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.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,6 @@
+---
+title: crypto_crewAI
+app_file: chat_gradio.py
+sdk: gradio
+sdk_version: 4.26.0
+---

.ipynb_checkpoints/agents-checkpoint.py

@@ -45,7 +45,8 @@ class CryptoAnalysisAgents():
                 'Armed with a flair for storytelling, the Content Writer transforms intricate crypto data into captivating narratives,'
                 'illuminating the latest trends for a wide audience.'
             ),
-            tools=[search_tool, 
+            tools=[
+                # search_tool, 
                    # AskWolframAlpha(),
                   ],
             allow_delegation=False,

.ipynb_checkpoints/chat_gradio-checkpoint.py

@@ -1,32 +1,25 @@
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain.prompts import PromptTemplate
-from langchain.schema import AIMessage, HumanMessage
-from langchain.chains import LLMChain
+# from langchain_community.llms import HuggingFaceEndpoint
+# from langchain.prompts import PromptTemplate
+# from langchain.schema import AIMessage, HumanMessage
+# from langchain.chains import LLMChain
 import gradio as gr
 import os
 
+from crew import CryptoCrew
+
 from dotenv import load_dotenv
+
 load_dotenv()
 
-repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
-llm = HuggingFaceEndpoint(
-    repo_id = repo_id, 
-)
-
-template = """You're a good chatbot. Answer this request: {question}
-Answer: Let's think step by step."""
-prompt = PromptTemplate.from_template(template=template)
-llm_chain = LLMChain(llm=llm, prompt=prompt)
-
-
-def predict(message, history):
-    history_langchain_format = []
-    # for human, ai in history:
-    #     history_langchain_format.append(HumanMessage(content=human))
-    #     history_langchain_format.append(AIMessage(content=ai))
-    # history_langchain_format.append(HumanMessage(content=message))
-    # gpt_response = llm(history_langchain_format)
-    response = llm_chain.invoke(message)['text']
+def predict(message):
+    # company = input(
+    #     dedent("""
+    #         Which cryptocurrency are you looking to delve into?
+    # """))
+
+    crypto_crew = CryptoCrew(company)
+    response = "## Here is the Report\n\n" + crypto_crew.run()
+    
     return response
 
 gr.ChatInterface(predict).launch()
@@ -42,3 +35,4 @@ gr.ChatInterface(predict).launch()
 
 
 
+

.ipynb_checkpoints/run_local-checkpoint.py

@@ -2,8 +2,6 @@
 # from langchain.prompts import PromptTemplate
 # from langchain.schema import AIMessage, HumanMessage
 # from langchain.chains import LLMChain
-import gradio as gr
-import os
 
 from crew import CryptoCrew
 
@@ -11,41 +9,18 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-def predict(message):
-    # company = input(
-    #     dedent("""
-    #         Which cryptocurrency are you looking to delve into?
-    # """))
-    # history_langchain_format = []
-    # for human, ai in history:
-    #     history_langchain_format.append(HumanMessage(content=human))
-    #     history_langchain_format.append(AIMessage(content=ai))
-    # history_langchain_format.append(HumanMessage(content=message))
-    # gpt_response = llm(history_langchain_format)
-    # response = llm_chain.invoke(message)['text']
+if __name__ == "__main__":
+    print("## Welcome to Crypto Analysis Crew")
+    print('-------------------------------')
+    company = input("""Which cryptocurrency are you looking to delve into?
+    """)
 
     crypto_crew = CryptoCrew(company)
-    response = "## Here is the Report\n\n" + crypto_crew.run()
-    
-    return response
-
-gr.ChatInterface(predict).launch()
-
-# if __name__ == "__main__":
-#     print("## Welcome to Crypto Analysis Crew")
-#     print('-------------------------------')
-#     company = input(
-#         dedent("""
-#             Which cryptocurrency are you looking to delve into?
-#     """))
-
-#     crypto_crew = CryptoCrew(company)
-#     result = crypto_crew.run()
-#     print("\n\n########################")
-#     print("## Here is the Report")
-#     print("########################\n")
-    
-# print(result)
+    result = crypto_crew.run()
+    print("\n\n########################")
+    print("## Here is the Report")
+    print("########################\n")
+    print(result)
 
