Update app.py
Browse files
app.py
CHANGED
@@ -1,13 +1,13 @@
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import gradio as gr
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from langchain_community.vectorstores import FAISS
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from langchain_community.embeddings import HuggingFaceEmbeddings
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import zipfile
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import os
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import openai
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import torch
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#
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# Percorsi ZIP per manuali e problemi
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@@ -43,17 +43,22 @@ def search_and_summarize(query):
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combined_text = f"Manual Results:\n{manual_output}\n\nProblems Results:\n{problems_output}"
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# Generazione del riassunto con OpenAI
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input_text = f"Riassumi le seguenti informazioni:\n{combined_text}\n\nRiassunto:"
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temperature=0.7
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)
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summary = response.choices[0].text.strip()
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return manual_output, problems_output, summary
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# Interfaccia Gradio
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@@ -63,17 +68,18 @@ iface = gr.Interface(
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outputs=[
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gr.Textbox(label="Manual Results"),
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gr.Textbox(label="Issues Results"),
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gr.Textbox(label="Summary by
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],
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examples=[
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["How to change the knife?"],
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["What are the safety precautions for using the machine?"],
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["How can I get help with the machine?"]
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],
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title="Manual Querying System with
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description="Enter a question to get information from the manual and the common issues, summarized by
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)
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# Avvia l'app Gradio
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iface.launch()
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import openai
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import gradio as gr
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from langchain_community.vectorstores import FAISS
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from langchain_community.embeddings import HuggingFaceEmbeddings
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import zipfile
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import os
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import torch
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# Imposta la tua chiave API di OpenAI come variabile d'ambiente
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# Percorsi ZIP per manuali e problemi
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combined_text = f"Manual Results:\n{manual_output}\n\nProblems Results:\n{problems_output}"
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# Generazione del riassunto con OpenAI GPT-3 (nuova API)
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input_text = f"Riassumi le seguenti informazioni:\n{combined_text}\n\nRiassunto:"
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# Chiamata all'API OpenAI per generare il riassunto usando la nuova interfaccia
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo", # Puoi scegliere anche "gpt-4" o altri modelli
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messages=[
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{"role": "system", "content": "Sei un assistente che aiuta con la sintesi di informazioni."},
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{"role": "user", "content": input_text}
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],
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max_tokens=150,
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temperature=0.7
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)
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summary = response['choices'][0]['message']['content'].strip()
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return manual_output, problems_output, summary
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# Interfaccia Gradio
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outputs=[
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gr.Textbox(label="Manual Results"),
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gr.Textbox(label="Issues Results"),
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gr.Textbox(label="Summary by GPT-3")
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],
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examples=[
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["How to change the knife?"],
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["What are the safety precautions for using the machine?"],
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["How can I get help with the machine?"]
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],
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title="Manual Querying System with GPT-3 Summarization",
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description="Enter a question to get information from the manual and the common issues, summarized by GPT-3."
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)
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# Avvia l'app Gradio
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iface.launch()
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