Upload 4 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+faiss_index_datamodel/index.faiss filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,80 @@
+import streamlit as st
+import json
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+model_name = "sentence-transformers/all-mpnet-base-v2"
+hf_embeddings = HuggingFaceEmbeddings(
+    model_name = model_name,
+    model_kwargs = {'device':'cpu'},
+    encode_kwargs = {'normalize_embeddings': False}
+)
+from langchain_groq import ChatGroq
+@st.cache_resource
+def load_resources():
+    vectorstore = FAISS.load_local("faiss_index_datamodel", hf_embeddings, allow_dangerous_deserialization=True)
+    llm = ChatGroq(
+    temperature=0,
+    groq_api_key = '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',
+    model_name = "llama-3.1-8b-instant"
+    )
+    return vectorstore, llm
+
+vectorstore, llm = load_resources()
+
+prompt_template = ChatPromptTemplate.from_template(
+    """You are a knowledgeable and helpful agricultural expert. Your task is to provide clear, concise, and accurate answers to questions on various agricultural topics.
+
+    **Guidelines:**
+
+    1. **Thoroughly address all aspects of the question**, using information from the provided context **and your own expertise**.
+    2. **Include specific examples and details** to illustrate key points, such as:
+    - Names of states, regions, or countries.
+    - Specific crops, livestock, or agricultural practices.
+    - Tools, technologies, or services (e.g., online platforms, government programs).
+    3. Provide **practical, actionable advice** that the questioner can implement.
+    4. **Stay directly relevant** to the question, avoiding unnecessary or unrelated information.
+    5. Respond in a **natural, conversational tone** as if speaking directly to the person asking the question.
+    6. Keep your response **focused and concise**, ideally within 4-5 sentences.
+    7. If the question cannot be answered accurately, **acknowledge the limitations gently** and provide the best possible response.
+    8. **Avoid using phrases** like "Based on the context" or "From the given information" in your response.
+
+    **Context:**
+    {context}
+
+    **Question:**
+    {question}
+
+    **Answer:**"""
+)
+chain = prompt_template | llm
+
+st.title("AgriChat")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Ask your agricultural question:"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    relevant_documents = vectorstore.similarity_search_with_score(prompt, k=4)
+    context = "\n\n".join([doc[0].page_content for doc in relevant_documents])
+
+    response = chain.invoke({"context": context, "question": prompt})
+
+    st.session_state.messages.append({"role": "assistant", "content": response.content})
+
+    st.rerun()
+
+st.sidebar.header("Instructions")
+st.sidebar.markdown("""
+- Type your question in the input box and press Enter.
+- The assistant will provide answers based on relevant agricultural contexts.
+""")

faiss_index_datamodel/index.faiss

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34d27d1347a851c0f775dcec8d004e68fa98b84983a453649f3623ae16841391
+size 71224365

faiss_index_datamodel/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a63ea5b3ee4c9e4e4ce652b13c3df674e75bef471cd7ba4aa8dbf8c7b6692228
+size 25540079

requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+langchain
+langchain-community
+langchain-groq
+faiss-cpu
+sentence-transformers