Update ABL-Agents.py

ABL-Agents.py

@@ -1,86 +1,86 @@
-import os
-import requests
-from dotenv import load_dotenv
-from langchain.agents import AgentType, initialize_agent, load_tools
-from langchain_openai import  OpenAI
-from transformers import AlbertTokenizer, AlbertForSequenceClassification
-
-
-load_dotenv()
-openai_api_key = os.getenv('OPENAI_API_KEY')
-
-from serpapi import GoogleSearch
-
-params = {
-    
-    "engine": "google",
-    "q": "ESPN",
-    "api_key": "1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6"
-    }
-
-search = GoogleSearch(params)
-results = search.get_dict()
-organic_results = results["organic_results"]
-
-llm = OpenAI(api_key=openai_api_key,temperature=0)
-tools = load_tools(["serpapi", "llm-math"], llm=llm)
-
-class fact_checking_pipeline: 
-    tokenizer= AlbertTokenizer.from_pretrained('Dzeniks/alberta_fact_checking')
-    model = AlbertForSequenceClassification.from_pretrained('Dzeniks/alberta_fact_checking')
-
-
-#Configure Langchain Agent
-agent_chain = initialize_agent(
-    tools,
-    llm,
-    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-    verbose=True,
-)
-
-class RefereeAgent:
-    def __init__(self):
-        self.serpapi_api_key = "1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6"
-        self.fact_checking_pipeline = AlbertForSequenceClassification.from_pretrained('Dzeniks/alberta_fact_checking')
-
-    def search_serpapi(self, query):
-        search_url = "https://serpapi.com/search"
-        params = {
-            "q": query,
-            "api_key": self.serpapi_api_key,
-        }
-        response = requests.get(search_url, params=params)
-        return response.json()
-
-    def extract_relevant_info(self, serpapi_results):
-        relevant_info = []
-        for result in serpapi_results.get('organic_results', []):
-            title = result.get('title')
-            snippet = result.get('snippet')
-            link = result.get('link')
-            relevant_info.append({'title': title, 'snippet': snippet, 'link': link})
-        return relevant_info
-
-    def fact_check(self, statement):
-        serpapi_results = self.search_serpapi(statement)
-        relevant_info = self.extract_relevant_info(serpapi_results)
-        
-        if not relevant_info:
-            return "No relevant data found."
-
-        context = " ".join([info['snippet'] for info in relevant_info])
-        result = self.fact_checking_pipeline(f"{statement} [SEP] {context}")
-        
-        label = result[0]['label']
-        score = result[0]['score']
-        
-        if label == "ENTAILMENT":
-            return f"The statement is likely true. Confidence score: {score}"
-        else:
-            return f"The statement is likely false. Confidence score: {score}"
-
-# Example usage
-referee = RefereeAgent()
-statement = "LeBron James is the most accomplished NBA player of all time, he has won 4 NBA championships."
-referee_result = referee.fact_check(statement)
-print(referee_result)
+import os
+from langchain_huggingface import HuggingFacePipeline
+from langchain_huggingface.llms import HuggingFacePipeline
+from langchain.agents import AgentType, initialize_agent, load_tools
+from transformers import pipeline
+
+
+load_dotenv()
+SERPAPI_API_KEY = os.getenv('SERPAPI_API_KEY')
+
+from serpapi import GoogleSearch
+
+params = {
+    
+    "engine": "google",
+    "q": "ESPN",
+    "api_key": "1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6"
+    }
+
+search = GoogleSearch(params)
+results = search.get_dict()
+organic_results = results["organic_results"]
+
+llm = HuggingFacePipeline(pipeline=pipeline("text2text-generation",
+               model="google/flan-t5-xl",
+               model_kwargs={"temperature":0.5},
+                huggingfacehub_api_token='<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>'
+
+               ))
+
+tools = load_tools(["serpapi", "llm-math"], llm=llm, serpapi_api_key=SERPAPI_API_KEY)
+
+class fact_checking_pipeline:
+    fact_checking_pipeline = pipeline("text-classification",
+                                  model="typeform/distilbert-base-uncased-mnli")
+
+
+#Configure Langchain Agent
+agent_chain = initialize_agent(
+    tools,
+    llm,
+    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    verbose=True,
+)
+
+class RefereeAgent:
+    def __init__(self):
+        self.serpapi_api_key = "1e2cf72abd974364e933d854720ec3704bb86196ee9a25267e53947192afada6"
+        self.fact_checking_pipeline = pipeline("text-classification",
+                                  model="typeform/distilbert-base-uncased-mnli")
+
+    def search_serpapi(self, query):
+        search_url = "https://serpapi.com/search"
+        params = {
+            "q": query,
+            "api_key": self.serpapi_api_key,
+        }
+        response = requests.get(search_url, params=params)
+        return response.json()
+
+    def extract_relevant_info(self, serpapi_results):
+        relevant_info = []
+        for result in serpapi_results.get('organic_results', []):
+            title = result.get('title')
+            snippet = result.get('snippet')
+            link = result.get('link')
+            relevant_info.append({'title': title, 'snippet': snippet, 'link': link})
+        return relevant_info
+
+    def fact_check(self, statement):
+        serpapi_results = self.search_serpapi(statement)
+        relevant_info = self.extract_relevant_info(serpapi_results)
+        
+        if not relevant_info:
+            return "No relevant data found."
+
+        context = " ".join([info['snippet'] for info in relevant_info])
+        result = self.fact_checking_pipeline(f"{statement} [SEP] {context}")
+        
+        label = result[0]['label']
+        score = result[0]['score']
+        
+        if label == "ENTAILMENT":
+            return f"The statement is likely true. Confidence score: {score}"
+        else:
+            return f"The statement is likely false. Confidence score: {score}"