docker changes

Dockerfile

@@ -1,16 +1,33 @@
 # Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
 # you will also find guides on how best to write your Dockerfile
 
-FROM python:3.9
+FROM python:3.11-slim
+
+RUN apt-get update && apt-get install -y curl && \
+    curl -fsSL https://ollama.ai/install.sh | sh && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
 
 RUN useradd -m -u 1000 user
 USER user
-ENV PATH="/home/user/.local/bin:$PATH"
+ENV HOME=/home/user \
+    PATH="/home/user/.local/bin:$PATH"
 
-WORKDIR /app
+# Création des répertoires nécessaires
+RUN mkdir -p $HOME/docker_ollama $HOME/logs
+WORKDIR $HOME/docker_ollama
 
-COPY --chown=user ./requirements.txt requirements.txt
+COPY --chown=user requirements.txt .
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
+COPY --chown=user . .
+RUN chmod +x start.sh
+EXPOSE 7860 11434
+
+CMD ["./start.sh"]
+
+# WORKDIR /app
+
+# COPY --chown=user ./requirements.txt requirements.txt
+# RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-COPY --chown=user . /app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+# COPY --chown=user . /app
+# CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,125 +1,136 @@
 from fastapi import FastAPI
 import uvicorn
-
-app = FastAPI()
-
-@app.post("/write_message")
-def write_message(data: dict):
-    print(data)
-    return None
-
-# if __name__ == "__main__":
-#     uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")
-
-# GOOGLESHEETS_CREDENTIALS = os.getenv("GOOGLESHEETS_CREDENTIALS")
-
-# from langchain_core.prompts import ChatPromptTemplate
-# from langchain_groq import ChatGroq
-# from pydantic import BaseModel, Field
-# import pygsheets
-# import json
-# from langgraph.graph import StateGraph, END
-# from typing import TypedDict, Annotated
-# import operator
-# from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage
-# from langchain_ollama import ChatOllama
-# import groq
-# from tqdm import tqdm
-# from opik.integrations.langchain import OpikTracer
-# from langgraph.pregel import RetryPolicy
-
-# MODEL = "llama3-70b-8192" # "meta-llama/llama-4-scout-17b-16e-instruct" #
-
-# class TransactionParser(BaseModel):
-#     """This Pydantic class is used to parse the transaction message."""
-
-#     amount: str = Field(description="The amount of the transaction in decimal format. If the transaction is a credit or a reversal, then include negative sign. DO not insert currency.", example="123.45")
-#     dr_or_cr: str = Field(description="Identify if the transaction was debit (spent) or credit (received). Strictly choose one of the values - Debit or Credit")
-#     receiver: str = Field(description="The recipient of the transaction. Identify the Merchant Name from the value.")
-#     category: str = Field(description="The category of the transaction. The category of the transaction is linked to the Merchant Name. Strictly choose from one the of values - Shopping,EMI,Education,Miscellaneous,Grocery,Utility,House Help,Travel,Transport")
-#     transaction_date: str = Field(description="The date of the transaction in yyyy-mm-dd format. If the year is not provided then use current year.")
-#     transaction_origin: str = Field(description="The origin of the transaction. Provide the card or account number as well.")
-
-# class AgentState(TypedDict):
-#     messages: Annotated[list[AnyMessage], operator.add]
-
-# class Agent:
-#     def __init__(self, model, system=""):
-#         self.system = system
-#         graph = StateGraph(AgentState)
-#         graph.add_node("classify_txn_type", self.classify_txn_type, retry=RetryPolicy(retry_on=[groq.BadRequestError], max_attempts=2))
-#         graph.add_node("parse_message", self.parse_message, retry=RetryPolicy(retry_on=[groq.BadRequestError], max_attempts=2))
-#         graph.add_node("write_message", self.write_message)
-#         graph.add_conditional_edges(
-#             "classify_txn_type",
-#             self.check_txn_and_decide,
-#             {True: "parse_message", False: END}
-#             )
-#         graph.add_edge("parse_message", "write_message")
-#         graph.add_edge("write_message", END)
