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# app.py
import streamlit as st
import json
from transformers import pipeline
# --- Functions (Simulated) ---
def get_current_weather(location):
"""Simulates fetching the current weather for a given location."""
weather_data = {
"location": location,
"temperature": "32°C", # Changed for consistency
"conditions": "Sunny", # Changed for consistency
"humidity": "50%" # Changed for consistency
}
return json.dumps(weather_data)
def search_wikipedia(query):
"""Simulates searching Wikipedia for a given query."""
search_results = {
"query": query,
"summary": f"This is a simulated summary for the query: {query}. Real information would be here."
}
return json.dumps(search_results)
available_functions = {
"get_current_weather": get_current_weather,
"search_wikipedia": search_wikipedia
}
# --- Agent Interaction Function ---
model = pipeline("text-generation", model="gpt2") # Keep it outside the function
def run_agent(user_query):
prompt = f"""You are a helpful information-gathering agent.
Your goal is to answer user queries effectively. You have access to the following tools (functions):
{list(available_functions.keys())}.
For certain questions, you might need to use these tools to get the most up-to-date information.
When you decide to use a tool, respond in a JSON format specifying the 'action' (the function name)
and the 'parameters' for that function.
If you can answer the question directly without using a tool, respond in a JSON format with
'action': 'final_answer' and 'answer': 'your direct answer'.
Example 1:
User query: What's the weather like in Karachi?
Agent response: {{"action": "get_current_weather", "parameters": {{"location": "Karachi"}}}}
Example 2:
User query: Tell me about the capital of Pakistan.
Agent response: {{"action": "final_answer", "answer": "The capital of Pakistan is Islamabad."}}
Example 3:
User query: Search for information about the Mughal Empire.
Agent response: {{"action": "search_wikipedia", "parameters": {{"query": "Mughal Empire"}}}}
User query: {user_query}
Agent response: """
output = model(prompt, max_new_tokens=100, num_return_sequences=1, stop_sequence="\n")[0]['generated_text']
return output
# --- Response Processing Function ---
def process_agent_response(agent_response_json):
try:
response_data = json.loads(agent_response_json)
action = response_data.get("action")
if action == "get_current_weather":
parameters = response_data.get("parameters", {})
location = parameters.get("location")
if location:
weather = get_current_weather(location)
return {"final_answer": f"The agent called 'get_current_weather' for '{location}'. Simulated result: {weather}"}
else:
return {"final_answer": "Error: Location not provided for weather lookup."}
elif action == "search_wikipedia":
parameters = response_data.get("parameters", {})
query = parameters.get("query")
if query:
search_result = search_wikipedia(query)
return {"final_answer": f"The agent called 'search_wikipedia' for '{query}'. Simulated result: {search_result}"}
else:
return {"final_answer": "Error: Query not provided for Wikipedia search."}
elif action == "final_answer":
return {"final_answer": response_data.get("answer", "No direct answer provided.")}
else:
return {"final_answer": f"Unknown action: {action}"}
except json.JSONDecodeError:
return {"final_answer": "Error decoding agent's response."}
# --- Streamlit App ---
def main():
st.title("Gen AI Information Gathering Agent")
st.write("Ask me a question, and I'll try to answer it using my simulated tools!")
user_query = st.text_input("Your question:", "What's the weather like in London?")
if user_query:
agent_response = run_agent(user_query)
st.write(f"Agent Response (JSON):")
st.json(agent_response) # Use st.json for better display
processed_response = process_agent_response(agent_response)
st.write("Processed Response:")
st.write(processed_response["final_answer"])
if __name__ == "__main__":
main()
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