Upload app.py

app.py

@@ -0,0 +1,292 @@
+# from groq import Groq
+
+# client = Groq()
+# completion = client.chat.completions.create(
+#     model="llama3-70b-8192",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": "Write a fairy tale"
+#         }
+#     ],
+#     temperature=1,
+#     max_tokens=1024,
+#     top_p=1,
+#     stream=True,
+#     stop=None,
+# )
+
+# for chunk in completion:
+#     print(chunk.choices[0].delta.content or "", end="")
+
+
+import os
+import tkinter as tk
+from tkinter import filedialog
+from crewai import Agent, Task, Crew
+from langchain_openai import ChatOpenAI
+from langchain_community.llms import Ollama
+from langchain.agents.agent_types import AgentType
+from langchain_experimental.agents.agent_toolkits import create_csv_agent
+from langchain_openai import ChatOpenAI, OpenAI
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from crewai.process import Process
+import gradio as gr
+import numpy as np
+
+os.environ["OPENAI_API_KEY"] = "NA"
+os.environ["GOOGLE_API_KEY"] = "AIzaSyD1Gxk5OZMnqAlzWoYjAoDBeu3Z5l2TS7U"
+
+os.environ["GROQ_API_KEY"] = "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"
+
+from crewai_tools import PDFSearchTool
+from crewai_tools import FileReadTool
+from crewai_tools import DOCXSearchTool
+from crewai_tools import TXTSearchTool
+from crewai_tools import CSVSearchTool
+
+llm = ChatGroq(
+    model="llama3-70b-8192",
+    verbose=True,
+    temperature=0.8,
+    api_key=os.environ["GROQ_API_KEY"]
+)
+
+#--------------------------------------------Class for choosing agent---------------------------------------#
+class agentCollection:
+
+    def agentPDF(filepath):
+        agentpdf = Agent(
+            role="PDF Content Searcher and Writer",
+            goal="Retrieve and summarize relevant content from a PDF provided by the user",
+            backstory="You are an expert in navigating and extracting information from PDF documents. Your task is to find the most relevant and accurate content within the PDF and provide a detailed and concise summary that addresses the user's query.",
+            verbose=True,
+            tools=[toolsCollection.toolPDF(filepath)],
+            llm=llm,
+            max_iter=10
+        )
+        return agentpdf
+
+    def agentFile(filepath):
+        agentfile = Agent(
+            role="General File Content Searcher and Writer",
+            goal="Retrieve and summarize relevant content from various file formats provided by the user",
+            backstory="You have extensive experience in handling different types of files, including PDFs, DOCX, TXT, and CSV. Your role is to expertly extract and summarize the most pertinent information from any file format to meet the user's needs.",
+            verbose=True,
+            tools=[toolsCollection.toolFile(filepath)],
+            llm=llm,
+            max_iter=10
+        )
+        return agentfile
+
+    def agentTXT(filepath):
+        agenttxt = Agent(
+            role="Text File Content Searcher and Writer",
+            goal="Retrieve and summarize relevant content from text files provided by the user",
+            backstory="You specialize in working with plain text files. Your job is to sift through the text and identify the most relevant information, providing a clear and accurate summary that fulfills the user's query.",
+            verbose=True,
+            tools=[toolsCollection.toolTXT(filepath)],
+            llm=llm,
+            max_iter=10
+        )
+        return agenttxt
+
+    def agentDOCX(filepath):
+        agentdoc = Agent(
+            role="DOCX Content Searcher and Writer",
+            goal="Retrieve and summarize relevant content from DOCX files provided by the user",
+            backstory="You are proficient in reading and extracting information from DOCX documents. Your expertise allows you to locate and summarize the most relevant content within a DOCX file, ensuring the user's query is answered thoroughly and accurately.",
+            verbose=True,
+            tools=[toolsCollection.toolDOCX(filepath)],
+            llm=llm,
+            max_iter=10
+        )
+        return agentdoc
+    
+    def agentCSV(filepath):
+        agentcsv = create_csv_agent(
+            ChatGroq(temperature=0, model="llama3-70b-8192"),
+            filepath,
+            verbose=True,
+            agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
+        )
+        return agentcsv
+
+    def agentContentWriter():
+        agentwriter = Agent(
+            role="Content Writer",
+            goal="Write a comprehensive report or blog based on the data received from other agents",
