Updated the RAG code

app.py

@@ -1,32 +1,87 @@
 import gradio as gr
+import requests
+from pdfminer.high_level import extract_text
+from langchain_community.vectorstores import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings, ChatHuggingFace
+from langchain_core.runnables import RunnablePassthrough
+from io import BytesIO
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.documents import Document
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.text_splitter import CharacterTextSplitter
 from huggingface_hub import InferenceClient
 
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}] + history
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-    for part in client.chat_completion(
-        messages, max_tokens=max_tokens, stream=True, temperature=temperature,
-        top_p=top_p
-    ):
-        token = part.choices[0].delta.content
-        if token:
-            response += token
-
-    history.append({"role": "user", "content": message})
-    history.append({"role": "assistant", "content": response})
-
-    return history, ""
+def extract_pdf_text(url: str) -> str:
+    response = requests.get(url)
+    pdf_file = BytesIO(response.content)
+    text = extract_text(pdf_file)
+    return text
+
+pdf_url = "https://huggingface.co/spaces/disLodge/Call_model/raw/main/temp.pdf"
+text = extract_pdf_text(pdf_url)
+docs_splits = [Document(page_content=text)]
+
+text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=7500, chunk_overlap=100)
+docs_splits = text_splitter.split_documents(docs_list)
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectorstore = Chroma.from_documents(
+    documents=docs_splits,
+    collection_name="rag-chroma",
+    embedding=embeddings,
+)
+retriever = vectorstore.as_retriever()
+
+llm = ChatHuggingFace(
+    huggingfacehub_api_token=None,
+    model_id="HuggingFaceH4/zephyr-7b-beta",
+    interference_client=client,
+)
+
+# Before RAG chain
+before_rag_template = "What is {topic}"
+before_rag_prompt = ChatPromptTemplate.from_template(before_rag_template)
+before_rag_chain = before_rag_prompt | llm | StrOutputParser()
+
+# After RAG chain
+after_rag_template = """You are a {role}. Summarize the following content for yourself and speak in terms of first person.
+Only include content relevant to that role like a resume summary.
+
+Context:
+{context}
+
+Question: Give a one paragraph summary of the key skills a {role} can have from this document.
+"""
+after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)
+
+def format_query(input_dict):
+    return f"Give a one paragraph summary of the key skills a {input_dict['role']} can have from this document."
+
+after_rag_chain = (
+    {
+        "context": format_query | retriever,
+        "role": lambda x: x["role"],
+    }
+    | after_rag_prompt
+    | llm
+    | StrOutputParser()
+)
+
+def process_query(role, system_message, max_tokens, temperature, top_p):
+    client.max_tokens = max_tokens
+    client.temperature = temperature
+    client.top_p = top_p
+
+    # Before RAG
+    before_rag_result = before_rag_chain.invoke({"topic": "Hugging Face"})
+
+    # After RAG
+    after_rag_result = after_rag_chain.invoke({"role": role})
+
+    return f"**Before RAG**\n{before_rag_result}\n\n**After RAG**\n{after_rag_result}"
+
 
 with gr.Blocks() as demo:
     gr.Markdown("## Zephyr Chatbot Controls")
@@ -38,12 +93,21 @@ with gr.Blocks() as demo:
     temperature = gr.Slider(0.1, 4.0, value=0.7, label="Temperature", step=0.1)
     top_p = gr.Slider(0.1, 1.0, value=0.95, label="Top-p", step=0.05)
 
-    with gr.Row():
-        clear_btn = gr.Button("Clear Chat")
-        dummy_btn = gr.Button("Dummy Action")
+    output = gr.Textbox(label="Output", lines=20)
+
+    submit_btn = gr.Button("Submit")
+    clear_btn = gr.Button("Clear")
+
+    submit_btn.click(
+        fn=process_query,
+        inputs=[role_dropdown, system_message, max_tokens, temperature, top_p]
+        outputs=output
+    )
 
-    clear_btn.click(lambda: gr.Info("Chat cleared!"))
-    dummy_btn.click(lambda: gr.Info("Dummy action clicked!"))
+    clear_btn.click(
+        fn=lambda: ("", gr.Info("Chat cleared!")),
+        outputs=[output]
+    )
 
 if __name__ == "__main__":
     demo.launch()