Update app.py

app.py

@@ -1,22 +1,158 @@
-from huggingface_hub import InferenceClient
+# from huggingface_hub import InferenceClient
+# import gradio as gr
+
+# client = InferenceClient(
+#     "mistralai/Mixtral-8x7B-Instruct-v0.1"
+# )
+
+
+# def format_prompt(message, history):
+#   prompt = "<s>"
+#   for user_prompt, bot_response in history:
+#     prompt += f"[INST] {user_prompt} [/INST]"
+#     prompt += f" {bot_response}</s> "
+#   prompt += f"[INST] {message} [/INST]"
+#   return prompt
+
+# def generate(
+#     prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+# ):
+#     temperature = float(temperature)
+#     if temperature < 1e-2:
+#         temperature = 1e-2
+#     top_p = float(top_p)
+
+#     generate_kwargs = dict(
+#         temperature=temperature,
+#         max_new_tokens=max_new_tokens,
+#         top_p=top_p,
+#         repetition_penalty=repetition_penalty,
+#         do_sample=True,
+#         seed=42,
+#     )
+
+#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+#     output = ""
+
+#     for response in stream:
+#         output += response.token.text
+#         yield output
+#     return output
+
+
+# additional_inputs=[
+#     gr.Textbox(
+#         label="System Prompt",
+#         max_lines=1,
+#         interactive=True,
+#     ),
+#     gr.Slider(
+#         label="Temperature",
+#         value=0.9,
+#         minimum=0.0,
+#         maximum=1.0,
+#         step=0.05,
+#         interactive=True,
+#         info="Higher values produce more diverse outputs",
+#     ),
+#     gr.Slider(
+#         label="Max new tokens",
+#         value=256,
+#         minimum=0,
+#         maximum=1048,
+#         step=64,
+#         interactive=True,
+#         info="The maximum numbers of new tokens",
+#     ),
+#     gr.Slider(
+#         label="Top-p (nucleus sampling)",
+#         value=0.90,
+#         minimum=0.0,
+#         maximum=1,
+#         step=0.05,
+#         interactive=True,
+#         info="Higher values sample more low-probability tokens",
+#     ),
+#     gr.Slider(
+#         label="Repetition penalty",
+#         value=1.2,
+#         minimum=1.0,
+#         maximum=2.0,
+#         step=0.05,
+#         interactive=True,
+#         info="Penalize repeated tokens",
+#     )
+# ]
+
+# examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
+#           ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
+#           ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
+#           ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
+#           ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
+#           ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
+#          ]
+
+# gr.ChatInterface(
+#     fn=generate,
+#     chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+#     additional_inputs=additional_inputs,
+#     title="Mixtral 46.7B",
+#     examples=examples,
+#     concurrency_limit=20,
+# ).launch(show_api= True)
+
+
+import os
 import gradio as gr
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from huggingface_hub import InferenceClient
+
+# Set the Hugging Face Hub API token
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']
 
-client = InferenceClient(
-    "mistralai/Mixtral-8x7B-Instruct-v0.1"
-)
+# Initialize the InferenceClient
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_text_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vectorstore(text_chunks):
+    model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+    encode_kwargs = {"normalize_embeddings": True}
+    embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
+    )
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
 
 def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
-
-def generate(
-    prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
-):
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
     temperature = float(temperature)
     if temperature < 1e-2:
         temperature = 1e-2
@@ -40,64 +176,79 @@ def generate(
         yield output
     return output
 
+def main(pdf_docs):
+    # get pdf text
+    raw_text = get_pdf_text(pdf_docs)
 
-additional_inputs=[
-    gr.Textbox(
-        label="System Prompt",
-        max_lines=1,
-        interactive=True,
-    ),
-    gr.Slider(
-        label="Temperature",
-        value=0.9,
-        minimum=0.0,
-        maximum=1.0,
-        step=0.05,
-        interactive=True,
-        info="Higher values produce more diverse outputs",
-    ),
-    gr.Slider(
-        label="Max new tokens",
-        value=256,
-        minimum=0,
-        maximum=1048,
-        step=64,
-        interactive=True,
-        info="The maximum numbers of new tokens",
-    ),
-    gr.Slider(
-        label="Top-p (nucleus sampling)",
-        value=0.90,
-        minimum=0.0,
-        maximum=1,
-        step=0.05,
-        interactive=True,
-        info="Higher values sample more low-probability tokens",
-    ),
-    gr.Slider(
-        label="Repetition penalty",
-        value=1.2,
-        minimum=1.0,
-        maximum=2.0,
-        step=0.05,
-        interactive=True,
-        info="Penalize repeated tokens",
-    )
-]
-
-examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
-          ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
-          ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
-          ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
-          ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
-          ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
-         ]
-
-gr.ChatInterface(
-    fn=generate,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    additional_inputs=additional_inputs,
-    title="Mixtral 46.7B",
-    examples=examples,
-    concurrency_limit=20,
-).launch(show_api= True)
+    # get the text chunks
+    text_chunks = get_text_chunks(raw_text)
+
+    # create vector store
+    vectorstore = get_vectorstore(text_chunks)
+
+    # create conversation chain
+    conversation_chain = get_conversation_chain(vectorstore)
+
+    additional_inputs=[
+        gr.Textbox(
+            label="System Prompt",
+            max_lines=1,
+            interactive=True,
+        ),
+        gr.Slider(
+            label="Temperature",
+            value=0.9,
+            minimum=0.0,
+            maximum=1.0,
+            step=0.05,
+            interactive=True,
+            info="Higher values produce more diverse outputs",
+        ),
+        gr.Slider(
+            label="Max new tokens",
+            value=256,
+            minimum=0,
+            maximum=1048,
+            step=64,
+            interactive=True,
+            info="The maximum numbers of new tokens",
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            value=0.90,
+            minimum=0.0,
+            maximum=1,
+            step=0.05,
+            interactive=True,
+            info="Higher values sample more low-probability tokens",
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            value=1.2,
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            interactive=True,
+            info="Penalize repeated tokens",
+        )
+    ]
+
+    examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
+              ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
+              ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
+              ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
+              ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
+              ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
+             ]
+
+    gr.ChatInterface(
+        fn=generate,
+        chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+        additional_inputs=additional_inputs,
+        title="Mixtral 46.7B",
+        examples=examples,
+        concurrency_limit=20,
+    ).launch(show_api= True)
+
+if __name__ == "__main__":
+    main([])