feat: finbert integration with uagents

integrations/finbert/README.md

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+# Finbert Integration
+
+FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification.
+
+Ensure you have the following software installed:
+
+- Python (v3.10+ recommended)
+- Poetry (A Python packaging and dependency management tool)
+
+## Setup
+
+1. For the demo to work, you need to get HuggingFaceAPI Token:
+
+    1. Visit [HuggingFace](https://huggingface.co/).
+    2. Sign up or log in.
+    3. Navigate to `Profile -> Settings -> Access Tokens`.
+    4. Copy an existing token or create a new one.
+
+2. In the `finbert/src` directory, create a `.env` file and set your HuggingFaceAPI Token:
+
+    ```
+    export HUGGING_FACE_ACCESS_TOKEN="{Your HuggingFaceAPI Token}"
+    ```
+
+3. To load the environment variables from `.env` and install the dependencies:
+
+    ```bash
+    cd src
+    source .env
+    poetry install
+    ```
+
+## Running The Script
+
+To run the project, use the command:
+
+```
+poetry run python main.py
+```
+
+In the console look for the following output:
+
+```
+Adding agent to Bureau: {agent_address}
+
+```
+
+Copy the {agent_address} address and replace **AI_MODEL_AGENT_ADDRESS** value in agents/finbert_user.py.
+
+
+You can change the input text to what you like, just open the src/finbert_user.py file, and change the value of **INPUT_TEXT** variable in agents/finbert_user.py.
+
+The finbert model will give softmax outputs for three labels: positive, negative or neutral.

integrations/finbert/pyproject.toml

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+[tool.poetry]
+name = "finbert-integration"
+version = "0.0.1"
+description = "showcasing finbert integration with uagents"
+authors = ["Sangram Singh"]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+uagents = "*"
+requests = "^2.31.0"

integrations/finbert/src/agents/finbert_agent.py

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+# Necessary imports: uagents for agent creation and message handling,
+# os and requests for managing API calls
+from uagents import Agent, Context, Protocol
+from messages.basic import UAResponse, UARequest, Error
+from uagents.setup import fund_agent_if_low
+import os
+import requests
+
+# The access token and URL for the FinBERT model, served by Hugging Face
+HUGGING_FACE_ACCESS_TOKEN = os.getenv(
+    "HUGGING_FACE_ACCESS_TOKEN", "HUGGING FACE secret phrase :)")
+FINBERT_URL = "https://api-inference.huggingface.co/models/ProsusAI/finbert"
+
+# Setting the headers for the API call
+HEADERS = {
+    "Authorization": f"Bearer {HUGGING_FACE_ACCESS_TOKEN}"
+}
+
+# Creating the agent and funding it if necessary
+agent = Agent(
+    name="finbert_agent",
+    seed=HUGGING_FACE_ACCESS_TOKEN,
+    port=8001,
+    endpoint=["http://127.0.0.1:8001/submit"],
+)
+fund_agent_if_low(agent.wallet.address())
+
+# Function to get classification results from FinBERT for a given text
+
+
+async def get_classification(ctx: Context, sender: str, text: str):
+    data = {
+        "inputs": text
+    }
+
+    try:
+        # Making POST request to Hugging Face FinBERT API
+        response = requests.post(FINBERT_URL, headers=HEADERS, json=data)
+
+        if response.status_code != 200:
+            # Error handling - send error message back to user if API call unsuccessful
+            await ctx.send(sender, Error(error=f"Error: {response.json().get('error')}"))
+            return
+        # If API call is successful, return the response from the model
+        model_res = response.json()[0]
+        await ctx.send(sender, UAResponse(response=model_res))
+        return
+    except Exception as ex:
+        # Catch and notify any exception occured during API call or data handling
+        await ctx.send(sender, Error(error=f"An exception occurred while processing the request: {ex}"))
+        return
+
+
+# Protocol declaration for UARequests
+finbert_agent = Protocol("UARequest")
+
+# Declaration of a message event handler for handling UARequests and send respective response.
+
+
+@finbert_agent.on_message(model=UARequest, replies={UAResponse, Error})
+async def handle_request(ctx: Context, sender: str, request: UARequest):
+    # Logging the request information
+    ctx.logger.info(
+        f"Got request from  {sender} for text classification : {request.text}")
+
+    # Call text classification function for the incoming request's text
+    await get_classification(ctx, sender, request.text)
+
+# Include protocol to the agent
+agent.include(finbert_agent)
+
+# If the script is run as the main program, run our agents event loop
+if __name__ == "__main__":
+    finbert_agent.run()

integrations/finbert/src/agents/finbert_user.py

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+from uagents import Agent, Context, Protocol  # Import necessary modules
+from messages.basic import UAResponse, UARequest, Error  # Import Basic messages
+# Import the fund_agent_if_low function
+from uagents.setup import fund_agent_if_low
+import os  # Import os for environment variables if any needed
+
+INPUT_TEXT = "Stocks rallied and the British pound gained."
+AI_MODEL_AGENT_ADDRESS = "agent1q2gdm20yg3krr0k5nt8kf794ftm46x2cs7xyrm2gqc9ga5lulz9xsp4l4kk"
+
+user = Agent(
+    name="finbert_user",
+    port=8000,
+    endpoint=["http://127.0.0.1:8000/submit"],
+)
+
+fund_agent_if_low(user.wallet.address())
+
+finbert_user = Protocol("Request")
+
+
+@finbert_user.on_interval(360, messages=UARequest)
+async def text_classification(ctx: Context):
+    ctx.logger.info(f"Asking AI model agent to classify: {INPUT_TEXT}")
+    await ctx.send(AI_MODEL_AGENT_ADDRESS, UARequest(text=INPUT_TEXT))
+
+
+@finbert_user.on_message(model=UAResponse)
+async def handle_data(ctx: Context, sender: str, data: UAResponse):
+    ctx.logger.info(f"Got response from AI model agent: {data.response}")
+
+
+@finbert_user.on_message(model=Error)
+async def handle_error(ctx: Context, sender: str, error: Error):
+    ctx.logger.info(f"Got error from AI model agent: {error}")
+
+user.include(finbert_user)
+
+if __name__ == "__main__":
+    finbert_user.run()

integrations/finbert/src/main.py

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+from uagents import Bureau
+
+from agents.finbert_agent import agent
+from agents.finbert_user import user
+
+
+if __name__ == "__main__":
+    bureau = Bureau(endpoint="http://127.0.0.1:8000/submit", port=8000)
+    print(f"Adding agent to Bureau: {agent.address}")
+    bureau.add(agent)
+    print(f"Adding user agent to Bureau: {user.address}")
+    bureau.add(user)
+    bureau.run()

integrations/finbert/src/messages/basic.py

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+from uagents import Model
+
+
+class UARequest(Model):
+    text: str
+
+
+class Error(Model):
+    error: str
+
+
+class UAResponse(Model):
+    response: list