Update api.py

api.py

@@ -1,197 +1,198 @@
-import collections
-from collections.abc import MutableMapping
-collections.MutableMapping = MutableMapping  # Patch for deprecated MutableMapping
-
-import os
-import shutil
-import json
-import logging
-from contextlib import asynccontextmanager
-from typing import Dict
-
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-
-from langchain.chat_models import init_chat_model
-from langchain_core.documents import Document
-from langchain_core.prompts import PromptTemplate
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
-
-import config  # Ensure config.py has GROQ_API_KEY
-
-# Set environment variable for Groq API key
-os.environ["GROQ_API_KEY"] = config.GROQ_API_KEY
-
-# Setup logging
-logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger(__name__)
-
-# Global variables for RAG components
-rag_chain = None
-retriever = None
-session_states: Dict[str, str] = {}  # Store last_disease per session_id
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global rag_chain, retriever
-    persist_directory = "/data/chroma_crop_rag"
-
-    # Clear existing ChromaDB collection
-    if os.path.exists(persist_directory):
-        try:
-            shutil.rmtree(persist_directory)
-            logger.debug("Cleared existing ChromaDB directory: %s", persist_directory)
-        except Exception as e:
-            logger.error("Error clearing ChromaDB directory: %s", str(e))
-            raise
-
-    # Load JSON QA Knowledge Base
-    try:
-        with open("crop_disease_qa.json", "r", encoding="utf-8") as f:
-            data = json.load(f)
-        logger.debug("JSON loaded, length: %d", len(data))
-    except Exception as e:
-        logger.error("Error loading JSON: %s", str(e))
-        raise
-
-    # Convert to Documents
-    documents = [
-        Document(page_content=item["answer"], metadata={"question": item["question"]})
-        for item in data
-    ]
-    logger.debug("Documents created: %d", len(documents))
-
-    # Chunk Documents
-    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=100)
-    docs = splitter.split_documents(documents)
-    logger.debug("Documents after splitting: %d", len(docs))
-
-    # Embedding + Vectorstore
-    try:
-        embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-        logger.debug("Embedding model initialized")
-        db = Chroma.from_documents(docs, embedding_model, persist_directory=persist_directory)
-        retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 6})
-        logger.debug("ChromaDB initialized")
-    except Exception as e:
-        logger.error("ChromaDB/Embedding error: %s", str(e))
-        raise
-
-    # Groq LLM
-    try:
-        llm = init_chat_model(
-            "llama3-8b-8192",
-            model_provider="groq",
-            temperature=0.5
-        )
-        logger.debug("Groq LLM initialized")
-    except Exception as e:
-        logger.error("Groq LLM initialization error: %s", str(e))
-        raise
-
-    # Prompt Template
-    prompt_template = PromptTemplate(
-        input_variables=["context", "question"],
-        template="""
-        You're a friendly agricultural expert helping farmers with crop health.
-        Answer in a warm, conversational tone, like you're chatting with a neighbor.
-        Keep it clear, engaging, and avoid overly technical terms.
-        Use the provided context from the knowledge base to ensure accuracy.
-        If the question is a follow-up (e.g., 'how to treat them?', 'how to fix it?', 'what medicines should I use?'),
-        assume it refers to the previously discussed disease or crop (e.g., Early blight in Potato) unless specified otherwise.
-        If the context doesn't cover the question, provide a practical, general response with actionable tips.
-        make sure the aize of the answer is not more than 100 words.
-        Context: {context}
-
-        Question: {question}
-
-        Answer:
-        """
-    )
-
-    # RAG Chain
-    try:
-        rag_chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            retriever=retriever,
-            chain_type="stuff",
-            chain_type_kwargs={"prompt": prompt_template}
-        )
-        logger.debug("RAG chain initialized")
-    except Exception as e:
-        logger.error("RAG chain initialization error: %s", str(e))
-        raise
-
-    yield  # FastAPI is now running
-
-
-# Initialize FastAPI with lifespan
-app = FastAPI(title="Crop Health Assistant API", lifespan=lifespan)
-
-# Add CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Allows all origins
-    allow_credentials=True,
-    allow_methods=["*"],  # Allows all methods
-    allow_headers=["*"],  # Allows all headers
-)
-
-
-# Pydantic request model
-class QueryRequest(BaseModel):
-    query: str
-    session_id: str = "default"
-
-
-# Query endpoint
-@app.post("/query")
-async def query_crop_health(request: QueryRequest):
-    global session_states
-    query = request.query
-    session_id = request.session_id
-
-    if query.lower() == "exit":
-        session_states.pop(session_id, None)
-        return JSONResponse(content={"message": "Session ended"})
-
-    # Handle follow-up queries
-    modified_query = query
-    last_disease = session_states.get(session_id)
-    if last_disease and query.lower() in [
-        "how to treat them?", "how to fix it?",
-        "how to manage it?", "what medicines should i use?"
-    ]:
-        modified_query = f"What medicines or treatments for {last_disease}?"
