Upload app.py

app.py

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+import gradio as gr
+from langchain.prompts import PromptTemplate
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_core.output_parsers import JsonOutputParser
+from langdetect import detect
+import time
+
+# Initialize the LLM and other components
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    task="text-generation",
+    max_new_tokens=4096,
+    temperature=0.5,
+    do_sample=False,
+)
+llm_engine_hf = ChatHuggingFace(llm=llm)
+
+template_classify = '''
+Please carefully read the following text. The text is written in {LANG} language:
+
+<text>  
+{TEXT}  
+</text>
+
+After reading it, I want you to classify it in three groups: Positive, Negative, or Neutral.
+Your final response MUST contain only the response, no other text.
+Example:
+Positive
+Negative
+Neutral
+'''
+
+template_json = '''
+Your task is to read the following text, convert it to json format using 'Answer' as key and return it.
+<text>
+{RESPONSE}
+</text>
+
+Your final response MUST contain only the response, no other text.
+Example:
+{{"Answer":"Positive"}}
+'''
+json_output_parser = JsonOutputParser()
+
+# Define the classify_text function
+def classify_text(text):
+    global llm
+
+    start = time.time()
+    lang = detect(text)
+
+    prompt_classify = PromptTemplate(
+        template=template_classify,
+        input_variables=["LANG", "TEXT"]
+    )
+    formatted_prompt = prompt_classify.format(TEXT=text, LANG=lang)
+    classify = llm.invoke(formatted_prompt)
+
+    prompt_json = PromptTemplate(
+        template=template_json,
+        input_variables=["RESPONSE"]
+    )
+
+    formatted_prompt = template_json.format(RESPONSE=classify)
+    response = llm.invoke(formatted_prompt)
+
+    parsed_output = json_output_parser.parse(response)
+    end = time.time()
+    duration = end - start
+    return parsed_output, duration #['Answer']
+
+# Create the Gradio interface
+def gradio_app(text):
+    classification, time_taken = classify_text(text)
+    return classification, f"Time taken: {time_taken:.2f} seconds"
+
+def create_gradio_interface():
+    with gr.Blocks() as iface:
+        text_input = gr.Textbox(label="Text to Classify")
+        output_text = gr.Textbox(label="Classification")
+        time_taken = gr.Textbox(label="Time Taken (seconds)")
+        submit_btn = gr.Button("Classify")
+
+        submit_btn.click(fn=classify_text, inputs=text_input, outputs=[output_text, time_taken])
+
+    iface.launch()
+
+if __name__ == "__main__":
+    create_gradio_interface()