Update api/endpoints.py

api/endpoints.py

@@ -1,157 +1,157 @@
-from fastapi import APIRouter, HTTPException, Request
-from api.models import PromptRequest, HistoryRequest
-from core.llm import llm
-from core.memory import get_memory, conversation_memories
-from core.utils import classify_message, process_response
-import uuid
-import time
-from langchain.schema import HumanMessage, AIMessage
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain, ConversationChain
-
-router = APIRouter()
-
-# Prompt templates
-CONVERSATION_TEMPLATE = """You are a helpful AI assistant. Have a natural conversation with the user.
-
-Current conversation:
-{history}
-Human: {input}
-AI:"""
-
-CODE_GENERATION_TEMPLATE = """You are an expert Python programmer. Generate clean, efficient Python code for the given request.
-
-Request: {input}
-
-Provide only the Python code without explanation:
-```python"""
-
-EXPLANATION_TEMPLATE = """You are a programming tutor. Explain how to solve the programming task clearly without providing code.
-
-Task: {input}
-
-Explanation:"""
-
-BOTH_TEMPLATE = """You are an expert Python programmer and tutor. For the given request, provide both a clear explanation and Python code.
-
-Request: {input}
-
-First, provide a clear explanation of the approach:
-[EXPLANATION]
-
-Then, provide the Python code:
-```python"""
-
-def create_chain(template: str, memory=None):
-    input_vars = ["input"]
-    if memory:
-        input_vars.append("history")
-    prompt = PromptTemplate(
-        input_variables=input_vars,
-        template=template
-    )
-    if memory:
-        return ConversationChain(
-            llm=llm,
-            prompt=prompt,
-            memory=memory,
-            verbose=True,
-            output_key="output"
-        )
-    else:
-        return LLMChain(
-            llm=llm,
-            prompt=prompt,
-            verbose=True,
-            output_key="text"
-        )
-
-@router.get("/")
-async def root():
-    return {
-        "status": "ok",
-        "title": "LangChain Chat Bot API",
-        "version": "2.0.0",
-        "model": llm.model if llm else None,
-        "description": "Advanced chatbot API using LangChain and Groq models",
-        "features": ["Conversation Memory", "Code Generation", "Explanations", "Session Management"]
-    }
-
-@router.post("/generate/")
-async def generate_response(request: PromptRequest):
-    if llm is None:
-        raise HTTPException(status_code=503, detail="AI model not initialized. Check server logs for errors during startup (e.g., missing API key).")
-    try:
-        session_id = request.session_id or str(uuid.uuid4())
-        memory = get_memory(session_id, request.max_history)
-        message_type = classify_message(request.prompt)
-        response_data = {}
-        if message_type == "conversation" or request.response_type == "conversation":
-            chain = create_chain(CONVERSATION_TEMPLATE, memory)
-            response = chain.predict(input=request.prompt)
-            response_data = {"response": response.strip(), "message_type": "conversation"}
-        elif request.response_type == "code":
-            chain = create_chain(CODE_GENERATION_TEMPLATE)
-            response = chain.run(input=request.prompt)
-            response_data = process_response(response, "code")
-            response_data["message_type"] = "code"
-        elif request.response_type == "explanation":
-            chain = create_chain(EXPLANATION_TEMPLATE)
-            response = chain.run(input=request.prompt)
-            response_data = process_response(response, "explanation")
-            response_data["message_type"] = "explanation"
-        else:  # "both"
-            chain = create_chain(BOTH_TEMPLATE)
-            response = chain.run(input=request.prompt)
-            response_data = process_response(response, "both")
-            response_data["message_type"] = "both"
-        response_data["session_id"] = session_id
-        print(f"✅ Generated response for session {session_id}")
-        return response_data
-    except Exception as e:
-        print(f"❌ Error in generate_response: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Generation error: {str(e)}")
-
-@router.post("/clear_history/")
-async def clear_history(request: HistoryRequest):
-    if request.session_id in conversation_memories:
-        conversation_memories[request.session_id].clear()
-        return {"status": "success", "message": "Conversation history cleared"}
-    return {"status": "not_found", "message": "Session ID not found"}
-
-@router.post("/get_history/")
-async def get_history(request: HistoryRequest):
-    if request.session_id in conversation_memories:
-        memory = conversation_memories[request.session_id]
-        messages = memory.chat_memory.messages
-        history = []
-        for msg in messages:
-            if isinstance(msg, HumanMessage):
-                history.append(f"Human: {msg.content}")
-            elif isinstance(msg, AIMessage):
-                history.append(f"AI: {msg.content}")
-        return {"status": "success", "history": history}
-    return {"status": "not_found", "message": "Session ID not found"}
-
-@router.get("/sessions/")
-async def get_active_sessions():
-    return {
-        "active_sessions": list(conversation_memories.keys()),
-        "total_sessions": len(conversation_memories)
-    }
-
-@router.delete("/sessions/{session_id}")
-async def delete_session(session_id: str):
-    if session_id in conversation_memories:
-        del conversation_memories[session_id]
-        return {"status": "success", "message": f"Session {session_id} deleted"}
-    return {"status": "not_found", "message": "Session ID not found"}
-
-@router.get("/health")
-async def health_check():
-    return {
-        "status": "healthy",
-        "model": llm.model if llm else None,
-        "active_sessions": len(conversation_memories),
-        "langchain_version": "0.0.350"
+from fastapi import APIRouter, HTTPException, Request
+from api.models import PromptRequest, HistoryRequest
+from core.llm import llm, GROQ_MODEL
+from core.memory import get_memory, conversation_memories
+from core.utils import classify_message, process_response
+import uuid
+import time
+from langchain.schema import HumanMessage, AIMessage
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain, ConversationChain
+
+router = APIRouter()
+
+# Prompt templates
+CONVERSATION_TEMPLATE = """You are a helpful AI assistant. Have a natural conversation with the user.
