Create app.py

app.py

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+import gradio as gr
+from io import BytesIO
+import fitz
+
+import openai
+
+
+class TranslationAgent:
+    def __init__(self, openai_key):
+        self.memory = []
+        system_msg = "You are a translator from english to italian.\n" \
+                     " The only thing you do is to translate.\n" \
+                     " You don't write anything other then the translation of the text you get.\n" \
+                     " The user will only provide the text without asking anything, but what he wants is the translation.\n" \
+                     " Never return the translation of a previously translated part!\n " \
+                     "The text you will need to translate will often include none sense stuff because it is coming from a text extraction of a pdf file including images and table.\n" \
+                     " Do your best to translate also this messy parts."
+
+        self.memory.append({"role": "system", "content": system_msg})
+
+        openai.api_key = openai_key
+
+    def fade_memory(self):
+        if len(self.memory) >= 5:
+            del self.memory[1:3]
+
+    def translate_chunk(self, chunk):
+        self.memory.append({"role": "user", "content": chunk})
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=self.memory
+        )
+        reply = response["choices"][0]["message"]["content"]
+        self.memory.append({"role": "assistant", "content": reply})
+        self.fade_memory()
+        return reply
+
+
+def extract_text_from_pdf(pdf, start, stop):
+    text = ""
+    with fitz.open(stream=BytesIO(pdf), filetype='pdf') as doc:  # remove .read()
+        for i, page in enumerate(doc):
+            if start <= i:
+                if i <= stop:
+                    text += page.get_text()
+                else:
+                    break
+    return text
+
+
+def split_text(text, chunk_size=100):
+    words = text.split()
+    chunks = []
+    current_chunk_words = []
+
+    for word in words:
+        current_chunk_words.append(word)
+        if word.endswith('.') and len(current_chunk_words) >= chunk_size:
+            chunks.append(' '.join(current_chunk_words))
+            current_chunk_words = []
+
+    # add the last chunk if any words remain
+    if current_chunk_words:
+        chunks.append(' '.join(current_chunk_words))
+
+    return chunks
+
+
+def translate_pdf(openai_key, pdf, start, stop):
+    translator = TranslationAgent(openai_key)
+
+    # extract text
+    if pdf is not None:
+        text = extract_text_from_pdf(pdf, start=start, stop=stop)
+        chunks = split_text(text)
+
+        translated_chunks = []
+        for chunk in chunks:
+            translated_chunk = translator.translate_chunk(chunk)
+            translated_chunks.append(translated_chunk + " ")
+
+        translated_text = ' '.join(translated_chunks)
+        with open('translated.txt', 'w') as f:
+            f.write(translated_text)
+
+        return translated_text, "Translation Successful"
+
+
+
+iface = gr.Interface(title="Pdf Translator English -> Italian",
+                     fn=translate_pdf,
+                     inputs=[
+                         gr.inputs.Textbox(lines=1, label="OpenAI API key",
+                                           placeholder="Enter your OpenAI API key here"),
+                         gr.inputs.File(type="binary", label="PDF file", ),
+                         gr.inputs.Number(label="Starting Page", ),
+                         gr.inputs.Number(label="Final Page")
+                     ],
+                     outputs=["text", "text"]
+                     )
+
+iface.launch(share=True)