Delete src

src/customgraph.py

@@ -1,74 +0,0 @@
-import nest_asyncio
-nest_asyncio.apply()
-
-from scrapegraphai.graphs import SmartScraperMultiGraph
-from scrapegraphai.nodes import FetchNode, ParseNode
-from langchain.schema import Document
-
- # Create a custom graph class
-class CustomSmartScraperMultiGraph(SmartScraperMultiGraph):
-    def run(self):
-        # Fetch data from the URL
-        url_data = ""
-        for source in self.source:
-            if isinstance(source, str) and source.startswith("http"):
-                fetch_node = FetchNode( input="url | local_dir",
-                                        output=["doc", "link_urls", "img_urls"],
-                                        node_config={ 
-                                            "verbose": True,
-                                            "headless": True,})
-
-                url_data = fetch_node.execute({"url": source})
-                
-                parse_node = ParseNode(
-                                      input="doc",
-                                      output=["parsed_doc"],
-                                      node_config={
-                                          "chunk_size": 4096,
-                                          "verbose": True,
-                                      }
-                              )
-
-                parsed_doc = parse_node.execute({"doc": url_data["doc"]})
-
-                break  # Assuming only one URL needs to be fetched
-
-        # Combine URL data with Document data
-        combined_data = ""
-        for source in self.source:
-            if isinstance(source, Document):
-                combined_data += source.page_content
-        combined_data += parsed_doc['parsed_doc'][0]
-
-
-        return combined_data
-
-
-def get_data(pdf_doc, web_url,openai_key):
-
-    graph_config = {
-            "llm": {
-                "api_key": openai_key,
-                "model": "gpt-4o",
-            },
-            "verbose": True
-
-        }
-
-    sources = [
-            web_url,
-            Document(page_content=pdf_doc, metadata={"source": "local_content"})
-        ]
-
-    prompt = "give an indepth analysis"
-
-    # Instantiate the custom graph
-    multiple_search_graph = CustomSmartScraperMultiGraph(
-        prompt=prompt,
-        source=sources,
-        config=graph_config
-    )
-
-    # Run the graph and print the result
-    result = multiple_search_graph.run()
-    return result

src/main.py

@@ -1,104 +0,0 @@
-import os
-from dotenv import load_dotenv
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi import File, UploadFile
-from kor.extraction import create_extraction_chain
-from langchain_openai import ChatOpenAI
-from langchain_community.document_loaders import PyPDFLoader
-import json
-from scrape_parse_combine import scrape_parse_combine
-import schema_quick_analysis
-import schema_indepth_analysis
-
-
-def configure(): 
-    load_dotenv()
-
-configure()
-app = FastAPI()
-openai_key = os.getenv("openai_key")
-
-llm = ChatOpenAI(
-    model_name="gpt-4o",
-    temperature=0,
-    max_tokens=2000,
-    openai_api_key=openai_key
-)
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.get("/ping")
-async def ping():
-    return "Hello, I am alive"
-
-# Helper function. Upload a pdf_file and save it
-def upload(file):
-    file_name = ""
-    try:
-        contents = file.file.read()
-        with open(file.filename, 'wb') as f:
-            f.write(contents)
-    except Exception:
-        return {"message": "There was an error uploading the file"}
-    finally:
-        file.file.close()
-
-    file_name += file.filename
-    pdf_path = f"./{file_name}"
-    return pdf_path
-
-
-@app.post("/quick_analysis")
-async def quick_analysis(file: UploadFile = File(...)):
-    state_dict = {}
-    pdf_path = upload(file)
-
-    loader = PyPDFLoader(pdf_path)
-    pages = loader.load_and_split()
-
-    doc_info = ""
-    for page in range(len(pages)):
-        doc_info += pages[page].page_content
-
-    state_dict["pdf_doc"] = doc_info
-
-    chain = create_extraction_chain(
-        llm, schema_quick_analysis.schema, encoder_or_encoder_class="json")
-    doc_output = chain.invoke(doc_info)["data"]
-
-    state_dict["website_url"] = doc_output["startup_info"]["website_url"]
-
-    # Write JSON string to a file
-    with open('pdf_data.json', 'w') as json_file:
-        json.dump(state_dict, json_file)
-
-    return {"quick_analysis": doc_output}
-
-
-
-@app.post("/indepth_analysis")
-async def indepth_analysis():
-
-    scrape_parse_combine(openai_key)
-
-    # Load JSON data from a file
-    with open('pdf_data.json', 'r') as json_file:
-        data = json.load(json_file)
-
-    result = data["startup_info"]
-
-    chain = create_extraction_chain(
-        llm, schema_indepth_analysis.schema, encoder_or_encoder_class="json")
-
-    doc_output = chain.invoke(result)["data"]
-
-    return {"indepth_analysis": doc_output["startup_info"]}
-

