temp changes to test

README.md

@@ -1,12 +1,57 @@
 ---
-title: Streamlit Panda
+title: Agentic FLow
 emoji: 🐠
 colorFrom: green
 colorTo: red
-sdk: streamlit
-sdk_version: 1.28.2
+sdk: gradio
+sdk_version: 5.38.0
 app_file: app.py
 pinned: false
 ---
 
+# Agentic Assistant
+
+A Gradio-based chat interface powered by LangChain and HuggingFace models.
+
+## Setup
+
+### 1. Install Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Environment Variables
+
+Create a `.env` file in the project root with the following variables:
+
+```bash
+# Required: HuggingFace API Token
+# Get your free token from: https://huggingface.co/settings/tokens
+HUGGINGFACEHUB_API_TOKEN=your_huggingface_token_here
+
+# Optional: SerpAPI Key (for web search functionality)
+# Get your key from: https://serpapi.com/
+SERPAPI_API_KEY=your_serpapi_key_here
+```
+
+### 3. Run the Application
+```bash
+python app.py
+```
+
+## Features
+
+- 🤖 Powered by Google's Flan-T5 model
+- 🔍 Optional web search capability (requires SerpAPI key)
+- 💬 Interactive chat interface with Gradio
+- 🛠️ Built with LangChain for agent capabilities
+
+## Troubleshooting
+
+- **Missing API Token**: Make sure you've set the `HUGGINGFACEHUB_API_TOKEN` environment variable
+- **Import Errors**: The code now uses `langchain_community` imports to avoid deprecation warnings
+- **Web Search**: If you don't have a SerpAPI key, the agent will run without web search functionality
+
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

agent.py

@@ -0,0 +1,29 @@
+from langchain.agents import initialize_agent, Tool
+from langchain.llms import HuggingFaceHub
+from langchain.utilities import SerpAPIWrapper
+import os
+
+# Load LLM from Hugging Face Hub
+llm = HuggingFaceHub(
+    repo_id="google/flan-t5-large",
+    model_kwargs={"temperature": 0.7, "max_length": 512},
+)
+
+# Optional: Use web search tool
+search = SerpAPIWrapper()  # Requires SERPAPI_API_KEY env var
+
+tools = [
+    Tool(
+        name="Search",
+        func=search.run,
+        description="Search engine for real-time information.",
+    )
+]
+
+agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
+
+def run_agent(prompt: str) -> str:
+    try:
+        return agent.run(prompt)
+    except Exception as e:
+        return f"[Agent Error] {str(e)}"

app.py

@@ -1,29 +1,11 @@
-import streamlit as st
-import pandas as pd
- 
-st.write("""
-# Personal GPT
-""")
+import gradio as gr
+from agent import run_agent
 
-with st.sidebar:
-    ping = st.text_input("API Key")
+def respond(message, history):
+    reply = run_agent(message)
+    return reply
 
+chat = gr.ChatInterface(respond, title="🧠 Agentic Assistant")
 
-tab1, tab2 = st.tabs(["New File", "History"])
-# tab1.write("this is tab 1")
-
-with tab1:
-    file = st.file_uploader("Upload a CSV to visualise")
-    # st.button("Click me")
-    if file:
-        df = pd.read_csv(file)
-        st.line_chart(df)
-
-# st.text_input(ping)
-# st.number_input("Pick a number", 0, 10)
-# st.text_area("Text to translate")
-# st.date_input("Your birthday")
-# st.time_input("Meeting time")
-# st.file_uploader("Upload a CSV")
-# st.camera_input("Take a picture")
-# st.color_picker("Pick a color")
+if __name__ == "__main__":
+    chat.launch()

requirements.txt

@@ -1 +1,6 @@
-pandas
+gradio
+langchain>=0.1.0
+langchain-community>=0.0.20
+transformers>=4.30.0
+huggingface-hub>=0.16.0
+python-dotenv>=1.0.0