Initial commit

app.py

@@ -0,0 +1,240 @@
+import gradio as gr
+from typing import Any
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import ConversationalRetrievalChain
+
+import re
+import uuid 
+import chromadb
+
+
+title = 'DocTalker'
+description = """ PDFs Conversing Naturally. Extract insights, ask questions, get instant context. Transforming documents into dynamic dialogues. Engage intelligently with your content."""
+
+# enable_box = gr.Textbox.update(value = None, placeholder = 'Upload your OpenAI API key',interactive = True)
+disable_box = gr.Textbox.update(value = 'OpenAI API key is Set', interactive = False)
+
+def set_apikey(api_key: str):
+        app.OPENAI_API_KEY = api_key   
+        return disable_box
+
+# def enable_api_box():
+#         return enable_box
+
+def add_text(history, text: str):
+    if not text:
+         raise gr.Error('enter text')
+    history = history + [(text,'')] 
+    return history
+
+class my_app:
+    def __init__(self, OPENAI_API_KEY: str = None) -> None:
+        self.OPENAI_API_KEY: str = OPENAI_API_KEY
+        self.chain = None
+        self.chat_history: list = []
+        self.N: int = 0
+        self.count: int = 0
+        self.model_type: str = ''
+        self.max_token: int = 0
+
+    def __call__(self, file: str) -> Any:
+        if self.count==0:
+            self.chain = self.build_chain(file)
+            self.count+=1
+        else:
+            self.chain = None
+            self.chat_history = []  # Clear chat history
+            self.N = 0
+            self.count = 0
+            self.model_type = ''
+            self.max_token = 0
+            self.chain = self.build_chain(file)
+        return self.chain
+    
+    def chroma_client(self):
+        #create a chroma client
+        client = chromadb.Client()
+        #create a collection
+        collection = client.get_or_create_collection(name="my-collection")
+        return client
+    
+    def process_file(self,file: str):
+        loader = PyPDFLoader(file.name)
+        documents = loader.load()  
+        pattern = r"/([^/]+)$"
+        match = re.search(pattern, file.name)
+        file_name = match.group(1)
+        return documents, file_name
+    
+    def build_chain(self, file: str):
+        if not self.model_type:
+            raise ValueError("Model type must be set before building the chain.")
+        documents, file_name = self.process_file(file)
+        #Load embeddings model
+        embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY) 
+        pdfsearch = Chroma.from_documents(documents, embeddings, collection_name= file_name,)
+        # print(self.model_type)
+        chain = ConversationalRetrievalChain.from_llm(
+                ChatOpenAI(
+                        temperature = 0.0, 
+                        openai_api_key = self.OPENAI_API_KEY, 
+                        model_name = self.model_type,
+                        max_tokens = self.max_token
+                    ), 
+                retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
+                return_source_documents=True,)
+        return chain
+    
+
+def get_response(history, query, file): 
+        print(app.model_type)
+        chain = app.chain
+        result = chain({"question": query, 'chat_history':app.chat_history},return_only_outputs=True)
+        app.chat_history += [(query, result["answer"])]
+        app.N = list(result['source_documents'][0])[1][1]['page']
+        for char in result['answer']:
+           history[-1][-1] += char
+           yield history,''
+
+def drop_value(file, typeM: str):
+    app.model_type = typeM
+    print(typeM)
+
+def slide_value(file, value: int):
+    app.max_token = value  # Assuming you have a max_token attribute in your app class
+    print(value)
+
+def confirm_update(file):
+    # Call app(file) once after updating attributes
+    app(file)
+    print('file loaded')
+    return [gr.update(visible=True),gr.update(visible=False),gr.update(visible=False)]
+     
+def update_textbox(selected_value):
+    return selected_value
+    input_box.value = selected_value
+
+app = my_app()
+with gr.Blocks() as demo:
+    gr.Markdown(f'<center><h1>{title}</h1></center>')
+    gr.Markdown(f'<center>{description}</center>')
+    with gr.Column():
+        with gr.Row():
+            with gr.Column(scale=0.8):
+                api_key = gr.Textbox(placeholder='Enter OpenAI API key and press enter', show_label=False, interactive=True).style(container=False)
+            with gr.Column(scale=0.2):
+                # change_api_key = gr.Button('Get OpenAI API Key', link="https://platform.openai.com/account/api-keys")
+                gr.Markdown(
+                    '<button style="color: black;padding: 7px 20px;text-align: center;text-decoration: none;display: inline-block;font-size: 16px;cursor: pointer;border: 2px solid #e5e7ed;border-radius: 8px;background-color: #e9ebee;"><a style="text-decoration: none;color: black;" href="https://platform.openai.com/account/api-keys">Get OpenAI API key here</a></button>'
+                )
+        with gr.Row():           
+            with gr.Column(min_width=400):