+            backstory="""You are a skilled content writer with expertise in synthesizing information from various sources. Your task is to use the summaries and insights provided by other agents to create a well-structured and coherent report or blog that addresses the user's query in detail.""",
+            verbose=True,
+            llm=llm,
+            max_iter=7
+        )
+        return agentwriter
+
+#--------------------------------------------Class for choosing tool---------------------------------------#
+
+class toolsCollection:
+ 
+    def toolPDF(filepath):
+        if filepath == "":
+            print("FILE NOT FOUND")
+            return
+        pdftool = PDFSearchTool(
+            config=dict(
+                llm=dict(
+                    provider="ollama",
+                    config=dict(
+                        model="llama3-70b-8192",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                    ),
+                ),
+            ),
+            pdf=filepath
+        )
+        return pdftool
+
+    def toolFile(filepath):
+        filetool = FileReadTool(
+            config=dict(
+                llm=dict(
+                    provider="ollama",
+                    config=dict(
+                        model="llama3-70b-8192",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                    ),
+                ),
+            ),
+            file_path=filepath
+        )
+        return filetool
+
+    def toolTXT(filepath):
+        txttool = TXTSearchTool(
+            config=dict(
+                llm=dict(
+                    provider="ollama",
+                    config=dict(
+                        model="llama3-70b-8192",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                    ),
+                ),
+            ),
+            txt=filepath
+        )
+        return txttool
+
+    def toolDOCX(filepath):
+        if filepath == "":
+            print("FILE NOT FOUND")
+            return
+        docxtool = DOCXSearchTool(
+            config=dict(
+                llm=dict(
+                    provider="ollama",
+                    config=dict(
+                        model="llama3-70b-8192",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                    ),
+                ),
+            ),
+            docx=filepath
+        )
+        return docxtool
+
+    def toolCSV(filepath):
+        csvtool = CSVSearchTool(
+            config=dict(
+                llm=dict(
+                    provider="ollama",
+                    config=dict(
+                        model="llama3-70b-8192",
+                    ),
+                ),
+                embedder=dict(
+                    provider="huggingface",
+                    config=dict(
+                        model="sentence-transformers/msmarco-distilbert-base-v4"
+                    ),
+                ),
+            ),
+            csv=filepath
+        )
+        return csvtool
+
+def run_ai(file, query, required_ans_format):
+    filepath = file.name
+
+    if filepath.endswith(".pdf"):
+        myagent = agentCollection.agentPDF(filepath)
+    elif filepath.endswith(".json"):
+        myagent = agentCollection.agentFile(filepath)
+    elif filepath.endswith(".docx"):
+        myagent = agentCollection.agentDOCX(filepath)
+    elif filepath.endswith(".txt"):
+        myagent = agentCollection.agentTXT(filepath)
+    elif filepath.endswith(".csv"):
+        myagent = agentCollection.agentCSV(filepath)
+        return myagent.run(query)
+       
+
+    task = Task(
+        description=f"Summarize the detailed description of the {query}",
+        expected_output=f'{required_ans_format} : {query}',
+        agent=myagent,
+    )
+
+    content_writer_agent = agentCollection.agentContentWriter()
+    content_writer_task = Task(
+        description=f"Write a comprehensive report on the topic '{query}' using the summaries and insights from the other agents",
+        expected_output=f'{required_ans_format} : {query}',
+        agent=content_writer_agent,
+         
+    )
+
+    crew = Crew(
+        agents=[myagent, content_writer_agent],
+        tasks=[task, content_writer_task],
+        process=Process.sequential,
+        verbose=2
+    )
+
+    # crew = Crew(
+    #     agents=[myagent],
+    #     tasks=[task],
+    #     process=Process.sequential,
+    #     verbose=2
+    # )
+
+    result = crew.kickoff()
+    return result
+
+iface = gr.Interface(
+    fn=run_ai,
+    inputs=[gr.File(label="Upload File"), "text", "text"],
+    outputs="text",
+    title="Document and Data Analyzer",
+    description="Upload a file, enter your query, and specify the format of the expected answer"
+)
+
+iface.launch()