test: classify endpoint

main.py

@@ -11,10 +11,25 @@ pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
 
 @app.get("/infer_t5")
 def t5(input):
-    # output = pipe_flan(input)
-    # return {"output": output[0]["generated_text"]}
-    return classify_acct_dtype_str("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg")
-
+    output = pipe_flan(input)
+    return {"output": output[0]["generated_text"]}
+
+
+# @app.post("/classify/")
+#    async def classify_doc(file: UploadFile):
+#        return {"file_size": len(file)}
+
+@app.post("/classify/")
+    async def classify_doc(files: List[UploadFile] = File(...)):
+        for file in files:
+            try:
+                contents = file.file.read()
+                classify_res = classify_acct_dtype_str(contents.stream)
+            except Exception:
+                return {"message": "There was an error in uploading file(s)"}
+            finally:
+                file.file.close()
+        return {"message": f"Successfuly uploaded {[(file.filename+ " " + classify_res) for file in files]}"}
 
 app.mount("/", StaticFiles(directory="static", html=True), name="static")
 
@@ -25,8 +40,6 @@ def index() -> FileResponse:
 
 
 
-
-
 import re
 import torch
 from transformers import DonutProcessor, VisionEncoderDecoderModel
@@ -136,4 +149,4 @@ def classify_acct_dtype_str(input_path):
 
     return dtype_inf
 
-classify_acct_dtype_str("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg")
+# classify_acct_dtype_str("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg")

static/index.html

@@ -31,6 +31,10 @@
           <p class="text-gen-output"></p>
         </form>
       </section>
+      <section>
+        <button id="img-gen-submit">Submit</button>
+        <p class="img-gen-output"></p>
+      </section>
     </main>
   </body>
 </html>

static/script.js

@@ -18,4 +18,13 @@ textGenForm.addEventListener('submit', async (event) => {
   } catch (err) {
     console.error(err);
   }
+});
+
+const imgGenSubmitBtn = document.querySelector('.img-gen-submit');
+
+// const getDocClass = async (text)
+
+imgGenSubmitBtn.addEventListener('onclick', async(event) => {
+  const imgGenInput = document.getElementById('img-gen-input');
+  imgGenInput.textContent = "aaa";
 });