init1

app.py

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+import os
+import gradio as gr
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.output_parsers import StrOutputParser
+
+# Ensure environment variables are loaded
+langchain_key = os.getenv("LANGCHAIN_API_KEY")
+HF_key = os.getenv("HUGGINGFACEHUB_TOKEN")
+
+if langchain_key is None or HF_key is None:
+    raise ValueError("API keys are not set. Please set LANGCHAIN_API_KEY and HUGGINGFACEHUB_TOKEN.")
+
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_API_KEY"] = langchain_key
+os.environ['HUGGINGFACEHUB_TOKEN'] = HF_key
+
+# 1. Prompt Template
+system_template = "Translate the following from into {language}:"
+human_template = "{text}"
+
+prompt_template = ChatPromptTemplate.from_messages([
+    SystemMessage(content=system_template),
+    HumanMessage(content=human_template)
+])
+
+# 2. Create model
+llm = HuggingFaceEndpoint(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+    task="translation",
+    max_new_tokens=150,
+    do_sample=False,
+    token=HF_key
+)
+
+# 3. Create parser
+parser = StrOutputParser()
+
+# 4. Create chain
+chain = prompt_template | llm | parser
+
+def translate(text, language):
+    input_data = {
+        "text": text,
+        "language": language
+    }
+    # Use the .invoke() method to properly run the chain
+    result = chain.invoke(input_data)
+    return result
+
+# Gradio interface
+iface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter text to translate"),
+        gr.Textbox(lines=1, placeholder="Enter target language")
+    ],
+    outputs="text",
+    title="LangChain Translation Service",
+    description="Translate text using LangChain and Hugging Face."
+)
+
+if __name__ == "__main__":
+    iface.launch()