py

app.py

@@ -74,15 +74,8 @@
 
 # copy_files(source_folder, destination_folder)
 
-import os
-import openai
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-os.environ["OPENAI_API_KEY"] = "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"
-openai.api_key = "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"
 
 def api_key(key):
-
     import os
     import openai
 
@@ -92,6 +85,7 @@ def api_key(key):
 
     return "Successful!"
 
+
 def save_file(input_file):
     import shutil
     import os
@@ -99,13 +93,14 @@ def save_file(input_file):
     destination_dir = "/home/user/app/file/"
     os.makedirs(destination_dir, exist_ok=True)
 
-    output_dir="/home/user/app/file/"
+    output_dir = "/home/user/app/file/"
 
     for file in input_file:
-      shutil.copy(file.name, output_dir)
+        shutil.copy(file.name, output_dir)
 
     return "File(s) saved successfully!"
 
+
 def process_file():
     from langchain.document_loaders import PyPDFLoader
     from langchain.document_loaders import DirectoryLoader
@@ -116,23 +111,27 @@ def process_file():
     from langchain.text_splitter import CharacterTextSplitter
     import openai
 
-    loader1 = DirectoryLoader('/home/user/app/file/', glob="./*.pdf", loader_cls=PyPDFLoader)
+    loader1 = DirectoryLoader(
+        "/home/user/app/file/", glob="./*.pdf", loader_cls=PyPDFLoader
+    )
     document1 = loader1.load()
 
-    loader2 = DirectoryLoader('/home/user/app/file/', glob="./*.txt", loader_cls=TextLoader)
+    loader2 = DirectoryLoader(
+        "/home/user/app/file/", glob="./*.txt", loader_cls=TextLoader
+    )
     document2 = loader2.load()
 
-    loader3 = DirectoryLoader('/home/user/app/file/', glob="./*.docx", loader_cls=Docx2txtLoader)
+    loader3 = DirectoryLoader(
+        "/home/user/app/file/", glob="./*.docx", loader_cls=Docx2txtLoader
+    )
     document3 = loader3.load()
 
     document1.extend(document2)
     document1.extend(document3)
 
     text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len)
+        separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
+    )
 
     docs = text_splitter.split_documents(document1)
     embeddings = OpenAIEmbeddings()
@@ -142,15 +141,16 @@ def process_file():
 
     return "File(s) processed successfully!"
 
+
 def formatted_response(docs, response):
     formatted_output = response + "\n\nSources"
 
     for i, doc in enumerate(docs):
-        source_info = doc.metadata.get('source', 'Unknown source')
-        page_info = doc.metadata.get('page', None)
+        source_info = doc.metadata.get("source", "Unknown source")
+        page_info = doc.metadata.get("page", None)
 
         # Get the file name without the directory path
-        file_name = source_info.split('/')[-1].strip()
+        file_name = source_info.split("/")[-1].strip()
 
         if page_info is not None:
             formatted_output += f"\n{file_name}\tpage no {page_info}"
@@ -159,6 +159,7 @@ def formatted_response(docs, response):
 
     return formatted_output
 
+
 def search_file(question):
     from langchain.embeddings.openai import OpenAIEmbeddings
     from langchain.vectorstores import FAISS
@@ -167,11 +168,12 @@ def search_file(question):
     from langchain.llms import OpenAI
     import openai
     from langchain.chat_models import ChatOpenAI
+
     embeddings = OpenAIEmbeddings()
     file_db = FAISS.load_local("/home/user/app/file_db/", embeddings)
     docs = file_db.similarity_search(question)
 
-    llm = ChatOpenAI(model_name='gpt-3.5-turbo')
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
     chain = load_qa_chain(llm, chain_type="stuff")
     with get_openai_callback() as cb:
         response = chain.run(input_documents=docs, question=question)
@@ -179,6 +181,7 @@ def search_file(question):
 
     return formatted_response(docs, response)
 