-#         graph.set_entry_point("classify_txn_type")
-#         self.graph = graph.compile()
-#         self.model = model
-
-#     def classify_txn_type(self, state: AgentState) -> AgentState:
-#         messages = state["messages"]
-#         if self.system:
-#             messages = [SystemMessage(content=self.system)] + messages
-
-#         message = self.model.invoke(messages)
-#         return {"messages": [message]}
+from langchain_core.prompts import ChatPromptTemplate
+from pydantic import BaseModel, Field
+import pygsheets
+import json
+from langgraph.graph import StateGraph, END
+from typing import TypedDict, Annotated
+import operator
+from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage
+from langchain_ollama import ChatOllama
+from langgraph.pregel import RetryPolicy
+import json
+import pandas as pd
+from google.oauth2 import service_account
+import os
+
+# GOOGLESHEETS_CREDENTIALS = json.dumps({"type": "service_account","project_id": "spendstracker-457904","private_key_id": "91bb3866226ab9f6734aa3fcc861faf17897d79d","private_key": "-----BEGIN PRIVATE KEY-----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\nvcRikUgNG7O8ipxnN28L8dg=\n-----END PRIVATE KEY-----\n","client_email": "spendstracker@spendstracker-457904.iam.gserviceaccount.com","client_id": "117285083911847446748","auth_uri": "https://accounts.google.com/o/oauth2/auth","token_uri": "https://oauth2.googleapis.com/token","auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs","client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/spendstracker%40spendstracker-457904.iam.gserviceaccount.com","universe_domain": "googleapis.com"})
+sheet_url = "https://docs.google.com/spreadsheets/d/1t4bOM4fULdaVsjDDnqEG1g8Zey6M00UuFhTZC03_4xo/edit?gid=0#gid=0"
+GOOGLESHEETS_CREDENTIALS = os.getenv("GOOGLESHEETS_CREDENTIALS")
+
+class TransactionParser(BaseModel):
+    """This Pydantic class is used to parse the transaction message."""
+
+    amount: str = Field(description="The amount of the transaction in decimal format. If the transaction is a credit or a reversal, then include negative sign. DO not insert currency.", example="123.45")
+    dr_or_cr: str = Field(description="Identify if the transaction was debit (spent) or credit (received). Strictly choose one of the values - Debit or Credit")
+    receiver: str = Field(description="The recipient of the transaction. Identify the Merchant Name from the value.")
+    category: str = Field(description="The category of the transaction. The category of the transaction is linked to the Merchant Name. Strictly choose from one the of values - Shopping,EMI,Education,Miscellaneous,Grocery,Utility,House Help,Travel,Transport")
+    transaction_date: str = Field(description="The date of the transaction in yyyy-mm-dd format. If the year is not provided then use current year.")
+    transaction_origin: str = Field(description="The origin of the transaction. Provide the card or account number as well.")
+
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], operator.add]
+
+class Agent:
+    def __init__(self, model, system=""):
+        self.system = system
+        graph = StateGraph(AgentState)
+        graph.add_node("classify_txn_type", self.classify_txn_type)
+        graph.add_node("parse_message", self.parse_message)
+        graph.add_node("write_message", self.write_message)
+        graph.add_conditional_edges(
+            "classify_txn_type",
+            self.check_txn_and_decide,
+            {True: "parse_message", False: END}
+            )
+        graph.add_edge("parse_message", "write_message")
+        graph.add_edge("write_message", END)
+        graph.set_entry_point("classify_txn_type")
+        self.graph = graph.compile()
+        self.model = model
+
+    def classify_txn_type(self, state: AgentState) -> AgentState:
+        print("Classifying transaction type...")
+        messages = state["messages"]
+        if self.system:
+            messages = [SystemMessage(content=self.system)] + messages
+
+        message = self.model.invoke(messages)
+        print("Classifying transaction type completed.")
+        return {"messages": [message]}
     
