Update app.py

app.py

@@ -1,215 +1,216 @@
-import os
-import pandas as pd
-import gradio as gr
-from crewai import Agent, Task, Crew
-from langchain_openai import ChatOpenAI
-from crewai_tools import PDFSearchTool, FileReadTool, DOCXSearchTool, CSVSearchTool
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.agents.agent_types import AgentType
-from langchain_experimental.agents.agent_toolkits import create_csv_agent
-from langchain_groq import ChatGroq
-
-# API keys-----------------move them to ENV 
-os.environ["OPENAI_API_KEY"] = "NA"
-os.environ["GOOGLE_API_KEY"] = "AIzaSyD7jKc5MdkRLakxcyhvrpie8XgbwY98NMo"
-
-# Load The Groq model for LLM
-llm = ChatGroq(
-    api_key="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",
-    model="llama3-70b-8192"
-)
-
-#<-----------------------------Tools----------------------------------->
-class tools:
-    def pdfRead(path):
-        PDFtool = PDFSearchTool(
-            config=dict(
-                llm=dict(
-                    provider="google",
-                    config=dict(
-                        model="gemini-1.5-flash-latest",
-                    ),
-                ),
-                embedder=dict(
-                    provider="huggingface",
-                    config=dict(
-                        model="sentence-transformers/msmarco-distilbert-base-v4"
-                        
-                    ),
-                ),
-            ),
-            pdf=path
-        )
-        return PDFtool
-    
-    def fileRead(path):
-        Filetool = FileReadTool(
-            config=dict(
-                llm=dict(
-                    provider="google",
-                    config=dict(
-                        model="gemini-1.5-flash-latest",
-                    ),
-                ),
-                embedder=dict(
-                    provider="huggingface",
-                    config=dict(
-                        model="sentence-transformers/msmarco-distilbert-base-v4"
-                        
-                    ),
-                ),
-            ),
-            file_path=path
-        )
-        return Filetool
-    
-    def docsRead(path):
-        Docstool = DOCXSearchTool(
-            config=dict(
-                llm=dict(
-                    provider="google",
-                    config=dict(
-                        model="gemini-1.5-flash-latest",
-                    ),
-                ),
-                embedder=dict(
-                    provider="huggingface",
-                    config=dict(
-                        model="sentence-transformers/msmarco-distilbert-base-v4"
-                        
-                    ),
-                ),
-            ),
-            docx=path
-        )
-        return Docstool
-#<-----------------------------Tools----------------------------------->
-
-#<------------------------------Agents START------------------------->
-
-class AgentLoader:
-
-    def csvReaderAgent(path):
-        agent = create_csv_agent(
-            ChatGoogleGenerativeAI(temperature=0.6, model="gemini-1.5-flash-latest"),
-            path,
-            verbose=True,
-            agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
-        )
-        return agent
-
-    def fileReaderAgent(path):
-        FileReader = Agent(
-            role='File searcher',
-            goal='To analyse and generate optimal and reliable results',
-            backstory="""You are a File specialist and can handle multiple file formats like .txt, .csv, .json etc.
-            You are responsible to analyse the file to find the relevant content that solves the problem of the user and generate high quality and reliable results.
-            You should also provide the results of your analysis and searching.""",
-            llm=llm,
-            verbose=True,
-            tools=[tools.fileRead(path)],
-            allow_delegation=False
-        )
-        return FileReader
-    
-    def PdfReaderAgent(path):
-        PdfReader = Agent(
-            role='PDF searcher',
-            goal='To analyse and generate optimal and reliable results',
-            backstory="""You are a PDF specialist and content writer.
-            You are responsible to analyse the pdf to find the relevant content that solves the problem of the user and generate high quality and reliable results.
-            You should also provide the results of your analysis and searching.""",
-            llm=llm,
-            verbose=True,
-            tools=[tools.pdfRead(path)],
-            allow_delegation=False
-        )
-        return PdfReader
-    
-    def DocsReaderAgent(path):
-        DocsReader = Agent(
-            role='Docs searcher',
-            goal='To analyse and generate optimal and reliable results',
-            backstory="""You are a Docs specialist and content writer.
-            You are responsible to analyse the pdf to find the relevant content that solves the problem of the user and generate high quality and reliable results.
