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app.py

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+import gradio as gr
+import os
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.chains import LLMChain
+from langchain_core.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Setting the API
+model_Api = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+
+def QueryBuilding():
+    Query_template = """Consider yourself as a personalized helper to let farmers know how to handle biogas plant, for the user {query},
+                    
+                    Answer: provide guidance and support to the user in a more detailed, simple and straightforward manner."""
+    return Query_template
+
+def PromptEngineering():
+    Prompt = PromptTemplate.from_template(QueryBuilding())
+    return Prompt
+
+def LLM_building():
+    llm_model = HuggingFaceEndpoint(
+        repo_id=repo_id,
+        max_length=128,
+        token=model_Api
+    )
+    return llm_model
+
+def langchainning():
+    llm_chain = LLMChain(prompt=PromptEngineering(), llm=LLM_building())
+    return llm_chain
+
+def user_input(user):
+    ans = langchainning().run(user)
+    return ans
+
+def chat_interface(message, history):
+    response = user_input(message)
+    return response
+
+iface = gr.ChatInterface(
+    fn=chat_interface,
+    title="Gas Whizz",
+    description="Hello Friend, How can I help you today?",
+    examples=["How does a biogas plant work?", "What are the benefits of biofuel?"],
+    retry_btn=None,
+    undo_btn="Delete Previous",
+    clear_btn="Clear",
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)

requirements.txt

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+gradio
+langchain
+langchain-community
+langchain-core
+huggingface_hub
+python-dotenv