+
 def search_local(question):
     from langchain.embeddings.openai import OpenAIEmbeddings
     from langchain.vectorstores import FAISS
@@ -187,13 +190,14 @@ def search_local(question):
     from langchain.llms import OpenAI
     import openai
     from langchain.chat_models import ChatOpenAI
+
     embeddings = OpenAIEmbeddings()
     file_db = FAISS.load_local("/home/user/app/local_db/", embeddings)
     docs = file_db.similarity_search(question)
 
     print(docs)
     type(docs)
-    llm = ChatOpenAI(model_name='gpt-3.5-turbo')
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
     chain = load_qa_chain(llm, chain_type="stuff")
     with get_openai_callback() as cb:
         response = chain.run(input_documents=docs, question=question)
@@ -201,8 +205,8 @@ def search_local(question):
 
     return formatted_response(docs, response)
 
-def delete_file():
 
+def delete_file():
     import shutil
 
     path1 = "/home/user/app/file/"
@@ -216,38 +220,44 @@ def delete_file():
     except:
         return "Already Deleted"
 
+
 import os
 import gradio as gr
 
+
 def list_files():
-    directory = '/home/user/app/docs'
+    directory = "/home/user/app/docs"
     file_list = []
     for root, dirs, files in os.walk(directory):
         for file in files:
             file_list.append(file)
     return gr.Dropdown.update(choices=file_list)
 
+
 file_list = list_files()
 
 print("List of file names in the directory:")
 for file_name in file_list:
     print(file_name)
 
+
 def soap_report(doc_name, question):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
     import openai
     import docx
 
-    docx_path = '/home/user/app/docs/'+doc_name
+    docx_path = "/home/user/app/docs/" + doc_name
 
     doc = docx.Document(docx_path)
-    extracted_text = 'Extracted text:\n\n\n'
+    extracted_text = "Extracted text:\n\n\n"
 
     for paragraph in doc.paragraphs:
-        extracted_text += paragraph.text + '\n'
+        extracted_text += paragraph.text + "\n"
 
-    question = "\n\nUse the 'Extracted text' to answer the following question:\n" + question
+    question = (
+        "\n\nUse the 'Extracted text' to answer the following question:\n" + question
+    )
     extracted_text += question
 
     if extracted_text:
@@ -266,6 +276,7 @@ def soap_report(doc_name, question):
 
     return response
 
+
 def search_gpt(question):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
@@ -281,6 +292,7 @@ def search_gpt(question):
 
     return response
 
+
 def local_gpt(question):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
@@ -296,11 +308,14 @@ def local_gpt(question):
 
     return response
 
+
 global output
 global response
 
+
 def audio_text(filepath):
     import openai
+
     global output
 
     audio = open(filepath, "rb")
@@ -309,12 +324,16 @@ def audio_text(filepath):
 
     return output
 
+
 def transcript(text):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+
     global response
 
-    question = "Use the following context given below to generate a detailed SOAP Report:\n\n"
+    question = (
+        "Use the following context given below to generate a detailed SOAP Report:\n\n"
+    )
     question += text
     print(question)
 
@@ -329,14 +348,18 @@ def transcript(text):
 
     return response
 
+
 def text_soap():
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+
     global output
     global response
     output = output
 
-    question = "Use the following context given below to generate a detailed SOAP Report:\n\n"
+    question = (
+        "Use the following context given below to generate a detailed SOAP Report:\n\n"
+    )
     question += output
     print(question)
 
@@ -351,12 +374,15 @@ def text_soap():
 
     return response
 
+
 global path
 
+
 def docx(name):
     global response
     response = response
     import docx
+
     global path
     path = f"/home/user/app/docs/{name}.docx"
 
@@ -366,6 +392,7 @@ def docx(name):
 
     return "Successfully saved .docx File"
 