src/schema_indepth_analysis.py

@@ -1,154 +0,0 @@
-from kor.nodes import Object, Text
-
-team = Object(
-    id="team",
-    description="Information about the team",
-    attributes=[
-        Text(id="name", description="The name of the team member"),
-        Text(id="position", description="The position of the team member in the company")
-    ],
-
-    examples=[
-        (
-            "Diego Hoter Co-founder & CEO",  # Text input
-            {
-                "name": "Diego Hoter", 
-                "position": "Co-founder & CEO"
-            }  # Dictionary with extracted attributes
-        )
-        # Add more examples in the same format if needed
-    ],
-    many=True
-)
-
-region = Object(
-    id="region",
-    description="Information about the regions where the startup are located",
-    attributes=[
-        Text(id="country", description="The country the startup is located"),
-        Text(id="city", description="The city the startup is located")
-    ],
-
-    examples=[
-        (
-            "We have a center in Sao Pablo, Brasil ",  # Text input
-            {
-                "country": "Brasil",
-                "city": "Sao Pablo"
-            }  # Dictionary with extracted attributes
-        )
-        # Add more examples in the same format if needed
-    ],
-    many=True
-)
-
-schema = Object(
-    id="startup_info",
-    description="Pitchdeck Information about a given startup.",
-    attributes=[
-        Text(
-            id="startup_overview",
-            description="A brief overview of the startup.",
-            examples=[("""
-                            We verify sustainable agriculture at global scale! simple, scalable and auditable from farm to marketWork with ucrop.it to develop a program to incentivize farmers to adopt desired agricultural changes
-                            Enabling farmers and AgFood companies to agree, trace, achieve & verify sustainability goals from Farm to market
-                            The solution is to MRV* the use of land and the crops management for a net nature-positive impact
-                """,
-
-                       "ucrop.it enables farmers and AgFood companies to trace, achieve, and verify sustainable agriculture practices from farm to market, ensuring nature-positive impacts and profitability through verified Crop Stories")],
-        ),
-        Text(
-            id="industry",
-            description="The industry or sector in which the startup operates.",
-            examples=[("We verify sustainable agriculture at global scale! simple, scalable and auditable from farm to market",
-                       "Agriculture, AI, Data Analytics, Blockchain")],
-        ),
-        Text(
-            id="startup_name",
-            description="The Name of a startup",
-            examples=[
-                ("Work with ucrop.it to develop a program to incentivize farmers to adopt desired agricultural changes", "ucrop.it")],
-        ),
-        Text(
-            id="product_or_service",
-            description="The product or service the startup sells",
-            examples=[
-                ("They offer a SaaS platform for farmers", "SaaS platform")],
-        ),
-
-        team,
-
-
-        Text(
-            id="business_model",
-            description="The business model of the startup",
-            examples=[("""
-                            2024 (F) 2022 2023 Annual Recurring Revenues Gross Margin 9% 58% 74% 36% 50% 55% Churn rate <5% Average customer value$23K Revenues In Thousands B2B Customer contracts Our growth since 2020 has been exponential
-                        """,
-                       """
-                            ucrop.it operates a B2B subscription-based model, charging for verified sustainable crop stories and land use assessments, with an average customer value of $23K and a gross margin of 55%.
-                        """)],
-        ),
-
-        Text(
-            id="company_stage",
-            description="The current stage of the startup (e.g., early-stage, growth, established).",
-            examples=[
-                ("Business development in Australia targeting Asia Pacific Region preparing for Series B growth opportunity", "early-stage")],
-        ),
-
-        region,
-
-        Text(
-            id="traction ",
-            description="Key performance indicators or metrics that demonstrate the startup’s progress.",
-            examples=[("""
-                            2024 (F) 2022 2023 Annual Recurring Revenues Gross Margin 9% 58% 74% 36% 50% 55% Churn rate <5% Average customer value$23K Revenues In Thousands B2B Customer contracts Our growth since 2020 has been exponential
-                        """,
-                       """
-                            Exponential growth since 2020, with $1.4M in annual recurring revenues in 2023, 70+ corporate customers, and operations in 10 countries.
-                        """)],
-        ),
-
-        Text(
-            id="go_to_market_strategy",
-            description="The approach the startup uses to reach its target audience.",
-            examples=[("""
-                            Market penetration Consolidate N. America  operations. Co-Founder relocation (US) Business development leveraging commercial contracts in BrazilBusiness development in Australia targeting Asia Pacific Region  preparing
-                            for Series B growth opportunity Talent growth 15% ESOPS vesting equity rights for Validated talents Key talent acquisition Strategic Geographies Use of Funds Series A Ask""",
-                       "The go-to-market strategy involves leveraging commercial contracts, co-founder relocation to the US, and business development in key regions.")],
-        ),
-        Text(
-            id="market_opportunity",
-            description="TThe market gap or need that the startup aims to address.",
-            examples=[("""
-                            2050 $ 77 Bn*on a 7~10 Tr market About us Ag transition: The MRV market value for the sustainability will 4x by 2050 2030 $ 20 Bn * MRV price rate is 1% of the Tr market value 4 x Market penetration Consolidate N. America
-                            operations. Co-Founder relocation (US) Business development leveraging commercial contracts in BrazilBusiness development in Australia targeting Asia Pacific Region  preparing for Series B growth opportunity
-                            """,
-                       "The MRV market for sustainability is projected to grow 4x by 2050, reaching $77 billion from $20 billion in 2030, with a focus on North America, Brazil, and the Asia Pacific region.")],
-        ),
-        Text(
-            id="financial_projections",
-            description="Projected financial performance (revenue, expenses, profits) over a specific period.",
-            examples=[("DeepAgro aims to achieve $1 million in annual revenue within the next three years.",
-                       "$1 million, for the next 3 years")],
-        ),
-        Text(
-            id="Investment_Round",
-            description="The type and amount of funding the startup is seeking.",
-            examples=[("Use of Funds Series A AsK $6 M ",
-                       "Serie A funding with an ask 6M")],
-        ),
-        Text(
-            id="value_proposition",
-            description="The unique value the startup offers to its customers.",
-            examples=[("""
-                The startup addresses the following Problem: Farmers and AgFood companies need to monitor, report, and verify sustainable agricultural practices to ensure compliance and add value to the supply chain.
-                ucrop.it provides a platform for creating and sharing traceable and verifiable Sustainable Crop Stories™ of land use and crop management practices.
-
-                """,
-                       "The company's platform offers a unique value proposition by enabling farmers and AgFood companies to monitor, report, and verify sustainable agricultural practices. This could attract a large customer base.")],
-        ),
-
-    ],
-)

