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README.md

@@ -1,12 +1,12 @@
----
-title: Einstein
-emoji: 💻
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Einstein
+emoji: 💻
+colorFrom: red
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.39.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,95 @@
+import os
+from crewai import Agent, Task, Crew
+from langchain_groq import ChatGroq
+import streamlit as st
+from PIL import Image
+
+# Initialize the LLM for the Einstein Agent
+llm = ChatGroq(
+    groq_api_key="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",
+    model_name="llama3-70b-8192",  # Replace with the actual Einstein model name
+)
+
+# Define the Einstein Agent with a research-oriented goal
+einstein_agent = Agent(
+    role='Einstein Agent',
+    goal='Provide in-depth answers and insights on various topics to help with research questions.',
+    backstory=(
+        "You are an Einstein Agent, skilled in gathering and synthesizing information across domains. Mainly in Physics. "
+        "Your role is to answer questions with a detailed and analytical approach."
+    ),
+    verbose=True,
+    llm=llm,
+)
+
+def process_question_with_agent(question):
+    # Describe the task for the agent
+    task_description = f"Research and provide a detailed answer to the question: '{question}'"
+    
+    # Define the task for the agent to generate a response to the question
+    research_task = Task(
+        description=task_description,
+        agent=einstein_agent,
+        human_input=False,
+        expected_output="According to user need response to the question"  # Placeholder for expected output
+    )
+
+    # Instantiate the crew with the defined agent and task
+    crew = Crew(
+        agents=[einstein_agent],
+        tasks=[research_task],
+        verbose=2,
+    )
+
+    # Get the crew to work on the task and return the result
+    result = crew.kickoff()
+    
+    return result
+
+# Load the image from the specified path
+image_path = r"C:\Users\PMLS\Downloads\image-removebg-preview (1).png"  # Update with your image path
+image = Image.open(image_path)
+
+# Resize the image to 500x500
+image = image.resize((300, 300))
+
+# Set the title of your app with Markdown
+st.markdown("<h1 style='text-align: center;'>Einstein Researcher Chatbot</h1>", unsafe_allow_html=True)
+
+# Convert the image to base64 for embedding in HTML
+import base64
+from io import BytesIO
+
+buffered = BytesIO()
+image.save(buffered, format="PNG")
+img_str = base64.b64encode(buffered.getvalue()).decode()
+
+# Display the image and center it using HTML
+st.markdown(f"<div style='text-align: center;'><img src='data:image/png;base64,{img_str}' width='300' height='300'/></div>", unsafe_allow_html=True)
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# React to user input
+if prompt := st.chat_input("Ask a research question:"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    # Get the response from the Einstein Agent
+    with st.spinner("Processing..."):
+        response = process_question_with_agent(prompt)
+    
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response)
+    
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})

requirements.txt

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+os
+crewai
+langchain_groq
+streamlit
+Pillow