Update agent.py

agent.py

@@ -14,6 +14,34 @@ from langchain_experimental.tools import PythonREPLTool
 import ast, json
 
 from langchain_community.tools import DuckDuckGoSearchRun
+import os
+from langchain_huggingface import HuggingFaceEndpoint , ChatHuggingFace
+from langchain import LLMChain, PromptTemplate
+
+def get_gpt_and_answer(question):
+      # إنشاء LLM باستخدام endpoint من Hugging Face
+      llm = HuggingFaceEndpoint(
+          repo_id="openai/gpt-oss-120b",               # أو أي نموذج موجود في HF
+          task="text-generation",
+
+          huggingfacehub_api_token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
+          provider="auto"              # يسمح باختيار المزود تلقائياً
+
+      )
+
+      # إنشاء سلسلة (chain) بسيطة
+      template = """
+      Question: {question}
+      Answer:
+      """
+      llm = ChatHuggingFace(llm=llm)
+
+      prompt = PromptTemplate.from_template(template)
+      chain = LLMChain(llm=llm, prompt=prompt)
+
+      # تجربة
+      result = chain.invoke({"question": question})
+      return (result['text'])
 
 
 ########## State ############
@@ -80,11 +108,8 @@ def get_code(state:InfoState) -> InfoState:
     f"- Import modules only if needed (e.g. datetime, math)"
 )
 
-    # 2️⃣ Call Gemini
-    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
-    response = model.invoke([HumanMessage(content=prompt)]).content.strip()
-
-    state["answer_code"] = response
+ 
+    state["answer_code"] = get_gpt_and_answer(prompt).strip()
 
 
     return state
@@ -125,9 +150,7 @@ def preprocess_text(state: dict) -> InfoState:
     )
 
 
-    # 2️⃣ Call Gemini
-    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
-    response = model.invoke([HumanMessage(content=prompt)]).content.strip()
+    response = get_gpt_and_answer(prompt).strip()
 
     # 3️⃣ Try to safely parse
     try:
@@ -166,13 +189,12 @@ def get_answer(state: InfoState) -> InfoState :
         "- Do not include tool names, prefixes, or metadata.\n"
         "- If the context contains partial hints, you can infer the answer from general knowledge of the same topic.\n"
         "- If the question asks about an attached file or audio, reply briefly that you cannot access attachments or audio files."
-        "- If absolutely nothing is relevant, reply: I'm not sure because the question depends on a file or missing data.\n\n"
+        "- If the context lacks key details or references a file, start with: I'm not sure because the question depends on missing data or an attached file. Then, add what you reasonably know about the topic."
         "- Final answer should be complete text not part of answer"
         "Final Answer:"
     )
 
-    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
-    state["final_answer"] = (model.invoke([HumanMessage(content=prompt)]).content)
+    state["final_answer"] = get_gpt_and_answer(prompt)
 
     return state
 
@@ -196,9 +218,8 @@ def get_type(state: InfoState) -> InfoState:
     - Output nothing else. No punctuation, no quotes, no explanation.
     - If unsure, default to LLM.
     """
-
-    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
-    state["answer_type"] = (model.invoke([HumanMessage(content=prompt)]).content)
+    
+    state["answer_type"] = get_gpt_and_answer(prompt)
 
     return state
 
@@ -275,4 +296,4 @@ def ask(compiled_graph,question):
 
         })
 
-        return legitimate_result['final_answer']
+        return legitimate_result['final_answer'] #,legitimate_result