Add application file

app.py

@@ -0,0 +1,159 @@
+import os
+import re
+import json
+import gradio as gr
+from langchain import HuggingFaceHub
+from langchain.output_parsers import PydanticOutputParser
+from langchain import PromptTemplate
+from pydantic import BaseModel, Field
+from transformers import pipeline
+from typing import List
+import openai
+
+
+
+openai.api_key = os.environ['OPENAI_API_TOKEN']
+
+HUGGINGFACEHUB_API_TOKEN=os.environ["HUGGINGFACEHUB_API_TOKEN"] 
+
+
+classifier = pipeline("sentiment-analysis")
+repo_id = "tiiuae/falcon-7b-instruct"
+llm = HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, 
+                     repo_id=repo_id, 
+                     model_kwargs={"temperature":0.1, "max_new_tokens":300})
+
+
+
+
+class Sentiment(BaseModel):
+    label: str = Field(description="Is a the above rview sentiment 'Good', or 'Bad' ?")
+
+
+
+
+def sentence_builder(Model,Text):
+    if Model=="Sentiment analysis pipeline":
+        good_label,bad_label=pipeline_sentiment(Text)
+    if Model=="Falcon-7b-instruct":
+        good_label,bad_label=falcon_sentiment(Text)
+    if Model=="GPT-4 Function call":
+        good_label,bad_label=gpt4_sentiment(Text)
+        
+    print({"Good": good_label, "Bad": bad_label})
+    return {"Good": good_label, "Bad": bad_label}
+
+
+demo = gr.Interface(
+    sentence_builder,
+    [
+     
+        gr.Dropdown(
+            ["Sentiment analysis pipeline","Falcon-7b-instruct","GPT-4 Function call"], label="Model", info="Wich model to use"
+        ),
+        gr.Textbox(
+            label="Text",
+            info="Review text",
+            lines=2,
+            value="I'm not sure about the origin of this product, it seems suspicious.",
+        ),
+        
+    ],
+   "label",
+    examples=[
+        
+        ["Sentiment analysis pipeline","The product broke ! Great ..."],
+        ["Sentiment analysis pipeline","Not sure if I like it or not."],
+        ["Sentiment analysis pipeline","This product is just a toy."],
+        ["Sentiment analysis pipeline","Bought a TV, received an Ipad..."],
+        ["Sentiment analysis pipeline","Could have found the same on wish.com ."],
+        ["Sentiment analysis pipeline","They did a wonderfull job at ripping us."],
+        ["Sentiment analysis pipeline","Is it dropshipping ?"],
+    ]
+)
+
+def pipeline_sentiment(text):
+    out = classifier(text)
+    print(out)
+    if out[0]['label'] == "NEGATIVE":
+        bad_label = out[0]['score']
+        good_label = 1 - bad_label
+    elif out[0]['label'] == "POSITIVE":
+        good_label = out[0]['score']
+        bad_label = 1 - good_label
+    print(good_label, bad_label)
+    return good_label, bad_label
+
+def falcon_sentiment(text):
+  
+    parser = PydanticOutputParser(pydantic_object=Sentiment)
+
+    prompt = PromptTemplate(
+        template="Classify the following  review as Good or Bad : .\n{format_instructions}\n{query}\n",
+        input_variables=["query"],
+        partial_variables={"format_instructions": parser.get_format_instructions()},
+    )
+
+    _input = prompt.format_prompt(query=text)
+
+    output = llm(_input.to_string())
+    print('Sentiment :', output)
+    try:
+        parsed_output=parser.parse(output)
+        print("parsed_output",parsed_output)
+    except:
+         
+        pattern = r'\b(Good|Bad)\b'
+        match = re.search(pattern, output)
+        print(match)
+        print(match.group(0))
+        parsed_output=match.group(0)
+    return parsed_output=="Good",parsed_output=='Bad'
+
+def Find_sentiment(sentence):
+   
+    system_msg = {"role": "system", "content": f'You an AI  that help me label sentences based on multiples features'}
+    
+     # Initialize messages array
+    messages = [system_msg]
+    message=f"Sentence: {sentence}, based on the sentence, fin the best sentiment to discribe this sentence."
+
+    messages.append({"role": "user", "content": message})
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4-0613",
+            messages=messages,
+            functions=[
+    {
+        "name": "set_sentiment",
+        "description": "Set the sentiment of the sentence.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "sentiment": {
+                    "type": "string",
+                    "description": "A Sentiment between 'Good' or 'Bad'",
+                },
+              
+            },
+            "required": ["sentiment"],
+        },
+    }
+        ],
+        function_call={"name": "set_sentiment"},
+)
+        assistant_msg = response['choices'][0]['message']
+        response_options = assistant_msg.to_dict()['function_call']['arguments']
+        options = json.loads(response_options)
+
+        return options["sentiment"]
+    except openai.error.OpenAIError as e:
+        print("Some error happened here.",e)
+        return
+
+def gpt4_sentiment(text):
+    out=Find_sentiment(text)
+    return out=="Good",out=='Bad'
+if __name__ == "__main__":
+    
+    demo.launch()