Create app.py

app.py

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+import gradio as gr
+from langchain.agents import Agent
+from langchain.llms import HuggingFaceHub
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+
+# Initialize the LLM from Hugging Face Hub
+llm = HuggingFaceHub(repo_id="gpt2")
+
+# Initialize embeddings
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Initialize the vector database (FAISS)
+vectorstore = FAISS(embeddings.embed_query, embeddings.embed_documents)
+
+# Define the agents with distinct roles
+class ResearchAgent(Agent):
+    def run(self, query):
+        return llm(query + " Please provide a detailed explanation.")
+
+class SummaryAgent(Agent):
+    def run(self, query):
+        return llm(query + " Summarize the information briefly.")
+
+class QAAgent(Agent):
+    def run(self, query):
+        return llm(query + " Answer the following question: " + query)
+
+# Create instances of the agents
+research_agent = ResearchAgent()
+summary_agent = SummaryAgent()
+qa_agent = QAAgent()
+
+# Function to handle the interaction with the agents
+def agent_interaction(query, agent_type):
+    if agent_type == "Research":
+        return research_agent.run(query)
+    elif agent_type == "Summary":
+        return summary_agent.run(query)
+    elif agent_type == "Q&A":
+        return qa_agent.run(query)
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=agent_interaction,
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter your query here..."),
+        gr.inputs.Radio(["Research", "Summary", "Q&A"], label="Agent Type")
+    ],
+    outputs="text"
+)
+
+if __name__ == "__main__":
+    interface.launch()