first CI

Just a little hack

Author: deanchanter

.DS_Store

@@ -150,3 +150,7 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+*.db
+*.py[cod]
+.web
+__pycache__/

.gitignore

@@ -1,2 +1,6 @@
 # hello_reflex
- 
+
+1. pip install -r requirements.txt
+2. Copy your .env file into the base folder
+3. cd app
+4. reflex run

README.md

@@ -0,0 +1,84 @@
+import reflex as rx
+
+from app import style
+from app.state import State
+
+
+
+
+def chat() -> rx.Component:
+    return rx.box(
+        rx.text(State.chat_history, style=style.question_style)
+        #rx.foreach(
+        #    State.chat_history,
+        #    lambda messages: qa(messages[0], messages[1]),
+        #)
+    )
+
+
+def action_bar() -> rx.Component:
+    return rx.hstack(
+        rx.input(
+            value=State.question,
+            placeholder="Put in a review...",
+            on_change=State.set_question,
+            style=style.input_style,
+        ),
+        rx.button(
+            "Ask",
+            on_click=State.answer,
+            style=style.button_style,
+        ),
+    )
+color = "rgb(107,99,246)"
+
+#def upload():
+#    """The main view."""
+#    return rx.vstack(
+#        rx.upload(
+#            rx.vstack(
+#                rx.button(
+#                    "Select File",
+#                    color=color,
+#                    bg="white",
+#                    border=f"1px solid {color}",
+#                ),
+#                rx.text(
+#                    "Drag and drop files here or click to select files"
+#                ),
+#            ),
+#            border=f"1px dotted {color}",
+#            padding="5em",
+#        ),
+#        rx.hstack(rx.foreach(rx.selected_files, rx.text)),
+#        rx.button(
+#            "Upload",
+#            on_click=lambda: State.handle_upload(
+#               rx.upload_files()
+#            ),
+#        ),
+#        rx.button(
+#            "Clear",
+#            on_click=rx.clear_selected_files,
+#        ),
+#        rx.foreach(
+#            State.img, lambda img: rx.image(src=img)
+#        ),
+#        padding="5em",
+#    )
+
+def index() -> rx.Component:
+    return rx.container(
+        action_bar(),
+        chat(),
+    )
+
+
+app = rx.App()
+app.add_page(index)
+app.compile()
+
+
+
+
+

app/app/__init__.py

@@ -0,0 +1,108 @@
+# state.py
+import reflex as rx
+import os
+
+from dotenv import load_dotenv
+
+from langchain.prompts import PromptTemplate, FewShotPromptTemplate
+from langchain.memory import ConversationBufferMemory
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+from ibm_watson_machine_learning.foundation_models.extensions.langchain import WatsonxLLM
+from langchain.chains import LLMChain
+
+
+
+class State(rx.State):
+
+    # The current question being asked.
+    question: str
+
+    # Keep track of the chat history as a list of (question, answer) tuples.
+    chat_history: str
+
+    async def handle_upload(
+        self, files: list[rx.UploadFile]
+    ):
+        """Handle the upload of file(s).
+
+        Args:
+            files: The uploaded files.
+        """
+        for file in files:
+            upload_data = await file.read()
+            outfile = rx.get_asset_path(file.filename)
+
+            # Save the file.
+            with open(outfile, "wb") as file_object:
+                file_object.write(upload_data)
+
+            # Update the img var.
+            self.img.append(file.filename)
+            #print(self.data)
+            
+    #config Watsonx.ai environment
+    load_dotenv()
+    api_key = os.getenv("API_KEY", None)
+    ibm_cloud_url = os.getenv("IBM_CLOUD_URL", None)
+    project_id = os.getenv("PROJECT_ID", None)
+    if api_key is None or ibm_cloud_url is None or project_id is None:
+        raise Exception("Ensure you copied the .env file that you created earlier into the same directory as this notebook")
+    else:
+        creds = {
+            "url": ibm_cloud_url,
+            "apikey": api_key 
+        }
+
+    def send_to_watsonxai(self):
+
+        #assert not any(map(lambda prompt: len(prompt) < 1, prompts)), "make sure none of the prompts in the inputs prompts are empty"
+
+        # Instantiate parameters for text generation
+        model_params = {
+            GenParams.DECODING_METHOD: 'sample',
+            GenParams.MIN_NEW_TOKENS: 3,
+            GenParams.MAX_NEW_TOKENS: 10,
+            GenParams.RANDOM_SEED: 42,
+            GenParams.TEMPERATURE: .1,
+            GenParams.REPETITION_PENALTY: 2.0,
+        }
+
+
+        # Instantiate a model proxy object to send your requests
+        model = Model(
+            model_id="meta-llama/llama-2-70b-chat",
+            params=model_params,
+            credentials=self.creds,
+            project_id=self.project_id)
+
+        llm = WatsonxLLM(model)
+        
+        template = """extract the emotions the reviewer expressed return answer as a comma separated list
+            Example: Review: I had relatives in the nursing home, which was understaffed, they have multiple needs, and they need a lot of care. They are understaffed and they need more than one staff to assist with caring for the residents. My client has sorosis of the liver, her mind is gone, patience is required to take care of your patient. Output: Sadness, Worry
+            Review text: '''{review}'''
+            Output:
+            """
+
+        prompt = PromptTemplate(
+            input_variables=["review"], template=template
+        )
+        #memory = ConversationBufferMemory(memory_key="chat_history")
+        
+        llm_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        verbose=True,
+        #memory=memory,
+        )
+        #print(memory.history)
+        return(llm_chain.predict(review=self.question))
+
+    def answer(self):
+        # Our chatbot is not very smart right now...
+        self.chat_history = ""
+        self.chat_history  = f"Review: {self.question} \n\n\n --------------------Sentiment: {self.send_to_watsonxai()}"
+        
+        # Clear the question input.
+        self.question = ""
+        

app/app/app.py

@@ -0,0 +1,27 @@
+# style.py
+
+# Common styles for questions and answers.
+shadow = "rgba(0, 0, 0, 0.15) 0px 2px 8px"
+chat_margin = "20%"
+message_style = dict(
+    padding="1em",
+    border_radius="5px",
+    margin_y="0.5em",
+    box_shadow=shadow,
+    max_width="30em",
+    display="inline-block",
+)
+
+# Set specific styles for questions and answers.
+question_style = message_style | dict(
+    bg="#F5EFFE", margin_left=chat_margin
+)
+answer_style = message_style | dict(
+    bg="#DEEAFD", margin_right=chat_margin
+)
+
+# Styles for the action bar.
+input_style = dict(
+    border_width="1px", padding="1em", box_shadow=shadow
+)
+button_style = dict(bg="#CEFFEE", box_shadow=shadow)

app/app/state.py

@@ -0,0 +1,5 @@
+import reflex as rx
+
+config = rx.Config(
+    app_name="app",
+)

app/app/style.py

@@ -0,0 +1,3 @@
+reflex
+ibm-watson-machine-learning >= 1.0.327
+langchain