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gradio_test.py

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+import os
+import chromadb
+import gradio as gr
+from dotenv import load_dotenv
+from llama_index.core import VectorStoreIndex
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import QueryEngineTool
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.gemini import Gemini
+from llama_index.core.workflow import Context
+from llama_index.vector_stores.chroma import ChromaVectorStore
+
+load_dotenv()
+
+agent = None
+conversation_context = None
+
+async def initialize_agent():
+    """Initialize the agent once"""
+    global agent, conversation_context
+    if agent is not None:
+        return agent, conversation_context
+
+    llm = Gemini(
+        model="models/gemini-flash-latest",
+        api_key=os.getenv("GEMINI_API"),
+        temperature=0.3,
+    )
+
+    embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+    db = chromadb.PersistentClient(path="./product_db")
+    chroma_collection = db.get_collection(name="product_catalog")
+    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+
+    index = VectorStoreIndex.from_vector_store(
+        vector_store=vector_store,
+        embed_model=embed_model
+    )
+
+    query_engine = index.as_query_engine(llm=llm)
+
+    query_tool = QueryEngineTool.from_defaults(
+        query_engine=query_engine,
+        name="ProductInfoTool",
+        description="A tool to retrieve information about camping products, including their stock availability.",
+    )
+
+    agent = ReActAgent(
+        llm=llm,
+        tools=[query_tool],
+        verbose=False,
+        system_prompt="""You are a friendly and knowledgeable camping gear expert.
+Your goal is to find the perfect product for the user and tell them about it in a helpful, conversational way.
+Use the `ProductInfoTool` to find the best match for the user's query.
+In your final response to the user, you MUST include the following three pieces of information:
+1. The full product name.
+2. A brief, one-sentence reason why it's a good choice for them.
+3. The exact stock status (e.g., '15 available' or 'out of stock').
+If the tool cannot find a suitable product, just say: 'I'm sorry, I couldn't find a product that matches your request.'
+Remember conversation context and refer back to previous messages when appropriate."""
+    )
+
+    conversation_context = Context(agent)
+
+    return agent, conversation_context
+
+
+async def chat_with_agent(message, history):
+    """Handle chat messages with the agent"""
+    global conversation_context, agent
+
+    try:
+        agent, ctx = await initialize_agent()
+        response = await agent.run(message, ctx=conversation_context)
+        return str(response.response)
+    except Exception as e:
+        if "index out of range" in str(e):
+            conversation_context = Context(agent)
+            response = await agent.run(message, ctx=conversation_context)
+            return str(response.response)
+        return f"Sorry, I encountered an error: {str(e)}"
+
+
+def main():
+    """Launch the simple Gradio interface"""
+    demo = gr.ChatInterface(
+        fn=chat_with_agent,
+        title="🏕️ Tory - The Camping Gear Assistant",
+        description="Ask me about camping products and I'll help you find the perfect gear!",
+        examples=[
+            "I need a lightweight tent for 2 people",
+            "What sleeping bags do you have?",
+            "Show me available camping stoves"
+        ],
+    )
+    demo.launch()
+
+if __name__ == "__main__":
+    main()