Update app.py

app.py

@@ -4,4 +4,131 @@ from transformers import (
     pipeline,
     BlenderbotTokenizer,
     BlenderbotForConditionalGeneration,
-)
+)
+
+image_path = ""
+current_image_path = ""
+question = ""
+first_turn = True
+
+try:
+    while image_path != str(1) and question != str(1):
+        ###############################################################################
+
+        if first_turn:
+            image_path = input("Please enter the image path (type 1 to EXIT)\n")
+        else:
+            image_path = input(
+                "Please enter the image path (type 1 to EXIT) (type 2 to reuse image)\n"
+            )
+
+        ###############################################################################
+
+        if image_path == str(1):
+            break
+        elif image_path == str(2) and first_turn == False:
+            pass
+        else:
+            image = Image.open(str(image_path))
+
+        question = input("Please enter your question (type 1 to EXIT)\n")
+
+        ###############################################################################
+
+        # 5 MODEL INFERENCES.
+        # User Input = Image + Question About The Image.
+        # User -> Model 1 -> Model 2 -> Model 3 -> Model 4 -> Model 5
+
+        # Model 1.
+
+        vqa_pipeline_output = vqa_pipeline(image, question, top_k=5)[0]
+
+        # Model 2.
+
+        text = (
+            "I love "
+            + str(vqa_pipeline_output["answer"])
+            + " and I would like to know how to [MASK]."
+        )
+        bbu_pipeline_output = bbu_pipeline(text)
+
+        # Model 3.
+
+        utterance = bbu_pipeline_output[0]["sequence"]
+        inputs = tokenizer(utterance, return_tensors="pt")
+        result = facebook_model.generate(**inputs)
+        facebook_model_output = tokenizer.decode(result[0])
+
+        # Model 4.
+
+        facebook_model_output = facebook_model_output.replace("<s> ", "")
+        facebook_model_output = facebook_model_output.replace("<s>", "")
+        facebook_model_output = facebook_model_output.replace("</s>", "")
+        gpt2_pipeline_output = gpt2_pipeline(facebook_model_output)[0]["generated_text"]
+
+        # Model 5.
+
+        topic, prob = topic_model_1.transform(gpt2_pipeline_output)
+        topic_model_1_output = topic_model_1.get_topic_info(topic[0])["Representation"][
+            0
+        ]
+
+        topic, prob = topic_model_2.transform(gpt2_pipeline_output)
+        topic_model_2_output = topic_model_2.get_topic_info(topic[0])["Representation"][
+            0
+        ]
+        ###############################################################################
+
+        print()
+
+        print("-" * 150)
+        print("vqa_pipeline_output = ", vqa_pipeline_output)
+        print("bbu_pipeline_output =", bbu_pipeline_output)
+        print("facebook_model_output =", facebook_model_output)
+        print("gpt2_pipeline_output =", gpt2_pipeline_output)
+        print("topic_model_1_output =", topic_model_1_output)
+        print("topic_model_2_output =", topic_model_2_output)
+
+        print()
+
+        print("-" * 150)
+        print("SUMMARY")
+        print("-" * 7)
+        print("Your Image:", image)
+        print("Your Question:", question)
+        print("-" * 100)
+        print(
+            "1. Highest Predicted Answer For Your Question:",
+            vqa_pipeline_output["answer"],
+            "\n",
+        )
+        print(text)
+        print(
+            "2. Highest Predicted Sequence On [MASK] Based on 1.:",
+            bbu_pipeline_output[0]["sequence"],
+            "\n",
+        )
+        print(
+            "3. Conversation Based On Previous Answer Based on 2.:",
+            facebook_model_output,
+            "\n",
+        )
+        print(
+            "4. Text Generated Based On Previous Answer Based on 3.:",
+            gpt2_pipeline_output,
+            "\n",
+        )
+        print(
+            "5. Highest Predicted Topic Model_1 For Previous The Answer Based on 4.:",
+            topic_model_1_output,
+            "\n",
+        )
+        print(
+            "6. Highest Predicted Topic Model_2 For Previous The Answer Based on 4.:",
+            topic_model_2_output,
+        )
+        print("-" * 150)
+
+        first_turn = False
+except Exception as e:
+    print("Error:", e)