+
+Current conversation:
+{history}
+Human: {input}
+AI:"""
+
+CODE_GENERATION_TEMPLATE = """You are an expert Python programmer. Generate clean, efficient Python code for the given request.
+
+Request: {input}
+
+Provide only the Python code without explanation:
+```python"""
+
+EXPLANATION_TEMPLATE = """You are a programming tutor. Explain how to solve the programming task clearly without providing code.
+
+Task: {input}
+
+Explanation:"""
+
+BOTH_TEMPLATE = """You are an expert Python programmer and tutor. For the given request, provide both a clear explanation and Python code.
+
+Request: {input}
+
+First, provide a clear explanation of the approach:
+[EXPLANATION]
+
+Then, provide the Python code:
+```python"""
+
+def create_chain(template: str, memory=None):
+    input_vars = ["input"]
+    if memory:
+        input_vars.append("history")
+    prompt = PromptTemplate(
+        input_variables=input_vars,
+        template=template
+    )
+    if memory:
+        return ConversationChain(
+            llm=llm,
+            prompt=prompt,
+            memory=memory,
+            verbose=True,
+            output_key="output"
+        )
+    else:
+        return LLMChain(
+            llm=llm,
+            prompt=prompt,
+            verbose=True,
+            output_key="text"
+        )
+
+@router.get("/")
+async def root():
+    return {
+        "status": "ok",
+        "title": "LangChain Chat Bot",
+        "version": "2.0.0",
+        "model": GROQ_MODEL,
+        "description": "Advanced chatbot using LangChain and Groq models",
+        "features": ["Conversation Memory", "Code Generation", "Explanations", "Session Management"]
+    }
+
+@router.post("/generate/")
+async def generate_response(request: PromptRequest):
+    if llm is None:
+        raise HTTPException(status_code=503, detail="AI model not initialized. Check server logs for errors during startup (e.g., missing API key).")
+    try:
+        session_id = request.session_id or str(uuid.uuid4())
+        memory = get_memory(session_id, request.max_history)
+        message_type = classify_message(request.prompt)
+        response_data = {}
+        if message_type == "conversation" or request.response_type == "conversation":
+            chain = create_chain(CONVERSATION_TEMPLATE, memory)
+            response = chain.predict(input=request.prompt)
+            response_data = {"response": response.strip(), "message_type": "conversation"}
+        elif request.response_type == "code":
+            chain = create_chain(CODE_GENERATION_TEMPLATE)
+            response = chain.run(input=request.prompt)
+            response_data = process_response(response, "code")
+            response_data["message_type"] = "code"
+        elif request.response_type == "explanation":
+            chain = create_chain(EXPLANATION_TEMPLATE)
+            response = chain.run(input=request.prompt)
+            response_data = process_response(response, "explanation")
+            response_data["message_type"] = "explanation"
+        else:  # "both"
+            chain = create_chain(BOTH_TEMPLATE)
+            response = chain.run(input=request.prompt)
+            response_data = process_response(response, "both")
+            response_data["message_type"] = "both"
+        response_data["session_id"] = session_id
+        print(f"✅ Generated response for session {session_id}")
+        return response_data
+    except Exception as e:
+        print(f"❌ Error in generate_response: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Generation error: {str(e)}")
+
+@router.post("/clear_history/")
+async def clear_history(request: HistoryRequest):
+    if request.session_id in conversation_memories:
+        conversation_memories[request.session_id].clear()
+        return {"status": "success", "message": "Conversation history cleared"}
+    return {"status": "not_found", "message": "Session ID not found"}
+
+@router.post("/get_history/")
+async def get_history(request: HistoryRequest):
+    if request.session_id in conversation_memories:
+        memory = conversation_memories[request.session_id]
+        messages = memory.chat_memory.messages
+        history = []
+        for msg in messages:
+            if isinstance(msg, HumanMessage):
+                history.append(f"Human: {msg.content}")
+            elif isinstance(msg, AIMessage):
+                history.append(f"AI: {msg.content}")
+        return {"status": "success", "history": history}
+    return {"status": "not_found", "message": "Session ID not found"}
+
+@router.get("/sessions/")
+async def get_active_sessions():
+    return {
+        "active_sessions": list(conversation_memories.keys()),
+        "total_sessions": len(conversation_memories)
+    }
+
+@router.delete("/sessions/{session_id}")
+async def delete_session(session_id: str):
+    if session_id in conversation_memories:
+        del conversation_memories[session_id]
+        return {"status": "success", "message": f"Session {session_id} deleted"}
+    return {"status": "not_found", "message": "Session ID not found"}
+
+@router.get("/health")
+async def health_check():
+    return {
+        "status": "healthy",
+        "model": llm.model if llm else None,
+        "active_sessions": len(conversation_memories),
+        "langchain_version": "0.0.350"
     }