T5 Base Model Integration (fetchai#162)

integrations/t5-base/README.md

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+# T5-BASE Model Integration
+
+## **About T5-BASE**
+
+The T5 model is a Text-to-Text Transfer Transformer model that was developed by Google Research. It's a large-scale transformer-based language model that's designed to handle a wide range of NLP tasks, including translation, summarization, and question answering.
+
+## Requirements
+
+- 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. **Install Dependencies**
+
+    ```bash
+    poetry install
+    ```
+
+3.  **Running The Agent Script**
+
+    open terminal goto "t5-base/src", run below command to load environment variables and run the agent.
+
+    ```bash
+    export HUGGING_FACE_ACCESS_TOKEN="{Your HuggingFaceAPI Token}"
+    poetry run python agent.py
+    ```
+
+    Check the log for "adding t5-base agent to bureau" line and copy the {agent address}.
+
+4.  **Running The User Script**
+
+    open terminal and goto "t5-base/src",run below command to load environment variables and run the client.
+
+    ```bash
+    export T5_BASE_AGENT_ADDRESS="{ agent address from last step }"
+    poetry run python client.py
+    ```
+
+After running the command, a request is sent to the agent every 30 sec till its successful.
+
+Modify **INPUT_TEXT** in **t5_base_user.py** to translate different sentence.

integrations/t5-base/pyproject.toml

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+[tool.poetry]
+name = "t5-base-uagent-integration"
+version = "0.0.1"
+description = "t5 base integration with fetch.ai uagent"
+authors = ["Prajna"]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+uagents = "*"
+requests = "^2.31.0"

integrations/t5-base/src/agent.py

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+from uagents import Bureau
+
+from agents.t5_base_agent import agent
+
+
+if __name__ == "__main__":
+    bureau = Bureau(endpoint="http://127.0.0.1:8000/submit", port=8000)
+    print(f"adding t5-base agent to bureau: {agent.address}")
+    bureau.add(agent)
+    bureau.run()

integrations/t5-base/src/agents/t5_base_agent.py

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+from uagents import Agent, Context, Protocol
+from messages.t5_base import TranslationRequest, TranslationResponse, Error
+from uagents.setup import fund_agent_if_low
+import os
+import requests
+import base64
+
+# Get the HUGGING_FACE_ACCESS_TOKEN from environment variable or default to a placeholder string if not found.
+HUGGING_FACE_ACCESS_TOKEN = os.getenv(
+    "HUGGING_FACE_ACCESS_TOKEN", "HUGGING_FACE_ACCESS_TOKEN")
+
+if HUGGING_FACE_ACCESS_TOKEN == "HUGGING_FACE_ACCESS_TOKEN":
+    raise Exception(
+        "You need to provide an HUGGING_FACE_ACCESS_TOKEN, by exporting env, follow README")
+
+T5_BASE_URL = "https://api-inference.huggingface.co/models/t5-base"
+
+# Define headers for HTTP request, including content type and authorization details
+HEADERS = {
+    "Authorization": f"Bearer {HUGGING_FACE_ACCESS_TOKEN}"
+}
+
+# Create an agent with predefined properties
+agent = Agent(
+    name="t5_base_agent",
+    seed=HUGGING_FACE_ACCESS_TOKEN,
+    port=8000,
+    endpoint=["http://127.0.0.1:8000/submit"],
+)
+
+# Ensure the agent has enough funds
+fund_agent_if_low(agent.wallet.address())
+
+
+async def translate_text(ctx: Context, sender: str, input_text: str):
+    # Prepare the data
+    payload = {
+        "inputs": input_text
+    }
+
+    # Make the POST request and handle possible errors
+    try:
+        response = requests.post(T5_BASE_URL, headers=HEADERS, json=payload)
+        if response.status_code == 200:
+            await ctx.send(sender, TranslationResponse(translated_text=f"{response.json()}"))
+            return
+        else:
+            await ctx.send(sender, Error(error=f"Error: {response.json()}"))
+            return
+    except Exception as ex:
+        await ctx.send(sender, Error(error=f"Exception Occurred: {ex}"))
+        return
+
+# Create an instance of Protocol with a label "T5BaseModelAgent"
+t5_base_agent = Protocol(name="T5BaseModelAgent", version="0.0.1")
+
+
+@t5_base_agent.on_message(model=TranslationRequest, replies={TranslationResponse, Error})
+async def handle_request(ctx: Context, sender: str, request: TranslationRequest):
+    # Log the request details
+    ctx.logger.info(f"Got request from  {sender}")
+
+    await translate_text(ctx, sender, request.text)
+
+
+# publish_manifest will make the protocol details available on agentverse.
+agent.include(t5_base_agent, publish_manifest=True)
+
+# Define the main entry point of the application
+if __name__ == "__main__":
+    t5_base_agent.run()