+
 import gradio as gr
 
 css = """
@@ -383,76 +410,73 @@ with gr.Blocks(css=css) as demo:
     gr.Markdown("File Chatting App")
 
     with gr.Tab("Chat with Files"):
-      with gr.Column(elem_classes="col"):
+        with gr.Column(elem_classes="col"):
+            with gr.Tab("Upload and Process Files"):
+                with gr.Column():
+                    api_key_input = gr.Textbox(label="Enter API Key here")
+                    api_key_button = gr.Button("Submit")
+                    api_key_output = gr.Textbox(label="Output")
 
-        with gr.Tab("Upload and Process Files"):
-          with gr.Column():
+                    file_input = gr.Files(label="Upload File(s) here")
+                    upload_button = gr.Button("Upload")
+                    file_output = gr.Textbox(label="Output")
 
-            api_key_input = gr.Textbox(label="Enter PI Key here")
-            api_key_button = gr.Button("Submit")
-            api_key_output = gr.Textbox(label="Output")
+                    process_button = gr.Button("Process")
+                    process_output = gr.Textbox(label="Output")
 
-            file_input = gr.Files(label="Upload File(s) here")
-            upload_button = gr.Button("Upload")
-            file_output = gr.Textbox(label="Output")
+            with gr.Tab("Ask Questions to Files"):
+                with gr.Column():
+                    search_input = gr.Textbox(label="Enter Question here")
+                    search_button = gr.Button("Search")
+                    search_output = gr.Textbox(label="Output")
 
-            process_button = gr.Button("Process")
-            process_output = gr.Textbox(label="Output")
+                    search_gpt_button = gr.Button("Ask ChatGPT")
+                    search_gpt_output = gr.Textbox(label="Output")
 
-        with gr.Tab("Ask Questions to Files"):
-          with gr.Column():
-
-            search_input = gr.Textbox(label="Enter Question here")
-            search_button = gr.Button("Search")
-            search_output = gr.Textbox(label="Output")
-
-            search_gpt_button = gr.Button("Ask ChatGPT")
-            search_gpt_output = gr.Textbox(label="Output")
-
-            delete_button = gr.Button("Delete")
-            delete_output = gr.Textbox(label="Output")
+                    delete_button = gr.Button("Delete")
+                    delete_output = gr.Textbox(label="Output")
 
     with gr.Tab("Chat with Local Files"):
-      with gr.Column(elem_classes="col"):
+        with gr.Column(elem_classes="col"):
+            local_search_input = gr.Textbox(label="Enter Question here")
+            local_search_button = gr.Button("Search")
+            local_search_output = gr.Textbox(label="Output")
 
-        local_search_input = gr.Textbox(label="Enter Question here")
-        local_search_button = gr.Button("Search")
-        local_search_output = gr.Textbox(label="Output")
-
-        local_gpt_button = gr.Button("Ask ChatGPT")
-        local_gpt_output = gr.Textbox(label="Output")
+            local_gpt_button = gr.Button("Ask ChatGPT")
+            local_gpt_output = gr.Textbox(label="Output")
 
     with gr.Tab("Ask Question to SOAP Report"):
-      with gr.Column(elem_classes="col"):
-
-        refresh_button = gr.Button("Refresh")
-        soap_input = gr.Dropdown(label="Choose File")
-        soap_question = gr.Textbox(label="Enter Question here")
-        soap_button = gr.Button("Submit")
-        soap_output = gr.Textbox(label="Output")
+        with gr.Column(elem_classes="col"):
+            refresh_button = gr.Button("Refresh")
+            soap_input = gr.Dropdown(label="Choose File")
+            soap_question = gr.Textbox(label="Enter Question here")
+            soap_button = gr.Button("Submit")
+            soap_output = gr.Textbox(label="Output")
 