 
 

agents.py

@@ -45,7 +45,8 @@ class CryptoAnalysisAgents():
                 'Armed with a flair for storytelling, the Content Writer transforms intricate crypto data into captivating narratives,'
                 'illuminating the latest trends for a wide audience.'
             ),
-            tools=[search_tool, 
+            tools=[
+                # search_tool, 
                    # AskWolframAlpha(),
                   ],
             allow_delegation=False,

chat_gradio.py

@@ -1,32 +1,25 @@
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain.prompts import PromptTemplate
-from langchain.schema import AIMessage, HumanMessage
-from langchain.chains import LLMChain
+# from langchain_community.llms import HuggingFaceEndpoint
+# from langchain.prompts import PromptTemplate
+# from langchain.schema import AIMessage, HumanMessage
+# from langchain.chains import LLMChain
 import gradio as gr
 import os
 
+from crew import CryptoCrew
+
 from dotenv import load_dotenv
+
 load_dotenv()
 
-repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
-llm = HuggingFaceEndpoint(
-    repo_id = repo_id, 
-)
-
-template = """You're a good chatbot. Answer this request: {question}
-Answer: Let's think step by step."""
-prompt = PromptTemplate.from_template(template=template)
-llm_chain = LLMChain(llm=llm, prompt=prompt)
-
-
-def predict(message, history):
-    history_langchain_format = []
-    # for human, ai in history:
-    #     history_langchain_format.append(HumanMessage(content=human))
-    #     history_langchain_format.append(AIMessage(content=ai))
-    # history_langchain_format.append(HumanMessage(content=message))
-    # gpt_response = llm(history_langchain_format)
-    response = llm_chain.invoke(message)['text']
+def predict(message):
+    # company = input(
+    #     dedent("""
+    #         Which cryptocurrency are you looking to delve into?
+    # """))
+
+    crypto_crew = CryptoCrew(company)
+    response = "## Here is the Report\n\n" + crypto_crew.run()
+    
     return response
 
 gr.ChatInterface(predict).launch()
@@ -42,3 +35,4 @@ gr.ChatInterface(predict).launch()
 
 
 
+

run_local.py

@@ -2,8 +2,6 @@
 # from langchain.prompts import PromptTemplate
 # from langchain.schema import AIMessage, HumanMessage
 # from langchain.chains import LLMChain
-import gradio as gr
-import os
 
 from crew import CryptoCrew
 
@@ -11,41 +9,18 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-def predict(message):
-    # company = input(
-    #     dedent("""
-    #         Which cryptocurrency are you looking to delve into?
-    # """))
-    # history_langchain_format = []
-    # for human, ai in history:
-    #     history_langchain_format.append(HumanMessage(content=human))
-    #     history_langchain_format.append(AIMessage(content=ai))
-    # history_langchain_format.append(HumanMessage(content=message))
-    # gpt_response = llm(history_langchain_format)
-    # response = llm_chain.invoke(message)['text']
+if __name__ == "__main__":
+    print("## Welcome to Crypto Analysis Crew")
+    print('-------------------------------')
+    company = input("""Which cryptocurrency are you looking to delve into?
+    """)
 
     crypto_crew = CryptoCrew(company)
-    response = "## Here is the Report\n\n" + crypto_crew.run()
-    
-    return response
-
-gr.ChatInterface(predict).launch()
-
-# if __name__ == "__main__":
-#     print("## Welcome to Crypto Analysis Crew")
-#     print('-------------------------------')
-#     company = input(
-#         dedent("""
-#             Which cryptocurrency are you looking to delve into?
-#     """))
-
-#     crypto_crew = CryptoCrew(company)
-#     result = crypto_crew.run()
-#     print("\n\n########################")
-#     print("## Here is the Report")
-#     print("########################\n")
-    
-# print(result)
+    result = crypto_crew.run()
+    print("\n\n########################")
+    print("## Here is the Report")
+    print("########################\n")
+    print(result)