-#     def parse_message(self, state: AgentState) -> AgentState:
-#         message = state["messages"][0].content
-#         system = """
-#         You are a helpful assistant skilled at parsing transaction messages and providing structured responses.
-#         """
-#         human = "Categorize the transaction message and provide the output in a structed format: {topic}"
-
-#         prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
-#         chain = prompt | model.with_structured_output(TransactionParser)
-#         result = chain.invoke({"topic": message})
+    def parse_message(self, state: AgentState) -> AgentState:
+        print("Parsing transaction message...")
+        message = state["messages"][0]#.content
+        system = """
+        You are a helpful assistant skilled at parsing transaction messages and providing structured responses.
+        """
+        human = "Categorize the transaction message and provide the output in a structed format: {topic}"
+
+        prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
+        chain = prompt | self.model.with_structured_output(TransactionParser)
+        result = chain.invoke({"topic": message})
+        print("Parsing transaction message completed.")        
+        return {"messages": [result]}
+
+    def write_message(self, state: AgentState) -> AgentState:
+        print("Writing transaction message to Google Sheets...")
+        result = state["messages"][-1]
+        SCOPES = ('https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive')
+        service_account_info = json.loads(GOOGLESHEETS_CREDENTIALS)
+        my_credentials = service_account.Credentials.from_service_account_info(service_account_info, scopes=SCOPES)
+        client = pygsheets.authorize(custom_credentials=my_credentials)
+        # client = pygsheets.authorize(service_account_json=GOOGLESHEETS_CREDENTIALS)
+        # client = pygsheets.authorize(service_account_file="serviceaccount.json")
+        worksheet = client.open_by_url(sheet_url)
+        wk = worksheet[0]
+        # Get number of rows in the worksheet
+        df = wk.get_as_df(start='A1', end='G999')
+        nrows = df.shape[0]
+        wk.update_value(f'A{nrows+2}', result.amount)
+        wk.update_value(f'B{nrows+2}', result.dr_or_cr)
+        wk.update_value(f'C{nrows+2}', result.receiver)
+        wk.update_value(f'D{nrows+2}', result.category)
+        wk.update_value(f'E{nrows+2}', result.transaction_date)
+        wk.update_value(f'F{nrows+2}', result.transaction_origin)
+        wk.update_value(f'G{nrows+2}', state["messages"][0])
+        print("Writing transaction message to Google Sheets completed.")
+        return {"messages": ["Transaction Completed"]}
         
-#         return {"messages": [result]}
+    def check_txn_and_decide(self, state: AgentState):
+        try:
+            result = json.loads(state['messages'][-1].content)['classification']
+        except json.JSONDecodeError:
+            result = state['messages'][-1].content.strip()
 
-#     def write_message(self, state: AgentState) -> AgentState:
-#         result = state["messages"][-1]
-#         client = pygsheets.authorize(service_account_file="serviceaccount.json")
-#         worksheet = client.open_by_url("https://docs.google.com/spreadsheets/d/1t4bOM4fULdaVsjDDnqEG1g8Zey6M00UuFhTZC03_4xo")
-#         wk = worksheet[0]
-#         # Get number of rows in the worksheet
-#         df = wk.get_as_df(start='A1', end='G999')
-#         nrows = df.shape[0]
-#         wk.update_value(f'A{nrows+2}', result.amount)
-#         wk.update_value(f'B{nrows+2}', result.dr_or_cr)
-#         wk.update_value(f'C{nrows+2}', result.receiver)
-#         wk.update_value(f'D{nrows+2}', result.category)
-#         wk.update_value(f'E{nrows+2}', result.transaction_date)
-#         wk.update_value(f'F{nrows+2}', result.transaction_origin)
-#         wk.update_value(f'G{nrows+2}', state["messages"][0].content)
-#         return {"messages": ["Transaction Completed"]}
-        
-#     def check_txn_and_decide(self, state: AgentState):
-#         try:
-#             result = json.loads(state['messages'][-1].content)['classification']
-#         except json.JSONDecodeError:
-#             result = state['messages'][-1].content.strip()
-
-#         return result == "Transaction"
+        return result == "Transaction"
     
-# prompt = """You are a smart assistant adept at classifying different messages. \
-# You will be penalized heavily for incorrect classification. \
-# Your task is to classify the message into one of the following categories: \
-# Transaction, OTP, Promotional, Scheduled. \
-# Output the classification in a structured format like below. \
-# {"classification": "OTP"} \
-# """
-
-# model = ChatGroq(temperature=1, groq_api_key=GROQ_API_KEY, model_name=MODEL)
-# # model = ChatOllama(model="gemma3:4b", temperature=1, callback=OpikTracer())
+prompt = """You are a smart assistant adept at classifying different messages. \
+You will be penalized heavily for incorrect classification. \
+Your task is to classify the message into one of the following categories: \
+Transaction, OTP, Promotional, Scheduled. \
+Output the classification in a structured format like below. \
+{"classification": "OTP"} \
+"""
+app = FastAPI()
 
