Update app.py

app.py

@@ -1,107 +1,107 @@
-import os
-import logging
-from dotenv import load_dotenv
-from fastapi import FastAPI, Request, Form
-from fastapi.responses import JSONResponse, HTMLResponse
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.templating import Jinja2Templates
-from langchain.chat_models import ChatOpenAI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.vectorstores import FAISS
-
-# Load environment variables
-load_dotenv()
-
-# Securely retrieve the OpenAI API key
-openai_api_key = os.getenv("sk-89la-SEB-GWL62eQC0RYXpVzPlYGLCZuXIAz39F58YT3BlbkFJqdiuuTxIAUwxud-MhcL6tAjLxZJvXbpEEcMUkN8DIA")
-if not openai_api_key:
-    raise ValueError("Missing OpenAI API key. Set OPENAI_API_KEY in your environment variables.")
-
-# Initialize FastAPI app
-app = FastAPI()
-templates = Jinja2Templates(directory="templates")
-
-# Configure CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Initialize OpenAI embeddings
-embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-
-# Load FAISS index with error handling
-try:
-    db = FAISS.load_local("faiss_index", embeddings)
-    logger.info("FAISS index loaded successfully.")
-except Exception as e:
-    logger.error(f"Error loading FAISS index: {e}")
-    db = None  # Avoid crashing
-
-# Define the prompt template
-prompt_template = """
-You are an expert in skin cancer research.
-Answer the question based only on the provided context, which may include text, images, or tables.
-
-Context:
-{context}
-
-Question: {question}
-
-If the context does not contain sufficient information, say: "Sorry, I don't have much information about it."
-
-Answer:
-"""
-
-qa_chain = LLMChain(
-    llm=ChatOpenAI(model="gpt-4", openai_api_key=openai_api_key, max_tokens=1024),
-    prompt=PromptTemplate.from_template(prompt_template),
-)
-
-@app.get("/", response_class=HTMLResponse)
-async def index(request: Request):
-    return templates.TemplateResponse("index.html", {"request": request})
-
-@app.post("/get_answer")
-async def get_answer(question: str = Form(...)):
-    if db is None:
-        return JSONResponse({"error": "FAISS database is unavailable."}, status_code=500)
-
-    try:
-        # Retrieve relevant documents from FAISS
-        relevant_docs = db.similarity_search(question)
-        context = ""
-        relevant_images = []
-
-        for d in relevant_docs:
-            doc_type = d.metadata.get('type', 'text')
-            original_content = d.metadata.get('original_content', '')
-
-            if doc_type == 'text':
-                context += f"[text] {original_content}\n"
-            elif doc_type == 'table':
-                context += f"[table] {original_content}\n"
-            elif doc_type == 'image':
-                context += f"[image] {d.page_content}\n"
-                relevant_images.append(original_content)
-
-        # Run the question-answering chain
-        result = qa_chain.run({'context': context, 'question': question})
-
-        # Handle cases where no relevant images are found
-        return JSONResponse({
-            "relevant_images": relevant_images[0] if relevant_images else None,
-            "result": result,
-        })
-    except Exception as e:
-        logger.error(f"Error processing request: {e}")
-        return JSONResponse({"error": "Internal server error."}, status_code=500)
+import os
+import logging
+from dotenv import load_dotenv
+from fastapi import FastAPI, Request, Form
+from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.templating import Jinja2Templates
+from langchain_community.chat_models import ChatOpenAI
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain_community.vectorstores import FAISS
+
+# Load environment variables
+load_dotenv()
+
+# Securely retrieve the OpenAI API key
+openai_api_key = os.getenv("sk-89la-SEB-GWL62eQC0RYXpVzPlYGLCZuXIAz39F58YT3BlbkFJqdiuuTxIAUwxud-MhcL6tAjLxZJvXbpEEcMUkN8DIA")
+if not openai_api_key:
+    raise ValueError("Missing OpenAI API key. Set OPENAI_API_KEY in your environment variables.")
+
+# Initialize FastAPI app
+app = FastAPI()
+templates = Jinja2Templates(directory="templates")
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize OpenAI embeddings
+embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+# Load FAISS index with error handling
+try:
+    db = FAISS.load_local("faiss_index", embeddings)
+    logger.info("FAISS index loaded successfully.")
+except Exception as e:
+    logger.error(f"Error loading FAISS index: {e}")
+    db = None  # Avoid crashing
+
+# Define the prompt template
+prompt_template = """
+You are an expert in skin cancer research.
+Answer the question based only on the provided context, which may include text, images, or tables.
+
+Context:
+{context}
+
+Question: {question}
+
+If the context does not contain sufficient information, say: "Sorry, I don't have much information about it."
+
+Answer:
+"""
+
+qa_chain = LLMChain(
+    llm=ChatOpenAI(model="gpt-4", openai_api_key=openai_api_key, max_tokens=1024),
+    prompt=PromptTemplate.from_template(prompt_template),
+)
+
+@app.get("/", response_class=HTMLResponse)
+async def index(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/get_answer")
+async def get_answer(question: str = Form(...)):
+    if db is None:
+        return JSONResponse({"error": "FAISS database is unavailable."}, status_code=500)
+
+    try:
+        # Retrieve relevant documents from FAISS
+        relevant_docs = db.similarity_search(question)
+        context = ""
+        relevant_images = []
+
+        for d in relevant_docs:
+            doc_type = d.metadata.get('type', 'text')
+            original_content = d.metadata.get('original_content', '')
+
+            if doc_type == 'text':
+                context += f"[text] {original_content}\n"
+            elif doc_type == 'table':
+                context += f"[table] {original_content}\n"
+            elif doc_type == 'image':
+                context += f"[image] {d.page_content}\n"
+                relevant_images.append(original_content)
+
+        # Run the question-answering chain
+        result = qa_chain.run({'context': context, 'question': question})
+
+        # Handle cases where no relevant images are found
+        return JSONResponse({
+            "relevant_images": relevant_images[0] if relevant_images else None,
+            "result": result,
+        })
+    except Exception as e:
+        logger.error(f"Error processing request: {e}")
+        return JSONResponse({"error": "Internal server error."}, status_code=500)