Update app.py

app.py

@@ -1,63 +1,133 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+# Import necessary modules
+import os
+from langchain_community.utilities.alpha_vantage import AlphaVantageAPIWrapper
+
+# Read API keys from files
+with open('mykey.txt', 'r') as file:
+    openai_key = file.read()
+
+with open('alpha_key.txt', 'r') as file:
+    alpha_key = file.read()
+
+# Set environment variables for API keys
+os.environ['OPENAI_API_KEY'] = openai_key
+os.environ["ALPHAVANTAGE_API_KEY"] = alpha_key  # 25 requests per day in free option
+
+# Create an instance of the AlphaVantageAPIWrapper
+alpha_vantage = AlphaVantageAPIWrapper()
+
+# Get the last 100 days prices for the stock symbol "AAPL"
+alpha_vantage._get_time_series_daily("AAPL")
+
+# Import necessary modules for creating a chatbot
+from langchain.agents import tool
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.agents import AgentExecutor
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.prompts import MessagesPlaceholder
+from langchain.memory import ConversationBufferMemory
+from langchain.memory import ConversationBufferWindowMemory
+
+
+# Import necessary modules for creating additional tools
+import wikipedia
+import datetime
+import requests
+
+@tool
+def search_wikipedia(query: str) -> str:
+    """Run Wikipedia search and get page summaries."""
+    page_titles = wikipedia.search(query)
+    summaries = []
+    for page_title in page_titles[:1]:
+        try:
+            wiki_page =  wikipedia.page(title=page_title, auto_suggest=False)
+            summaries.append(f"Page: {page_title}\nSummary: {wiki_page.summary}")
+        except (
+            self.wiki_client.exceptions.PageError,
+            self.wiki_client.exceptions.DisambiguationError,
+        ):
+            pass
+    if not summaries:
+        return "No good Wikipedia Search Result was found"
+    return "\n\n".join(summaries)
+
+@tool
+def get_current_temperature(latitude: float, longitude: float) -> dict:
+    """Fetch current temperature for given coordinates."""
+    
+    BASE_URL = "https://api.open-meteo.com/v1/forecast"
+    
+    # Parameters for the request
+    params = {
+        'latitude': latitude,
+        'longitude': longitude,
+        'hourly': 'temperature_2m',
+        'forecast_days': 1,
+    }
+
+    # Make the request
+    response = requests.get(BASE_URL, params=params)
+    
+    if response.status_code == 200:
+        results = response.json()
+    else:
+        raise Exception(f"API Request failed with status code: {response.status_code}")
+
+    current_utc_time = datetime.datetime.utcnow()
+    time_list = [datetime.datetime.fromisoformat(time_str.replace('Z', '+00:00')) for time_str in results['hourly']['time']]
+    temperature_list = results['hourly']['temperature_2m']
+    
+    closest_time_index = min(range(len(time_list)), key=lambda i: abs(time_list[i] - current_utc_time))
+    current_temperature = temperature_list[closest_time_index]
+    
+    return f'The current temperature is {current_temperature}°C'
+
+
+# Update the prompt template to include multiple tools
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a helpful assistant"),
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("user", "{input}"),
+    MessagesPlaceholder(variable_name="agent_scratchpad")
+])
+
+# Convert the additional functions to OpenAI functions
+functions = [convert_to_openai_function(f) for f in [get_stock_price, get_current_temperature, search_wikipedia]]
+
+# Create a new model instance with the updated functions
+model = ChatOpenAI(temperature=0, model='gpt-4o').bind(functions=functions)
+
+# Update the agent chain with the new model and functions
+agent_chain = RunnablePassthrough.assign(
+    agent_scratchpad= lambda x: format_to_openai_functions(x["intermediate_steps"])
+) | prompt | model | OpenAIFunctionsAgentOutputParser()
+
+# Update the memory buffer
+memory = ConversationBufferWindowMemory(return_messages=True, memory_key="chat_history", k =5, output_key="output")
+tools = [get_stock_price, search_wikipedia, get_current_temperature]
+
+agent_executor = AgentExecutor(agent=agent_chain, tools=tools, verbose=False, memory=memory, return_intermediate_steps=True)
+
+
+def my_chatbot(prompt: str):
+    reply = agent_executor.invoke({"input": prompt})
+    if len(reply['intermediate_steps'])==0:
+        tool = 'None'
+    else:
+        tool = reply['intermediate_steps'][0][0].tool
+
+    return tool, reply['output']
+
+    
+
+demo = gr.Interface(fn=my_chatbot, 
+                    inputs=[gr.Textbox(label="Query", lines=3)],
+                    outputs=[gr.Textbox(label="Tool", lines = 1), gr.Textbox(label="Tool", lines = 10)],
+                    title="Demo Agent",
+                    description= "Flag responses where inappropriate tool is used")
+demo.launch()