Upload app2.py

app2.py

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+from langchain_community.llms import HuggingFaceEndpoint 
+import os 
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+import streamlit as st 
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+# def qabot(question):
+#     llm_hugginface = HuggingFaceEndpoint(repo_id='google/flan-t5-large',token=os.getenv("HUGGINGFACEHUB_API_TOKEN"),temperature=0.5,max_length=128)
+#     result= llm_hugginface("Can you write me the capital of {question}")
+#     return result
+
+# ans =qabot("india")
+# print(ans)
+
+# question = "Who won the FIFA World Cup in the year 1994? "
+def qabot(question):
+    template = """Question: {question}
+
+    Answer: Let's think step by step."""
+
+    prompt = PromptTemplate.from_template(template)
+
+    repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
+
+    llm = HuggingFaceEndpoint(
+        repo_id=repo_id, max_length=128, temperature=0.5, token=os.getenv('HUGGINGFACEHUB_API_TOKEN')
+    )
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    result =llm_chain.run(question)
+    return result
+
+# print(qabot("Who won the FIFA World Cup in the year 1994? "))
+
+
+
+st.header("Langchain Application")
+
+input=st.text_input("Input: ",key="input")
+response=qabot(input)
+
+submit=st.button("Ask the question")
+
+## If ask button is clicked
+
+if submit:
+    st.subheader("The Response is")
+    st.write(response)
+