-            You should also provide the results of your analysis and searching.""",
-            llm=llm,
-            verbose=True,
-            tools=[tools.docsRead(path)],
-            allow_delegation=False
-        )
-        return DocsReader
-    
-    def writerAgent():
-        writer=Agent(
-            role='Content Writer',
-            goal='To provide QUICK and reliable output',
-            backstory="""You are content specialist.
-            You are responsible to generate high quality results in the required format very quickly as soon as data is available.
-            You are very accurate and fast at the same time.""",
-            verbose=True,
-            llm=llm,
-            max_iter=5
-        )
-        return writer
-
-#<------------------------------Agents END------------------------->
-
-#<-------------------------------Tasks---------------------------->
-def getTasks(query, agent, exp):
-    task_read=Task(
-        description=f'{query}',
-        agent=agent,
-        expected_output=f'A detailed information on {query}'
-    )
-
-    task_write=Task(
-        description=f'{query}',
-        agent=AgentLoader.writerAgent(),
-        expected_output=exp
-    )
-
-    return [task_read, task_write]
-
-# Gradio interface function
-def process_file(file, query, expected_output):
-    path = file.name
-    
-    if path.endswith(".pdf"):
-        agent = AgentLoader.PdfReaderAgent(path)
-    elif path.endswith(".docx"):
-        agent = AgentLoader.DocsReaderAgent(path)
-    elif path.endswith(".json") or path.endswith(".txt"):
-        agent = AgentLoader.fileReaderAgent(path)
-    elif path.endswith(".csv"):
-        agent = AgentLoader.csvReaderAgent(path)
-        results = agent.run(query)
-    else:
-        return 'File NOT supported'
-    
-    if not path.endswith(".csv"):
-        task1 = getTasks(query, agent, expected_output)
-        mycrew = Crew(
-            agents=[agent, AgentLoader.writerAgent()],
-            tasks=task1,
-            verbose=True
-        )
-        results = mycrew.kickoff()
-    
-    return results
-
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=process_file,
-    inputs=[
-        gr.File(label="Upload File"),
-        gr.Textbox(label="Query"),
-        gr.Textbox(label="Expected Output")
-    ],
-    outputs="text",
-    title="File Analyzer",
-    description="Upload a file (CSV, PDF, DOCX, TXT, JSON) and enter your query to get detailed information."
-)
-
-# Launch the Gradio interface
-interface.launch()
+import os
+import pandas as pd
+import gradio as gr
+from crewai import Agent, Task, Crew
+from langchain_openai import ChatOpenAI
+from crewai_tools import PDFSearchTool, FileReadTool, DOCXSearchTool, CSVSearchTool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.agents.agent_types import AgentType
+from langchain_experimental.agents.agent_toolkits import create_csv_agent
+from langchain_groq import ChatGroq
+import asyncio
+
+# API keys-----------------move them to ENV 
+os.environ["OPENAI_API_KEY"] = "NA"
+os.environ["GOOGLE_API_KEY"] = "AIzaSyD7jKc5MdkRLakxcyhvrpie8XgbwY98NMo"
+
+# Load The Groq model for LLM
+llm = ChatGroq(
+    api_key="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",
+    model="llama3-70b-8192"
+)
+
+#<-----------------------------Tools----------------------------------->
+class tools:
+    def pdfRead(path):
+        PDFtool = PDFSearchTool(
+            config=dict(
+                llm=dict(
+                    provider="google",
+                    config=dict(
+                        model="gemini-1.5-flash-latest",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                        
+                    ),
+                ),
+            ),
+            pdf=path
+        )
+        return PDFtool
+    
+    def fileRead(path):
+        Filetool = FileReadTool(
+            config=dict(
+                llm=dict(
+                    provider="google",
+                    config=dict(
+                        model="gemini-1.5-flash-latest",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                        
+                    ),
+                ),
+            ),
+            file_path=path
+        )
+        return Filetool
+    
+    def docsRead(path):
+        Docstool = DOCXSearchTool(
+            config=dict(
+                llm=dict(
+                    provider="google",
+                    config=dict(
+                        model="gemini-1.5-flash-latest",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                        
+                    ),
+                ),
+            ),
+            docx=path
+        )
+        return Docstool
+#<-----------------------------Tools----------------------------------->
+
+#<------------------------------Agents START------------------------->
+
+class AgentLoader:
+
+    def csvReaderAgent(path):
+        agent = create_csv_agent(
+            ChatGoogleGenerativeAI(temperature=0.6, model="gemini-1.5-flash-latest"),
+            path,
+            verbose=True,
+            agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
+        )
+        return agent
+
+    def fileReaderAgent(path):
+        FileReader = Agent(
+            role='File searcher',
+            goal='To analyse and generate optimal and reliable results',
+            backstory="""You are a File specialist and can handle multiple file formats like .txt, .csv, .json etc.