integrations/t5-base/src/agents/t5_base_user.py

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+from uagents import Agent, Context, Protocol
+from messages.t5_base import TranslationRequest, TranslationResponse, Error
+from uagents.setup import fund_agent_if_low
+import base64
+import os
+
+# text you want to translate
+INPUT_TEXT = "Who are you?"
+
+T5_BASE_AGENT_ADDRESS = os.getenv(
+    "T5_BASE_AGENT_ADDRESS", "T5_BASE_AGENT_ADDRESS")
+
+if T5_BASE_AGENT_ADDRESS == "T5_BASE_AGENT_ADDRESS":
+    raise Exception(
+        "You need to provide an T5_BASE_AGENT_ADDRESS, by exporting env, check README file")
+
+# Define user agent with specified parameters
+user = Agent(
+    name="t5_base_user",
+    port=8001,
+    endpoint=["http://127.0.0.1:8001/submit"],
+)
+
+# Check and top up the agent's fund if low
+fund_agent_if_low(user.wallet.address())
+
+
+@user.on_event("startup")
+async def initialize_storage(ctx: Context):
+    ctx.storage.set("TranslationDone", False)
+
+
+# Create an instance of Protocol with a label "T5BaseModelUser"
+t5_base_user = Protocol(name="T5BaseModelUser", version="0.0.1")
+
+
+@t5_base_user.on_interval(period=30, messages=TranslationRequest)
+async def transcript(ctx: Context):
+    TranslationDone = ctx.storage.get("TranslationDone")
+
+    if not TranslationDone:
+        await ctx.send(T5_BASE_AGENT_ADDRESS, TranslationRequest(text=INPUT_TEXT))
+
+
+@t5_base_user.on_message(model=TranslationResponse)
+async def handle_data(ctx: Context, sender: str, response: TranslationResponse):
+    ctx.logger.info(f"Translated text:  {response.translated_text}")
+    ctx.storage.set("TranslationDone", True)
+
+
+@t5_base_user.on_message(model=Error)
+async def handle_error(ctx: Context, sender: str, error: Error):
+    ctx.logger.info(f"Got error from uagent: {error}")
+
+# publish_manifest will make the protocol details available on agentverse.
+user.include(t5_base_user, publish_manifest=True)
+
+# Initiate the task
+if __name__ == "__main__":
+    t5_base_user.run()

integrations/t5-base/src/client.py

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+from uagents import Bureau
+from agents.t5_base_user import user
+
+if __name__ == "__main__":
+    bureau = Bureau(endpoint="http://127.0.0.1:8001/submit", port=8001)
+    print(f"adding t5-base user agent to bureau: {user.address}")
+    bureau.add(user)
+    bureau.run()

integrations/t5-base/src/messages/t5_base.py

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+from uagents import Model
+
+
+class TranslationRequest(Model):
+    text: str
+
+
+class TranslationResponse(Model):
+    translated_text: str
+
+
+class Error(Model):
+    error: str