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pages/charts.py

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+import streamlit as st
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+input = st.chat_input(
+    placeholder="Type a message",
+    key="input",
+)
+chart = alt.Chart(source).mark_circle().encode(
+    x='Horsepower',
+    y='Miles_per_Gallon',
+    color='Origin',
+).interactive()
+
+tab1, tab2 = st.tabs(["Streamlit theme (default)", "Altair native theme"])
+
+with tab1:
+    # Use the Streamlit theme.
+    # This is the default. So you can also omit the theme argument.
+    st.altair_chart(chart, theme="streamlit", use_container_width=True)
+with tab2:
+    # Use the native Altair theme.
+    st.altair_chart(chart, theme=None, use_container_width=True)
+
+message = st.chat_message("assistant")
+message.write("Hello human")
+message.altair_chart(chart, theme=None, use_container_width=True)
+
+if input == "show me sales":
+    message = st.chat_message("assistant")
+    message.write("Here are the sales")
+    message.altair_chart(chart, theme="streamlit", use_container_width=True)

pages/search.py

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+import os
+import queue
+import re
+import tempfile
+import threading
+from groq import Groq
+import streamlit as st
+
+from embedchain import App
+from embedchain.config import BaseLlmConfig
+from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
+                                          generate)
+
+client_groq = Groq(api_key="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")
+def embedchain_bot(db_path, api_key):
+    return App.from_config(
+        config={
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-3.5-turbo-1106",
+                    "temperature": 0.5,
+                    "max_tokens": 4096,
+                    "top_p": 1,
+                    "stream": True,
+                    "api_key": api_key,
+                },
+            },
+            "vectordb": {
+                "provider": "chroma",
+                "config": {"collection_name": "chat-pdf", "dir": db_path, "allow_reset": True},
+            },
+            "embedder": {"provider": "openai", "config": {"api_key": api_key}},
+            "chunker": {"chunk_size": 20000, "chunk_overlap": 0, "length_function": "len"},
+        }
+    )
+
+
+def get_db_path():
+    tmpdirname = tempfile.mkdtemp()
+    return tmpdirname
+
+
+def get_ec_app(api_key):
+    if "app" in st.session_state:
+        print("Found app in session state")
+        app = st.session_state.app
+    else:
+        print("Creating app")
+        db_path = get_db_path()
+        app = embedchain_bot(db_path, api_key)
+        st.session_state.app = app
+    return app
+
+
+with st.sidebar:
+    #openai_access_token = st.text_input("OpenAI API Key", key="api_key", type="password")
+    st.session_state.api_key = "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"
+    if st.session_state.api_key:
+        app = get_ec_app(st.session_state.api_key)
+
+    pdf_files = st.file_uploader("Upload your PDF files", accept_multiple_files=True, type="pdf")
+    add_pdf_files = st.session_state.get("add_pdf_files", [])
+    for pdf_file in pdf_files:
+        file_name = pdf_file.name
+        if file_name in add_pdf_files:
+            continue
+        try:
+            if not st.session_state.api_key:
+                st.error("Please enter your OpenAI API Key")
+                st.stop()
+            temp_file_name = None
+            with tempfile.NamedTemporaryFile(mode="wb", delete=False, prefix=file_name, suffix=".pdf") as f:
+                f.write(pdf_file.getvalue())
+                temp_file_name = f.name
+            if temp_file_name:
+                st.markdown(f"Adding {file_name} to knowledge base...")
+                app.add(temp_file_name, data_type="pdf_file")
+                st.markdown("")
+                add_pdf_files.append(file_name)
+                os.remove(temp_file_name)
+            st.session_state.messages.append({"role": "assistant", "content": f"Added {file_name} to knowledge base!"})
+        except Exception as e:
+            st.error(f"Error adding {file_name} to knowledge base: {e}")
+            st.stop()
+    st.session_state["add_pdf_files"] = add_pdf_files
+
+st.title("📄 Embedchain - Chat with PDF")
+styled_caption = '<p style="font-size: 17px; color: #aaa;">🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered by OpenAI!</p>'  # noqa: E501
+st.markdown(styled_caption, unsafe_allow_html=True)
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {
+            "role": "assistant",
+            "content": """
+                Hi! I'm chatbot powered by Embedchain, which can answer questions about your pdf documents.\n
+                Upload your pdf documents here and I'll answer your questions about them! 
+            """,
+        }
+    ]
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Ask me anything!"):
+    if not st.session_state.api_key:
+        st.error("Please enter your OpenAI API Key", icon="🤖")
+        st.stop()
+
+    app = get_ec_app(st.session_state.api_key)
+
+    with st.chat_message("user"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        msg_placeholder = st.empty()
+        msg_placeholder.markdown("Thinking...")
+        full_response = ""
+
+        q = queue.Queue()
+
+        def app_response(result):
+            llm_config = app.llm.config.as_dict()
+            llm_config["callbacks"] = [StreamingStdOutCallbackHandlerYield(q=q)]
+            config = BaseLlmConfig(**llm_config)
+            answer, citations = app.chat(prompt, config=config, citations=True)
+            result["answer"] = answer
+            result["citations"] = citations
+
+        results = {}
+        thread = threading.Thread(target=app_response, args=(results,))
+        thread.start()
+
+        for answer_chunk in generate(q):
+            full_response += answer_chunk
+            msg_placeholder.markdown(full_response)
+
+        thread.join()
+        answer, citations = results["answer"], results["citations"]
+        if citations:
+            full_response += "\n\n**Sources**:\n"
+            sources = []
+            for i, citation in enumerate(citations):
+                source = citation[1]["url"]
+                pattern = re.compile(r"([^/]+)\.[^\.]+\.pdf$")
+                match = pattern.search(source)
+                if match:
+                    source = match.group(1) + ".pdf"
+                sources.append(source)
+            sources = list(set(sources))
+            for source in sources:
+                full_response += f"- {source}\n"
+                      
+        completion = client_groq.chat.completions.create(
+                model="mixtral-8x7b-32768",
+                messages=[
+                    {
+                        "role": "system",
+                        "content" : "You are a helpful assistant helping elaborate on teh given topics and also remove any negative words or phrases taht you receive as your prompt in teh input text",
+                        "role": "user",
+                        "content": "Please expand on teh following " " Text: " + full_response + " " "Topic: " + prompt,
+                    }
+                ],
+                temperature=0.5,
+                max_tokens=1324,
+                top_p=1,
+                stream=False,
+                stop=None,
+            )  
+        full_response = completion.choices[0].message.content
+        msg_placeholder.markdown(full_response)       
+    
+    print("Answer: ", full_response)
+    st.session_state.messages.append({"role": "assistant", "content": full_response})