Update app.py

app.py

@@ -1,44 +1,59 @@
 import streamlit as st
-
-#from langchain_openai import OpenAI
-#from langchain.llms import HuggingFaceEndpoint
 from langchain_community.llms import HuggingFaceEndpoint
 
 #When deployed on huggingface spaces, this values has to be passed using Variables & Secrets setting, as shown in the video :)
 #import os
 #os.environ["OPENAI_API_KEY"] = "sk-PLfFwPq6y24234234234FJ1Uc234234L8hVowXdt"
 
-#Function to return the response
-def load_answer(question):
-    # "text-davinci-003" model is depreciated, so using the latest one https://platform.openai.com/docs/deprecations
-    #llm = OpenAI(model_name="gpt-3.5-turbo-instruct",temperature=0)
-    llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.9)
-    #Last week langchain has recommended to use invoke function for the below please :)
-    answer=llm.invoke(question)
-    return answer
-
-
-#App UI starts here
-st.set_page_config(page_title="Sentiment Analysis", page_icon=":robot:")
-st.header("Sentiment Analysis")
-
-#Gets the user input
-def get_text():
-    input_text = st.text_input("You:", "Pls Write Your Something.......")
-    if input_text.isalpha():
-        st.write(text, 'string', )
-    else:
-        st.write('Please type in a string Only')
-    return input_text
-
-user_input=get_text()
-response = load_answer(user_input)
-
-submit = st.button('Generate')  
-
-#If generate button is clicked
-if submit:
-
-    st.subheader("Answer:")
-
-    st.write(response)
+#from api import Api
+#import streamlit as st 
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+#external class for api integrations, 
+#api = Api()
+
+#llm default OpenAPI
+#llm = api.llm
+llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2", Temperature=0.9)
+
+#streamlit view components
+with st.form("my_form"):
+    st.title('Sentiment Analysis')
+    text_review = st.text_area('Write me a review') 
+
+    option = st.selectbox(
+    'Select the language to evaluate:',
+    ('Italian', 'Spanish', 'English'))
+    submitted = st.form_submit_button("Submit")
+    if submitted:
+        
+        #1 prompt template
+        template = """
+        Please act as a machine learning model trained for perform a supervised learning task, 
+        for extract the sentiment of a review in '{option}' Language.
+
+        Give your answer writing a Json evaluating the sentiment field between the dollar sign, the value must be printed without dollar sign.
+        The value of sentiment must be "positive"  or "negative", otherwise if the text is not valuable write "null".
+
+        Example:
+
+        field 1 named :
+        text_review with value: {text_review}
+        field 2 named :
+        sentiment with value: $sentiment$
+        Field 3 named : 
+        language with value: {option}
+        Review text: '''{text_review}'''
+
+        """
+
+        prompt = PromptTemplate(template=template, input_variables=["text_review","option"])
+
+        llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+        if prompt:
+            response = llm_chain.run({"text_review": text_review, "option": option})
+            #json printed
+            print(response)
+            st.text(response)