     with gr.Tab("Convert Audio to SOAP Report"):
-      with gr.Column(elem_classes="col"):
-
-        mic_text_input = gr.Audio(source="microphone", type="filepath", label="Speak to the Microphone")
-        mic_text_button = gr.Button("Generate Transcript")
-        mic_text_output = gr.Textbox(label="Output")
-
-        upload_text_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File here")
-        upload_text_button = gr.Button("Generate Transcript")
-        upload_text_output = gr.Textbox(label="Output")
-
-        transcript_input = gr.Textbox(label="Enter Transcript here")
-        transcript_button = gr.Button("Generate SOAP Report")
-        transcript_output = gr.Textbox(label="Output")
-
-        text_soap_button = gr.Button("Generate SOAP Report")
-        text_soap_output = gr.Textbox(label="Output")
-
-        docx_input = gr.Textbox(label="Enter the name of .docx File")
-        docx_button = gr.Button("Save .docx File")
-        docx_output = gr.Textbox(label="Output")
-
+        with gr.Column(elem_classes="col"):
+            mic_text_input = gr.Audio(
+                source="microphone", type="filepath", label="Speak to the Microphone"
+            )
+            mic_text_button = gr.Button("Generate Transcript")
+            mic_text_output = gr.Textbox(label="Output")
+
+            upload_text_input = gr.Audio(
+                source="upload", type="filepath", label="Upload Audio File here"
+            )
+            upload_text_button = gr.Button("Generate Transcript")
+            upload_text_output = gr.Textbox(label="Output")
+
+            transcript_input = gr.Textbox(label="Enter Transcript here")
+            transcript_button = gr.Button("Generate SOAP Report")
+            transcript_output = gr.Textbox(label="Output")
+
+            text_soap_button = gr.Button("Generate SOAP Report")
+            text_soap_output = gr.Textbox(label="Output")
+
+            docx_input = gr.Textbox(label="Enter the name of .docx File")
+            docx_button = gr.Button("Save .docx File")
+            docx_output = gr.Textbox(label="Output")
 
     api_key_button.click(api_key, inputs=api_key_input, outputs=api_key_output)
 
@@ -465,30 +489,29 @@ with gr.Blocks(css=css) as demo:
 
     delete_button.click(delete_file, inputs=None, outputs=delete_output)
 
-    local_search_button.click(search_local, inputs=local_search_input, outputs=local_search_output)
-    local_gpt_button.click(local_gpt, inputs=local_search_input, outputs=local_gpt_output)
+    local_search_button.click(
+        search_local, inputs=local_search_input, outputs=local_search_output
+    )
+    local_gpt_button.click(
+        local_gpt, inputs=local_search_input, outputs=local_gpt_output
+    )
 
     refresh_button.click(list_files, inputs=None, outputs=soap_input)
-    soap_button.click(soap_report, inputs=[soap_input, soap_question], outputs=soap_output)
+    soap_button.click(
+        soap_report, inputs=[soap_input, soap_question], outputs=soap_output
+    )
 
     mic_text_button.click(audio_text, inputs=mic_text_input, outputs=mic_text_output)
-    upload_text_button.click(audio_text, inputs=upload_text_input, outputs=upload_text_output)
+    upload_text_button.click(
+        audio_text, inputs=upload_text_input, outputs=upload_text_output
+    )
 
-    transcript_button.click(transcript, inputs=transcript_input, outputs=transcript_output)
+    transcript_button.click(
+        transcript, inputs=transcript_input, outputs=transcript_output
+    )
     text_soap_button.click(text_soap, inputs=None, outputs=text_soap_output)
     docx_button.click(docx, inputs=docx_input, outputs=docx_output)
 
 
 demo.queue()
-demo.launch(debug=True, share=True)
-
-
-# Commented out IPython magic to ensure Python compatibility.
-#download file_db
-
-# %cd /home/user/app/
-
-!zip -r "file_db.zip" "file_db"
-
-from IPython.display import FileLink
-FileLink("file_db.zip")
+demo.launch()