src/schema_quick_analysis.py

@@ -1,162 +0,0 @@
-from kor.nodes import Object, Text
-
-team = Object(
-    id="team",
-    description="Information about the team",
-    attributes=[
-        Text(id="name", description="The name of the team member"),
-        Text(id="position",
-             description="The position of the team member in the company")
-    ],
-
-    examples=[
-        (
-            "Diego Hoter Co-founder & CEO",  # Text input
-            {
-                "name": "Diego Hoter",
-                "position": "Co-founder & CEO"
-            }  # Dictionary with extracted attributes 
-        )
-        # Add more examples in the same format if needed
-    ],
-    many=True
-)
-
-region = Object(
-    id="region",
-    description="Information about the regions where the startup are located",
-    attributes=[
-        Text(id="country",
-             description="The country the startup is located"),
-        Text(id="city", description="The city the startup is located")
-    ],
-
-    examples=[
-        (
-            "We have a center in Sao Pablo, Brasil ",  # Text input
-            {
-                "country": "Brasil",
-                "city": "Sao Pablo"
-            }  # Dictionary with extracted attributes
-        )
-        # Add more examples in the same format if needed
-    ],
-    many=True
-)
-
-schema = Object(
-    id="startup_info",
-    description="Pitchdeck Information about a given startup.",
-    attributes=[
-        Text(
-            id="startup_overview",
-            description="A brief overview of the startup.",
-            examples=[("""
-                                        We verify sustainable agriculture at global scale! simple, scalable and auditable from farm to marketWork with ucrop.it to develop a program to incentivize farmers to adopt desired agricultural changes
-                                        Enabling farmers and AgFood companies to agree, trace, achieve & verify sustainability goals from Farm to market
-                                        The solution is to MRV* the use of land and the crops management for a net nature-positive impact
-                            """,
-
-                       "ucrop.it enables farmers and AgFood companies to trace, achieve, and verify sustainable agriculture practices from farm to market, ensuring nature-positive impacts and profitability through verified Crop Stories")],
-        ),
-        Text(
-            id="industry",
-            description="The industry or sector in which the startup operates.",
-            examples=[("We verify sustainable agriculture at global scale! simple, scalable and auditable from farm to market",
-                       "Agriculture, AI, Data Analytics, Blockchain")],
-        ),
-        Text(
-            id="startup_name",
-            description="The Name of a startup",
-            examples=[
-                ("Work with ucrop.it to develop a program to incentivize farmers to adopt desired agricultural changes", "ucrop.it")],
-        ),
-        Text(
-            id="product_or_service",
-            description="The product or service the startup sells",
-            examples=[
-                ("They offer a SaaS platform for farmers", "SaaS platform")],
-        ),
-
-        team,
-
-
-        Text(
-            id="business_model",
-            description="The business model of the startup",
-            examples=[("""
-                                        2024 (F) 2022 2023 Annual Recurring Revenues Gross Margin 9% 58% 74% 36% 50% 55% Churn rate <5% Average customer value$23K Revenues In Thousands B2B Customer contracts Our growth since 2020 has been exponential
-                                    """,
-                       """
-                                        ucrop.it operates a B2B subscription-based model, charging for verified sustainable crop stories and land use assessments, with an average customer value of $23K and a gross margin of 55%.
-                                    """)],
-        ),
-
-        Text(
-            id="company_stage",
-            description="The current stage of the startup (e.g., early-stage, growth, established).",
-            examples=[
-                ("Business development in Australia targeting Asia Pacific Region preparing for Series B growth opportunity", "early-stage")],
-        ),
-
-        Text(
-            id="website_url",
-            description="The website link of the startup.",
-            # examples=[
-            #     ("A Brighter Future for Agriculture\nwww.deepagro.com2ARTIFICIAL INTELLIGENCE ", "https://www.deepagro.com/")],