+            You are responsible to analyse the file to find the relevant content that solves the problem of the user and generate high quality and reliable results.
+            You should also provide the results of your analysis and searching.""",
+            llm=llm,
+            verbose=True,
+            tools=[tools.fileRead(path)],
+            allow_delegation=False
+        )
+        return FileReader
+    
+    def PdfReaderAgent(path):
+        PdfReader = Agent(
+            role='PDF searcher',
+            goal='To analyse and generate optimal and reliable results',
+            backstory="""You are a PDF specialist and content writer.
+            You are responsible to analyse the pdf to find the relevant content that solves the problem of the user and generate high quality and reliable results.
+            You should also provide the results of your analysis and searching.""",
+            llm=llm,
+            verbose=True,
+            tools=[tools.pdfRead(path)],
+            allow_delegation=False
+        )
+        return PdfReader
+    
+    def DocsReaderAgent(path):
+        DocsReader = Agent(
+            role='Docs searcher',
+            goal='To analyse and generate optimal and reliable results',
+            backstory="""You are a Docs specialist and content writer.
+            You are responsible to analyse the pdf to find the relevant content that solves the problem of the user and generate high quality and reliable results.
+            You should also provide the results of your analysis and searching.""",
+            llm=llm,
+            verbose=True,
+            tools=[tools.docsRead(path)],
+            allow_delegation=False
+        )
+        return DocsReader
+    
+    def writerAgent():
+        writer=Agent(
+            role='Content Writer',
+            goal='To provide QUICK and reliable output',
+            backstory="""You are content specialist.
+            You are responsible to generate high quality results in the required format very quickly as soon as data is available.
+            You are very accurate and fast at the same time.""",
+            verbose=True,
+            llm=llm,
+            max_iter=5
+        )
+        return writer
+
+#<------------------------------Agents END------------------------->
+
+#<-------------------------------Tasks---------------------------->
+def getTasks(query, agent, exp):
+    task_read=Task(
+        description=f'{query}',
+        agent=agent,
+        expected_output=f'A detailed information on {query}'
+    )
+
+    task_write=Task(
+        description=f'{query}',
+        agent=AgentLoader.writerAgent(),
+        expected_output=exp
+    )
+
+    return [task_read, task_write]
+
+# Gradio interface function
+async def process_file(file, query, expected_output):
+    path = file.name
+    
+    if path.endswith(".pdf"):
+        agent = AgentLoader.PdfReaderAgent(path)
+    elif path.endswith(".docx"):
+        agent = AgentLoader.DocsReaderAgent(path)
+    elif path.endswith(".json") or path.endswith(".txt"):
+        agent = AgentLoader.fileReaderAgent(path)
+    elif path.endswith(".csv"):
+        agent = AgentLoader.csvReaderAgent(path)
+        results = await anyio.to_thread.run_sync(agent.run, query)
+    else:
+        return 'File NOT supported'
+    
+    if not path.endswith(".csv"):
+        task1 = getTasks(query, agent, expected_output)
+        mycrew = Crew(
+            agents=[agent, AgentLoader.writerAgent()],
+            tasks=task1,
+            verbose=True
+        )
+        results = await anyio.to_thread.run_sync(mycrew.kickoff)
+    
+    return results
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=process_file,
+    inputs=[
+        gr.File(label="Upload File"),
+        gr.Textbox(label="Query"),
+        gr.Textbox(label="Expected Output")
+    ],
+    outputs="text",
+    title="File Analyzer",
+    description="Upload a file (CSV, PDF, DOCX, TXT, JSON) and enter your query to get detailed information."
+)
+
+# Launch the Gradio interface
+interface.launch()