Upload app.py

app.py

@@ -1,121 +1,54 @@
-import gradio as gr
-from datasets import load_dataset, Dataset
-from datetime import datetime
-from datetime import date
-import io
-import os
-from PIL import Image, ImageDraw, ImageFont
-from huggingface_hub import login
-
-login(token=os.environ["HUGGINGFACE_TOKEN"])
-
-# Constants
-SCORES_DATASET = "agents-course/unit4-students-scores"
-CERTIFICATES_DATASET = "agents-course/course-certificates-of-excellence"
-THRESHOLD_SCORE = 30
-
-# Function to check user score
-def check_user_score(username):
-    score_data = load_dataset(SCORES_DATASET, split="train", download_mode="force_redownload")
-    matches = [row for row in score_data if row["username"] == username]
-    return matches[0] if matches else None
-
-# Function to check if certificate entry exists
-def has_certificate_entry(username):
-    cert_data = load_dataset(CERTIFICATES_DATASET, split="train", download_mode="force_redownload")
-    print(username)
-    return any(row["username"] == username for row in cert_data)
-
-# Function to add certificate entry
-def add_certificate_entry(username, name, score):
-    # Load current dataset
-    ds = load_dataset(CERTIFICATES_DATASET, split="train", download_mode="force_redownload")
-    
-    # Remove any existing entry with the same username
-    filtered_rows = [row for row in ds if row["username"] != username]
-
-    # Append the updated/new entry
-    new_entry = {
-        "username": username,
-        "score": score,
-        "timestamp": datetime.now().isoformat()
-    }
-    filtered_rows.append(new_entry)
-
-    # Rebuild dataset and push
-    updated_ds = Dataset.from_list(filtered_rows)
-    updated_ds.push_to_hub(CERTIFICATES_DATASET)
-
-# Function to generate certificate PDF
-def generate_certificate(name, score):
-    """Generate certificate image and PDF."""
-    certificate_path = os.path.join(
-        os.path.dirname(__file__), "templates", "certificate.png"
-    )
-    im = Image.open(certificate_path)
-    d = ImageDraw.Draw(im)
-
-    name_font = ImageFont.truetype("Quattrocento-Regular.ttf", 100)
-    date_font = ImageFont.truetype("Quattrocento-Regular.ttf", 48)
-
-    name = name.title()
-    d.text((1000, 740), name, fill="black", anchor="mm", font=name_font)
-
-    d.text((1480, 1170), str(date.today()), fill="black", anchor="mm", font=date_font)
-
-    pdf = im.convert("RGB")
-    pdf.save("certificate.pdf")
-
-    return im, "certificate.pdf"
-
-# Main function to handle certificate generation
-def handle_certificate(name, profile: gr.OAuthProfile):
-    if profile is None:
-        return "You must be logged in with your Hugging Face account.", None
-
-    username = profile.username
-    user_score = check_user_score(username)
-
-    if not user_score:
-        return "You need to complete Unit 4 first.", None, None
-
-    score = user_score["score"]
-
-    if score < THRESHOLD_SCORE:
-        return f"Your score is {score}. You need at least {THRESHOLD_SCORE} to pass.", None, None
-
-    certificate_image, certificate_pdf = generate_certificate(name, score)
-    add_certificate_entry(username, name, score)
-    return "Congratulations! Here's your certificate:", certificate_image, certificate_pdf
-
-# Gradio interface    
-with gr.Blocks() as demo:
-    gr.Markdown("# 🎓 Agents Course - Get Your Final Certificate")
-    gr.Markdown("Welcome! Follow the steps below to receive your official certificate:")
-    gr.Markdown("⚠️ **Note**: Due to high demand, you might experience occasional bugs. If something doesn't work, please try again after a moment!")
-    
-    with gr.Group():
-        gr.Markdown("## ✅ How it works")
-        gr.Markdown("""
-        1. **Sign in** with your Hugging Face account using the button below.  
-        2. **Enter your full name** (this will appear on the certificate).  
-        3. Click **'Get My Certificate'** to check your score and download your certificate.
-        """)
-    gr.Markdown("---")
-    gr.Markdown("📝 **Note**: You must have completed [Unit 4](https://huggingface.co/learn/agents-course/unit4/introduction) and your Agent must have scored **above 30** to get your certificate.")
-
-    gr.LoginButton()
-    with gr.Row():
-        name_input = gr.Text(label="Enter your name (this will appear on the certificate)")
-    generate_btn = gr.Button("Get my certificate")
-    output_text = gr.Textbox(label="Result")
-    cert_image = gr.Image(label="Your Certificate")
-    cert_file = gr.File(label="Download Certificate (PDF)", file_types=[".pdf"])
-
-    generate_btn.click(
-        fn=handle_certificate,
-        inputs=[name_input],
-        outputs=[output_text, cert_image, cert_file]
-    )
-
-demo.launch()
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper
+from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun, DuckDuckGoSearchRun
+from langchain.agents import initialize_agent, AgentType
+from langchain.callbacks import StreamlitCallbackHandler
+import os
+from dotenv import load_dotenv
+
+# Used the inbuilt tools of Arxiv and Wikipedia
+api_wrapper_arxiv = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=250)
+arxiv = ArxivQueryRun(api_wrapper=api_wrapper_arxiv)
+
+api_wrapper_wiki = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=250)
+wiki = WikipediaQueryRun(api_wrapper=api_wrapper_wiki)
+
+search = DuckDuckGoSearchRun(name="Search")
+
+st.title("Langchain - Chat with Search")
+"""
+In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
+Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
+"""
+
+# Sidebar for settings
+st.sidebar.title("Settings")
+api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password")
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role":"assistant", "content":"Hi, I am a Chatbot who can search the web. How can I help you ?"}
+    ]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+if prompt:=st.chat_input(placeholder="What is machine learning ?"):
+    st.session_state.messages.append({"role":"user", "content":prompt})
+    st.chat_message("user").write(prompt)
+
+    llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
+    tools = [search, arxiv, wiki]
+
+    search_agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
+
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
+        st.session_state.messages.append({'role':'assistant', "content":response})
+        st.write(response)
+    
+
+
+