-
-    try:
-        response = rag_chain.invoke({"query": modified_query})["result"]
-        # Simple heuristic to update last disease
-        if "blight" in query.lower() or "potato" in query.lower():
-            session_states[session_id] = "Early blight in Potato"
-
-        return JSONResponse(content={"question": query, "answer": response})
-    except Exception as e:
-        logger.error("RAG chain execution error for query '%s': %s", query, str(e))
-        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")
-
-
-# Session reset endpoint
-@app.delete("/reset-session/{session_id}")
-async def reset_session(session_id: str):
-    global session_states
-    session_states.pop(session_id, None)
-    return JSONResponse(content={"message": f"Session {session_id} reset"})
-
-
-# Run FastAPI with Uvicorn
-if __name__ == "__main__":
-    import uvicorn
-    print("Starting FastAPI server")
+import collections
+from collections.abc import MutableMapping
+collections.MutableMapping = MutableMapping  # Patch for deprecated MutableMapping
+
+import os
+import shutil
+import json
+import logging
+from contextlib import asynccontextmanager
+from typing import Dict
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+from langchain.chat_models import init_chat_model
+from langchain_core.documents import Document
+from langchain_core.prompts import PromptTemplate
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+
+import config  # Ensure config.py has GROQ_API_KEY
+
+# Set environment variable for Groq API key
+os.environ["GROQ_API_KEY"] = config.GROQ_API_KEY
+
+# Setup logging
+logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+# Global variables for RAG components
+rag_chain = None
+retriever = None
+session_states: Dict[str, str] = {}  # Store last_disease per session_id
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global rag_chain, retriever
+    persist_directory = "/app/data/chroma_crop_rag"
+
+
+    # Clear existing ChromaDB collection
+    if os.path.exists(persist_directory):
+        try:
+            shutil.rmtree(persist_directory)
+            logger.debug("Cleared existing ChromaDB directory: %s", persist_directory)
+        except Exception as e:
+            logger.error("Error clearing ChromaDB directory: %s", str(e))
+            raise
+
+    # Load JSON QA Knowledge Base
+    try:
+        with open("crop_disease_qa.json", "r", encoding="utf-8") as f:
+            data = json.load(f)
+        logger.debug("JSON loaded, length: %d", len(data))
+    except Exception as e:
+        logger.error("Error loading JSON: %s", str(e))
+        raise
+
+    # Convert to Documents
+    documents = [
+        Document(page_content=item["answer"], metadata={"question": item["question"]})
+        for item in data
+    ]
+    logger.debug("Documents created: %d", len(documents))
+
+    # Chunk Documents
+    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=100)
+    docs = splitter.split_documents(documents)
+    logger.debug("Documents after splitting: %d", len(docs))
+
+    # Embedding + Vectorstore
+    try:
+        embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+        logger.debug("Embedding model initialized")
+        db = Chroma.from_documents(docs, embedding_model, persist_directory=persist_directory)
+        retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 6})
+        logger.debug("ChromaDB initialized")
+    except Exception as e:
+        logger.error("ChromaDB/Embedding error: %s", str(e))
+        raise
+
+    # Groq LLM
+    try:
+        llm = init_chat_model(
+            "llama3-8b-8192",
+            model_provider="groq",
+            temperature=0.5
+        )
+        logger.debug("Groq LLM initialized")
+    except Exception as e:
+        logger.error("Groq LLM initialization error: %s", str(e))
+        raise
+
+    # Prompt Template
+    prompt_template = PromptTemplate(
+        input_variables=["context", "question"],
+        template="""
+        You're a friendly agricultural expert helping farmers with crop health.
+        Answer in a warm, conversational tone, like you're chatting with a neighbor.
+        Keep it clear, engaging, and avoid overly technical terms.
+        Use the provided context from the knowledge base to ensure accuracy.
+        If the question is a follow-up (e.g., 'how to treat them?', 'how to fix it?', 'what medicines should I use?'),
+        assume it refers to the previously discussed disease or crop (e.g., Early blight in Potato) unless specified otherwise.
+        If the context doesn't cover the question, provide a practical, general response with actionable tips.
+        make sure the aize of the answer is not more than 100 words.
+        Context: {context}
+
+        Question: {question}
+
+        Answer:
+        """
+    )
+
+    # RAG Chain
+    try:
+        rag_chain = RetrievalQA.from_chain_type(
+            llm=llm,
+            retriever=retriever,
+            chain_type="stuff",
+            chain_type_kwargs={"prompt": prompt_template}
+        )
+        logger.debug("RAG chain initialized")
+    except Exception as e:
+        logger.error("RAG chain initialization error: %s", str(e))
+        raise
+
+    yield  # FastAPI is now running
+
+
+# Initialize FastAPI with lifespan
+app = FastAPI(title="Crop Health Assistant API", lifespan=lifespan)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods
+    allow_headers=["*"],  # Allows all headers
+)
+
+
+# Pydantic request model
+class QueryRequest(BaseModel):
+    query: str
+    session_id: str = "default"
+
+
+# Query endpoint
+@app.post("/query")
+async def query_crop_health(request: QueryRequest):
+    global session_states
+    query = request.query
+    session_id = request.session_id
+
+    if query.lower() == "exit":
+        session_states.pop(session_id, None)
+        return JSONResponse(content={"message": "Session ended"})
+
+    # Handle follow-up queries
+    modified_query = query
+    last_disease = session_states.get(session_id)
+    if last_disease and query.lower() in [
+        "how to treat them?", "how to fix it?",
+        "how to manage it?", "what medicines should i use?"
+    ]:
+        modified_query = f"What medicines or treatments for {last_disease}?"
+
+    try:
+        response = rag_chain.invoke({"query": modified_query})["result"]
+        # Simple heuristic to update last disease
+        if "blight" in query.lower() or "potato" in query.lower():
+            session_states[session_id] = "Early blight in Potato"
+
+        return JSONResponse(content={"question": query, "answer": response})
+    except Exception as e:
+        logger.error("RAG chain execution error for query '%s': %s", query, str(e))
+        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")
+
+
+# Session reset endpoint
+@app.delete("/reset-session/{session_id}")
+async def reset_session(session_id: str):
+    global session_states
+    session_states.pop(session_id, None)
+    return JSONResponse(content={"message": f"Session {session_id} reset"})
+
+
+# Run FastAPI with Uvicorn
+if __name__ == "__main__":
+    import uvicorn
+    print("Starting FastAPI server")
     uvicorn.run(app, host="0.0.0.0", port=7860)