-# transaction_bot = Agent(model, system=prompt)
+@app.get("/")
+def greetings():
+    return {"message": "Hello, this is a transaction bot. Please send a POST request to /write_message with the transaction data."}
 
-# for message in tqdm(messages):
-#     message = [HumanMessage(content=message)]
-#     result = transaction_bot.graph.invoke({"messages": message}, config={"callbacks" : [OpikTracer(graph=transaction_bot.graph.get_graph(xray=True))]})
+@app.post("/write_message")
+def write_message(data: dict):
+    message = data['message']
+    model = ChatOllama(model="gemma3:4b", temperature=1)
+    transaction_bot = Agent(model, system=prompt)
+    result = transaction_bot.graph.invoke({"messages": [message]})
+    return {"message": "Transaction completed successfully"}
+
+@app.get("/ask")
+def ask(prompt: str):
+    model = ChatOllama(model="gemma3:4b", temperature=1)
+    return model.invoke(prompt)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860, log_level="info")

requirements.txt

@@ -1,2 +1,5 @@
 fastapi
-uvicorn[standard]
+uvicorn[standard]
+langchain-ollama
+langgraph
+pygsheets

start.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+#MisterAI/Docker_Ollama
+#start.sh_01
+#https://huggingface.co/spaces/MisterAI/Docker_Ollama/
+
+
+# Set environment variables for optimization HFSpace Free 2CPU - 16GbRAM
+export OMP_NUM_THREADS=2
+export MKL_NUM_THREADS=2
+export CUDA_VISIBLE_DEVICES=-1
+
+# Start Ollama in the background
+ollama serve &
+
+# Pull the model if not already present
+echo "Mistral 7b will be download"
+if ! ollama list | grep -q "gemma3:4b"; then
+    ollama pull gemma3:4b
+fi
+
+# Wait for Ollama to start up
+max_attempts=30
+attempt=0
+while ! curl -s http://localhost:11434/api/tags >/dev/null; do
+    sleep 1
+    attempt=$((attempt + 1))
+    if [ $attempt -eq $max_attempts ]; then
+        echo "Ollama failed to start within 30 seconds. Exiting."
+        exit 1
+    fi
+done
+
+echo "Ollama is Ready - Mistral 7b is Loaded"
+
+# Démarrer Application
+python app.py

test.py

@@ -0,0 +1,16 @@
+import json
+from google.oauth2 import service_account
+import pygsheets
+
+GOOGLESHEETS_CREDENTIALS = json.dumps({"type": "service_account","project_id": "spendstracker-457904","private_key_id": "91bb3866226ab9f6734aa3fcc861faf17897d79d","private_key": "-----BEGIN PRIVATE KEY-----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\nvcRikUgNG7O8ipxnN28L8dg=\n-----END PRIVATE KEY-----\n","client_email": "spendstracker@spendstracker-457904.iam.gserviceaccount.com","client_id": "117285083911847446748","auth_uri": "https://accounts.google.com/o/oauth2/auth","token_uri": "https://oauth2.googleapis.com/token","auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs","client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/spendstracker%40spendstracker-457904.iam.gserviceaccount.com","universe_domain": "googleapis.com"})
+sheet_url = "https://docs.google.com/spreadsheets/d/1t4bOM4fULdaVsjDDnqEG1g8Zey6M00UuFhTZC03_4xo/edit?gid=0#gid=0"
+
+SCOPES = ('https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive')
+service_account_info = json.loads(GOOGLESHEETS_CREDENTIALS)
+my_credentials = service_account.Credentials.from_service_account_info(service_account_info, scopes=SCOPES)
+client = pygsheets.authorize(custom_credentials=my_credentials)
+
+print("Writing transaction message to Google Sheets...")
+worksheet = client.open_by_url(sheet_url)
+
+print(worksheet)