+                pdf_url = gr.Textbox(label='Enter PDF URL here')
+                gr.Markdown("<center><h4>OR<h4></center>")
+                btn = gr.File(
+                    label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf']
+                )
+                # btn = gr.UploadButton("📁 upload a PDF", file_types=[".pdf"]).style()
+                drop = gr.Dropdown(label="Select Model", info="GEN AI model", choices=["gpt-4-32k", "gpt-4", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"])
+                slide = gr.Slider(0, 2048, step=20, label="Max Token", info="Change output Token size according",interactive=True)
+                update_btn = gr.Button('Update App')
+                confirm_btn = gr.Button("Confirm Update", variant="stop", visible=False)
+                cancel_btn = gr.Button("Cancel", visible=False)
+                drop_qus = gr.Dropdown(
+                    label="PRE-SET QUESTIONS", 
+                    info="Click a question to automatically populate the input box, and then hit Enter!", 
+                    choices=[
+                        "Highlighted document equations or formulas?",
+                        "What specific areas does this document cover?",
+                        "Which theories are explored within this document?",
+                        "Are there any notable equations or formulas highlighted in the content?",
+                        "What is the central focus of this document or PDF?",
+                        "Could you offer a concise overview of the main points in this material?",
+                        "What limitations or shortcomings were identified in the study?",
+                        "Can you summarize the main contributions or advancements outlined?",
+                        "What conclusion or final remarks are provided in this paper?"
+                             ]
+                    )
+            with gr.Column(min_width=600):
+                chatbot = gr.Chatbot(value=[], label='Doctalker', elem_id='chatbot').style(height=550)
+                input_box = gr.Textbox(
+                            show_label=False,
+                            container=False,
+                            placeholder="Enter text and press enter",
+                        )#.style(container=False)
+    # with gr.Row():
+    #     with gr.Column(min_width=400):
+    #         dataset_selection = gr.Dataset(
+    #             components=[gr.Textbox(visible=False)],
+    #                 label="PRE-SET QUESTIONS: Click a question to automatically populate the input box, and then hit Enter!",
+    #                 samples=[
+    #                     ["Highlighted document equations or formulas?"],
+    #                     ["What specific areas does this document cover?"],
+    #                     ["Which theories are explored within this document?"],
+    #                     ["Are there any notable equations or formulas highlighted in the content?"],
+    #                     ["What is the central focus of this document or PDF?"],
+    #                     ["Could you offer a concise overview of the main points in this material?"],
+    #                     ["What limitations or shortcomings were identified in the study?"],
+    #                     ["Can you summarize the main contributions or advancements outlined?"],
+    #                     ["What conclusion or final remarks are provided in this paper?"]
+    #                 ],
+    #             )
+        
+        # with gr.Column(min_width=200):
+        #     submit_btn = gr.Button('submit')
+
+    print(api_key)
+    api_key.submit(
+            fn=set_apikey, 
+            inputs=[api_key], 
+            outputs=[api_key,])
+
+    drop.change(
+        fn=drop_value,
+        inputs=[btn, drop],
+        outputs=[])
+
+    slide.change(
+        fn=slide_value,
+        inputs=[btn, slide],
+        outputs=[])
+    
+    update_btn.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)], None, [update_btn, confirm_btn, cancel_btn])
+    cancel_btn.click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)], None, [update_btn, confirm_btn, cancel_btn])
+    
+    confirm_btn.click(
+        fn=confirm_update,
+        inputs=[btn],
+        outputs=[update_btn, confirm_btn, cancel_btn]
+    )
+
+    drop_qus.change(
+        fn=update_textbox,
+        inputs=[drop_qus],
+        outputs=[input_box]
+    )
+
+    input_box.submit(
+        fn=add_text,
+        inputs=[chatbot,input_box],
+        outputs=[chatbot, ], 
+        queue=False).success(
+        fn=get_response,
+        inputs = [chatbot, input_box, btn],
+        outputs = [chatbot,input_box])
+
+    # submit_btn.click(
+    #         fn=add_text, 
+    #         inputs=[chatbot,txt], 
+    #         outputs=[chatbot, ], 
+    #         queue=False).success(
+    #         fn=get_response,
+    #         inputs = [chatbot, txt, btn],
+    #         outputs = [chatbot,txt])
+
+    
+demo.queue()
+demo.launch() 

requirements.txt

@@ -0,0 +1,8 @@
+gradio == 3.27.0
+openai == 0.27.4
+python-dotenv == 1.0.0
+langchain == 0.0.148
+chromadb == 0.4.5 
+tiktoken == 0.3.3
+pypdf == 3.8.1
+pymupdf == 1.22.2