-        ),
-        region,
-
-        Text(
-            id="traction ",
-            description="Key performance indicators or metrics that demonstrate the startup’s progress.",
-            examples=[("""
-                                        2024 (F) 2022 2023 Annual Recurring Revenues Gross Margin 9% 58% 74% 36% 50% 55% Churn rate <5% Average customer value$23K Revenues In Thousands B2B Customer contracts Our growth since 2020 has been exponential
-                                    """,
-                       """
-                                        Exponential growth since 2020, with $1.4M in annual recurring revenues in 2023, 70+ corporate customers, and operations in 10 countries.
-                                    """)],
-        ),
-
-        Text(
-            id="go_to_market_strategy",
-            description="The approach the startup uses to reach its target audience.",
-            examples=[("""
-                                        Market penetration Consolidate N. America  operations. Co-Founder relocation (US) Business development leveraging commercial contracts in BrazilBusiness development in Australia targeting Asia Pacific Region  preparing
-                                        for Series B growth opportunity Talent growth 15% ESOPS vesting equity rights for Validated talents Key talent acquisition Strategic Geographies Use of Funds Series A Ask""",
-                       "The go-to-market strategy involves leveraging commercial contracts, co-founder relocation to the US, and business development in key regions.")],
-        ),
-        Text(
-            id="market_opportunity",
-            description="The market gap or need that the startup aims to address.",
-            examples=[("""
-                                        2050 $ 77 Bn*on a 7~10 Tr market About us Ag transition: The MRV market value for the sustainability will 4x by 2050 2030 $ 20 Bn * MRV price rate is 1% of the Tr market value 4 x Market penetration Consolidate N. America
-                                        operations. Co-Founder relocation (US) Business development leveraging commercial contracts in BrazilBusiness development in Australia targeting Asia Pacific Region  preparing for Series B growth opportunity
-                                        """,
-                       "The MRV market for sustainability is projected to grow 4x by 2050, reaching $77 billion from $20 billion in 2030, with a focus on North America, Brazil, and the Asia Pacific region.")],
-        ),
-        Text(
-            id="financial_projections",
-            description="Projected financial performance (revenue, expenses, profits) over a specific period.",
-            examples=[
-                ("DeepAgro aims to achieve $1 million in annual revenue within the next three years.", "$1 million, for the next 3 years")],
-        ),
-        Text(
-            id="Investment_Round",
-            description="The type and amount of funding the startup is seeking.",
-            examples=[("Use of Funds Series A AsK $6 M ",
-                       "Serie A funding with an ask 6M")],
-        ),
-        Text(
-            id="value_proposition",
-            description="The unique value the startup offers to its customers.",
-            examples=[("""
-                            The startup addresses the following Problem: Farmers and AgFood companies need to monitor, report, and verify sustainable agricultural practices to ensure compliance and add value to the supply chain.
-                            ucrop.it provides a platform for creating and sharing traceable and verifiable Sustainable Crop Stories™ of land use and crop management practices.
-
-                            """,
-                       "The company's platform offers a unique value proposition by enabling farmers and AgFood companies to monitor, report, and verify sustainable agricultural practices. This could attract a large customer base.")],
-        ),
-
-    ],
-)

src/scrape_parse_combine.py

@@ -1,17 +0,0 @@
-from customgraph import get_data
-import json
-
-
-def scrape_parse_combine(openai_key):
-    # Load JSON data from a file
-    with open('pdf_data.json', 'r') as json_file:
-        data = json.load(json_file)
-
-
-    if "pdf_doc" in data.keys():
-        data["startup_info"] = get_data(data["pdf_doc"].encode("utf8").decode("utf8"),
-                                        "http://" + data["website_url"].encode("utf8").decode("utf8"),openai_key)
-
-    with open('pdf_data.json', 'w') as json_file:
-            json.dump(data, json_file)
-